Indonesian Journal of Data and Science
type: channel
description: Indonesian Journal of Data and Science (IJODAS) is an electronic journal published by Yocto Brain. The journal publishes peer-reviewed original research articles in Data Science, Data Mining, Data Representation, and Applied Statistics. IJODAS is issued three times a year (March, July, and December). All manuscripts must be written in English and follow the journal’s guidelines and template requirements. IJODAS is indexed in SINTA 2 and reputable indexing databases. The journal is registered with the National Library of Indonesia and holds the ISSN 2715-9930.
publisher: Yocto Brain
language: en
publication name: Indonesian Journal of Data and Science
issn: 2715-9930
copyright: Authors retain copyright and full publishing rights to their articles. Upon acceptance, authors grant Indonesian Journal of Data and Science a non-exclusive license to publish the work and to identify itself as the original publisher. Self-archiving. Authors may deposit the submitted version, accepted manuscript, and version of record in institutional or subject repositories, with citation to the published article and a link to the version of record on the journal website. Commercial permissions. Uses intended for commercial advantage or monetary compensation are not permitted under CC BY-NC 4.0. For permissions, contact the editorial office at ijodas.journal@gmail.com. Legacy notice. Some earlier PDFs may display “Copyright © [Journal Name]” or only a CC BY-NC logo without the full license text. To ensure clarity, the authors maintain copyright, and all articles are distributed under CC BY-NC 4.0. Where any discrepancy exists, this policy and the article landing-page license statement prevail.
items: The Performance of the XGBOOST-LSTM and CNN-LSTM Algorithms in the Analysis of Stock Price Prediction Models for the Indonesian Banking Sector
items: An IoT-Based Fuzzy Decision Support System for Rhizobium Inoculation to Improve Soybean Productivity
items: Zero-Shot Detection of IndoT5-Synthesized Indonesian Scientific Abstracts Using mDeBERTa v3
items: An IoT-Based Precision Hydroponic Monitoring System and Long-Term Characterization of Low-Cost Temperature Sensor Drift
items: Comparative Evaluation of Machine Learning Models for Heavy Crude Oil Viscosity Prediction Using Repeated Nested Cross-Validation and Independent Holdout Testing
items: Sentiment Classification of TikTok Comments on The Free Nutritious Meal Program Using Multinomial Naïve Bayes
items: A Comparative Study of LSTM and CNN Models in SQL Injection Attack Detection
items: Weakly Supervised Sentiment Analysis of Gold Price Discussions Using Conventional Machine Learning and IndoBERT
items: Effect of Spatial, Intensity, and Hybrid Augmentation on Kidney CT Image Classification
items: Digital Burnout Risk Classification Based on Doomscrolling and Fear of Missing Out Using the Mamdani Fuzzy Method
items: The Performance of Support Vector Machine in Classifying Public Sentiment toward Student Suicide Cases
items: Interpreting Reactive Species Interdependencies in Plasma-Activated Saline: An Exploratory Multivariate Workflow
items: A Pilot Study on Machine Learning Models for Predicting Student Pass/Fail Outcomes Using LMS Activity Logs and Interactive Dashboard Analytics
items: An LLM-Based AI Task Agent for Academic Task Management with n8n and Telegram
items: Comparing ECLAT and Decision Tree for Drug Therapy Recommendation Rules on Multi Label Clinical Data
items: Handling Imbalanced Data in Concept Map Proposition Quality Classification: A Comparative Study of Resampling and Class-Weighted SVM
items: Resin Code Classification on Plastic Packaging Using Few-Shot Learning
items: Drug Recommendation Using Multilabel Classification with Decision Tree Based on Patient Complaints and Diagnoses
items: Hybrid Deep Learning Models For Gold Price Prediction: Enhancing Forecast In Volatile Financial Markets
items: The Effect of Clinical Rule-Based Domain Filtering on the Performance of FP-Growth-Based Drug Recommendation Systems
items: Applicant Data Segmentation and Pattern Analytics for University Admissions Strategy Using Hybrid SOM and K-Means
items: Implementation of an AI Agent Chatbot with a Dynamic Knowledge Base from Google Drive for Journal Information Service
items: Comparison of Naïve Bayes and SVM in Sentiment Analysis of ChatGPT for Learning on X and YouTube
items: Kodály Hand Sign Recognition from Hand Landmarks Using XGBoost
items: Comparing Sentiment Labeling with RoBERTa and IndoBERTweet on Public Opinion of Program Makan Bergizi Gratis
items: Deep Learning-Based Blood Cell Image Classification Using ResNet18 Architecture
items: Sentiment Analysis of Student Comments on Facilities and Infrastructure at Instiki Using Retrieval Augmented Generation
items: Sales Forecasting Analysis Using Fuzzy Time Series and Simple Linear Regression Methods at Toko Ari
items: Medium Range Meteorological Drought Prediction Based on SPEI-3 Using Ensemble Machine Learning and Deep Learning in North West Province, South Africa
items: Sentiment Analysis of BRImo Reviews on Google Play Store Using SVM and KNN

The Performance of the XGBOOST-LSTM and CNN-LSTM Algorithms in the Analysis of Stock Price Prediction Models for the Indonesian Banking Sector
type: item
description: Introduction: Accurate stock price forecasting is important for investors and financial institutions because stock movements reflect dynamic market conditions and influence investment decision-making. This study compares CNN-LSTM and XGBoost-LSTM models for predicting stock prices in the Indonesian banking sector. Method: Historical daily closing prices of PT Bank Central Asia Tbk (BBCA), PT Bank Rakyat Indonesia (Persero) Tbk (BBRI), and PT Bank Mandiri (Persero) Tbk (BMRI) were collected from Yahoo Finance, with 1,000 observations for each stock. Data were normalized using Min-Max scaling, transformed using a four-day sliding window to predict the following day, and chronologically divided into 80% training and 20% testing sets. Both models were evaluated using RMSE, MAE, R², and MAPE, with each experiment repeated ten times. Results and Discussion: CNN-LSTM consistently outperformed XGBoost-LSTM on all three test datasets. For BBCA, BBRI, and BMRI, CNN-LSTM achieved R² values of 0.8589, 0.7746, and 0.7409, respectively, compared with 0.8048, 0.7509, and 0.6260 for XGBoost-LSTM. CNN-LSTM also produced lower RMSE, MAE, and MAPE values across all test sets, indicating stronger and more stable generalization. Conclusion: CNN-LSTM provides more reliable predictive performance than XGBoost-LSTM for the evaluated Indonesian banking stocks, demonstrating the effectiveness of combining local feature extraction with temporal dependency learning for stock price forecasting
subject: Data Science
  topic: section
subject: Prediction
  topic: keywords
subject: XGBoost
  topic: keywords
subject: LSTM
  topic: keywords
subject: CNN
  topic: keywords
creator: M. Zainal Arifin
creator: Filbert Chaitra Bessel Kristianto
creator: Fadia Irsania Putri
creator: Agung Bella Putra Utama
rights: Copyright (c) 2026 M. Zainal Arifin, Filbert Chaitra Bessel Kristianto, Fadia Irsania Putri, Agung Bella Putra Utama https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 318
ending page: 332
doi: 10.56705/ijodas.v7i2.459

An IoT-Based Fuzzy Decision Support System for Rhizobium Inoculation to Improve Soybean Productivity
type: item
description: Introduction: Soybean productivity in Indonesia remains below national demand, while the effectiveness of Rhizobium inoculation depends strongly on dynamic soil conditions such as pH, moisture, temperature, and nitrogen availability. This study develops an Internet of Things (IoT)-based decision support system for adaptive Rhizobium inoculation in highland soybean cultivation. Method: The system integrates a Soil NPK RS485 Modbus sensor, ESP32-WROOM-32D microcontroller, MQTT communication, and the SMARTO web dashboard to monitor four environmental parameters in real time. A Mamdani Fuzzy Inference System was implemented using 17 membership functions and 25 expert-derived IF–THEN rules, with centroid defuzzification producing Rhizobium dose recommendations from 0 to 200 g/ha. Results and Discussion: Field readings of pH 7.5, soil moisture 55%, temperature 26°C, and nitrogen 155 mg/kg generated a recommendation of 16.56 g/ha, classified as Very Low, with a pump volume of 33 mL/ha. Expert validation across 30 simulated highland scenarios produced an overall agreement rate of 83.3%, demonstrating satisfactory consistency between system recommendations and agronomic judgment. Conclusion: The proposed IoT-Fuzzy DSS demonstrates the feasibility of location-specific, real-time, and interpretable Rhizobium inoculation support for highland soybean cultivation, providing a practical foundation for precision biological-input management.
subject: Applied Statistics
  topic: section
subject: Decision Support System
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: Fuzzy Logic
  topic: keywords
subject: Mamdani
  topic: keywords
subject: Rhizobium
  topic: keywords
subject: Soybean
  topic: keywords
subject: Highland Agroecosystem
  topic: keywords
creator: Andi Ulfah Tenripada
creator: Lukman Syafie
creator: Muh Mu'min
creator: Raqhib Ataillah
creator: Muh Nawir
rights: Copyright (c) 2026 Andi Ulfah Tenripada https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 395
ending page: 405
doi: 10.56705/ijodas.v7i2.458

Zero-Shot Detection of IndoT5-Synthesized Indonesian Scientific Abstracts Using mDeBERTa v3
type: item
description: Introduction: Distinguishing human-written scientific abstracts from AI-synthesized text remains challenging, particularly when machine-generated language appears fluent and formally structured. This study evaluates mDeBERTa v3 in a zero-shot Natural Language Inference (NLI) setting for detecting Indonesian scientific abstracts specifically synthesized using IndoT5-base-paraphrase. Method: A balanced dataset of 2,274 abstracts comprising 1,137 human-written abstracts from SINTA 3 journals and 1,137 IndoT5-synthesized counterparts was analyzed. Seven linguistic features were examined using the Mann–Whitney U test, followed by zero-shot mDeBERTa v3 classification using one-, three-, and five-aspect NLI instruction scenarios. A Random Forest classifier using the same linguistic features was included as a supervised baseline. Results and Discussion: All seven linguistic features differed significantly between classes (p < 0.001), with AI texts showing substantially higher sentence-length variation than human texts. The targeted one-aspect NLI scenario achieved the highest recall of 76.52% but only 53.52% accuracy because 790 human abstracts were misclassified as AI. Increasing instruction complexity further reduced recall. In contrast, Random Forest achieved 91.21% accuracy and an F1-score of 0.9130, confirming that the identified linguistic anomalies are strong learnable signals. Conclusion: Zero-shot mDeBERTa v3 can detect generator-specific structural artifacts but remains insufficiently precise for standalone academic-integrity screening and should be supplemented by supervised methods and human review.
subject: Data Science
  topic: section
subject: AI Text Detection;
  topic: keywords
subject: IndoT5
  topic: keywords
subject: mDeBERTa v3
  topic: keywords
subject: Scientific Abstract
  topic: keywords
subject: Zero-Shot Classification
  topic: keywords
creator: Aldo Syahputra
creator: Aris Wahyu Murdiyanto
creator: Ulfi Saidata Aesyi
rights: Copyright (c) 2026 Aldo, Aris Wahyu Murdiyanto, Ulfi Saidata Aesyi https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 333
ending page: 349
doi: 10.56705/ijodas.v7i2.457

An IoT-Based Precision Hydroponic Monitoring System and Long-Term Characterization of Low-Cost Temperature Sensor Drift
type: item
description: Introduction: Temperature monitoring is critical in hydroponic cultivation because it influences nutrient solubility, dissolved oxygen, and root uptake, yet low-cost digital sensors commonly used in Internet of Things (IoT) systems may experience accuracy degradation during long-term deployment. This study develops a low-cost IoT monitoring platform for nutrient film technique (NFT) hydroponics and characterizes temperature-sensor drift under continuous operating conditions. Method: The system employed two redundant temperature sensors in the nutrient channel and one ambient sensor, with measurements timestamped, filtered, stored locally at the edge, and visualized through a cloud dashboard. Temperature data were recorded hourly for 40 days, producing 960 observations per sensor. Drift was evaluated from the deviation between the primary and redundant channel sensors using mean absolute error, maximum absolute deviation, standard deviation, drift onset, and estimated drift rate. Results and Discussion: The primary sensor showed a mean absolute deviation of 0.82 °C over the full monitoring period and a maximum deviation of 1.47 °C. Sensor agreement remained close during the first 10 days but diverged after approximately day 12, with late-period MAE increasing to 1.18 °C and an estimated drift rate of about 0.04 °C/day. Conclusion: Long-term drift in low-cost temperature sensors can materially affect hydroponic monitoring accuracy, and redundant sensing provides a practical baseline for future adaptive edge-based calibration methods.
subject: Applied Statistics
  topic: section
subject: Data Quality
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: Precision Hydroponics
  topic: keywords
subject: Sensor Drift
  topic: keywords
subject: Temperature Sensing
  topic: keywords
creator: Dedy Atmajaya
creator: Abdullah Basalamah
creator: Nia Kurniati
creator: Muhammad Iqbal
creator: Thalita Sherly Putri Jasmin
rights: Copyright (c) 2026 Dedy Atmajaya, Abdullah Basalamah, Nia Kurniati, Muhammad Iqbal, Thalita Sherly Putri Jasmin https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 388
ending page: 394
doi: 10.56705/ijodas.v7i2.456

Comparative Evaluation of Machine Learning Models for Heavy Crude Oil Viscosity Prediction Using Repeated Nested Cross-Validation and Independent Holdout Testing
type: item
description: Introduction: Accurate prediction of heavy crude oil viscosity is important for reservoir engineering, production planning, and flow assurance because viscosity strongly affects fluid mobility and transport behavior. This study comparatively evaluates established machine learning models under a rigorous validation protocol rather than proposing a new predictive framework. Method: A published Middle Eastern heavy crude-oil dataset containing 196 development measurements and 47 independent holdout measurements was used. Linear Regression, Support Vector Regression, Random Forest, Gradient Boosting, and the Beggs–Robinson correlation were evaluated using repeated nested cross-validation with five outer folds repeated twice and five inner folds. Preprocessing and hyperparameter selection were embedded within the validation pipeline, while the untouched holdout set was used only for final evaluation. Results and Discussion: Gradient Boosting achieved the best internal performance with R² = 0.99313 and RMSE = 11.41 cP. On the independent holdout set, it achieved R² = 0.99308, RMSE = 8.43 cP, MAE = 6.64 cP, and MAPE = 0.78%, outperforming Random Forest and Support Vector Regression. Residual diagnostics showed no detectable heteroscedasticity, while permutation importance identified temperature and C7+ as the dominant predictors. Conclusion: Gradient Boosting provides highly accurate viscosity predictions within the sampled domain; however, the absence of row-level oil identifiers and external reservoir data limits conclusions regarding oil-disjoint and field-level generalization.
subject: Data Science
  topic: section
subject: Heavy Crude Oil Viscosity
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Gradient Boosting
  topic: keywords
subject: Repeated Nested Cross-Validation
  topic: keywords
subject: Independent Holdout Testing
  topic: keywords
subject: Comparative Evaluation
  topic: keywords
creator: Enggie Hendrawan Saputra
creator: Ilham Ari Elbaith Zaeni
creator: Didik Dwi Prasetya
creator: Azlan Mohd Zain
creator: Welly Antonius
creator: I Made Wirawan
rights: Copyright (c) 2026 Enggie Hendrawan Saputra, Ilham Ari Elbaith Zaeni, Didik Dwi Prasetya, Azlan Mohd Zain, Welly Antonius, I Made Wirawan https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 247
ending page: 160
doi: 10.56705/ijodas.v7i2.455

Sentiment Classification of TikTok Comments on The Free Nutritious Meal Program Using Multinomial Naïve Bayes
type: item
description: Introduction: TikTok has become an important platform for public discussion of government programs, including Indonesia’s Free Nutritious Meal (MBG) Program, while the large volume of comments makes manual sentiment analysis inefficient. This study evaluates Multinomial Naïve Bayes for classifying public sentiment toward the MBG Program using TikTok comments. Method: A total of 1,048 comments were collected from the official National Nutrition Agency TikTok account using Apify, with 1,028 comments remaining after preprocessing. Sentiment pseudo-labels were generated using IndoRoBERTa, producing 333 positive and 695 negative comments. Undersampling balanced the dataset to 666 comments, followed by an 80:20 stratified train–test split. TF-IDF feature extraction and Multinomial Naïve Bayes classification were integrated in a Scikit-learn Pipeline, while GridSearchCV with 5-fold cross-validation optimized model parameters. Results and Discussion: The optimized model achieved 80.60% accuracy, 81.04% precision, 80.60% recall, and an 80.53% F1-score on 134 test comments. Negative sentiment dominated the original post-preprocessing dataset at 67.61%. However, these results primarily indicate agreement with IndoRoBERTa-generated pseudo-labels rather than human-verified sentiment labels. Conclusion: The TF-IDF–Multinomial Naïve Bayes pipeline provides a useful baseline for MBG-related TikTok sentiment classification, but manual label validation and comparison with alternative classifiers are required to establish stronger reliability and generalizability.
subject: Data Science
  topic: section
subject: Sentiment Classification
  topic: keywords
subject: Multinomial Naive Bayes
  topic: keywords
subject: IndoBERT
  topic: keywords
subject: Free Nutritious Meal Program
  topic: keywords
subject: TikTok
  topic: keywords
subject: TF-IDF
  topic: keywords
creator: Salom Sefanya Onibala
creator: Rosmasari
creator: Kezia Arum Sary
rights: Copyright (c) 2026 Salom Sefanya Onibala, Rosmasari, Kezia Arum Sary https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 261
ending page: 273
doi: 10.56705/ijodas.v7i2.448

A Comparative Study of LSTM and CNN Models in SQL Injection Attack Detection
type: item
description: Introduction: SQL Injection (SQLi) remains a critical cybersecurity threat because it exploits vulnerabilities in user input validation and can compromise the confidentiality, integrity, and availability of information systems. This study compares Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures for detecting malicious SQL queries under identical experimental conditions. Method: A publicly available dataset containing 148,327 malicious and benign SQL query instances was preprocessed through missing-value removal, label encoding, tokenization, sequence transformation, padding, and embedding representation. LSTM and CNN models were evaluated using three train-test split scenarios of 70:30, 80:20, and 90:10. Performance was assessed using accuracy, precision, recall, F1-score, and confusion matrices, with the 80:20 split selected for detailed evaluation. Results and Discussion: LSTM consistently achieved higher accuracy across the evaluated splits, ranging from 97.84% to 98.00%. Under the 80:20 configuration, LSTM achieved 97.86% accuracy, 99.34% precision, 96.55% recall, and a 97.92% F1-score, compared with CNN at 97.00%, 97.68%, 96.56%, and 97.12%, respectively. LSTM also reduced false positives from 356 to 99, demonstrating better discrimination between legitimate and malicious queries. Conclusion: LSTM provides more reliable SQL Injection detection than CNN by better capturing sequential dependencies within SQL query structures, making it a promising approach for practical cybersecurity systems
subject: Data Science
  topic: section
subject: SQL Injection
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Long Short-Term Memory
  topic: keywords
subject: Convolutional Neural Network
  topic: keywords
subject: Embedding
  topic: keywords
creator: Abdul Rachman Manga'
creator: Wahyu Kadri Rahmat Suat Suat
creator: Huzain Azis
rights: Copyright (c) 2026 Abdul Rachman Manga', Wahyu Kadri Rahmat Suat Suat, Huzain Azis https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 350
ending page: 362
doi: 10.56705/ijodas.v7i2.460

Weakly Supervised Sentiment Analysis of Gold Price Discussions Using Conventional Machine Learning and IndoBERT
type: item
description: Introduction: Gold price movements attract substantial public and investor attention because gold serves as both a safe-haven asset and a hedging instrument. This study investigates Indonesian public sentiment toward gold price discussions on Platform X using a weakly supervised sentiment-analysis framework. Method: A total of 7,283 Indonesian-language tweets containing the keyword “harga emas” were collected during 2023–2025, with 4,429 tweets retained after preprocessing. Sentiment labels were generated using a domain-specific lexicon and validated through manual annotation. Naïve Bayes, K-Nearest Neighbor, Support Vector Machine, and IndoBERT were evaluated using the same train–test partition. Results and Discussion: Manual validation achieved a Cohen’s Kappa of 0.8718, indicating almost perfect inter-annotator agreement, while the lexicon-based labels achieved 70.62% accuracy against the manually annotated reference. IndoBERT achieved the highest performance on weakly supervised labels with 98.31% accuracy and a 98.16% macro F1-score, outperforming SVM, Naïve Bayes, and KNN. However, its accuracy decreased to 69.49% when evaluated against manually annotated data, demonstrating that downstream performance remains strongly influenced by weak-label quality. Conclusion: Weak supervision provides an efficient and scalable approach for large-scale Indonesian financial sentiment annotation, while contextual models such as IndoBERT offer superior classification performance; however, reliable manual validation remains essential to mitigate label noise and improve generalizability.
subject: Data Science
  topic: section
subject: Sentiment Analysis
  topic: keywords
subject: Gold Price
  topic: keywords
subject: Weak Supervision
  topic: keywords
subject: IndoBERT
  topic: keywords
subject: Platform X
  topic: keywords
creator: M Rizki Hardika
creator: Brina Miftahurrohmah
rights: Copyright (c) 2026 M Rizki Hardika, Brina Miftahurrohmah https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 274
ending page: 290
doi: 10.56705/ijodas.v7i2.445

Effect of Spatial, Intensity, and Hybrid Augmentation on Kidney CT Image Classification
type: item
description: Introduction: Kidney diseases remain a major global health challenge, and computed tomography (CT) provides detailed renal imaging that supports accurate diagnosis. Although data augmentation is commonly used to improve deep learning performance on limited medical datasets, the effects of different augmentation strategies on image characteristics and model learning behavior remain insufficiently understood. Method: This study evaluated spatial, intensity, and hybrid augmentation for kidney CT image classification using a custom CNN, MobileNetV2, and EfficientNet-B0. A public dataset containing 12,446 CT images across Normal, Cyst, Tumor, and Stone classes was partitioned using stratified sampling into training, validation, and test sets. Four scenarios—baseline, spatial, intensity, and hybrid augmentation—were evaluated across three independent random seeds using accuracy, precision, recall, F1-score, and AUC. Results and Discussion: The baseline achieved mean accuracies of 99.96%, 98.32%, and 94.06% for CNN, MobileNetV2, and EfficientNet-B0, respectively. Intensity augmentation slightly improved CNN accuracy to 99.99% and consistently produced smaller performance degradation and more stable convergence than spatial and hybrid augmentation. Spatial and hybrid transformations generally reduced classification performance, indicating that excessive geometric changes may disrupt diagnostically relevant anatomical features. Conclusion: Baseline training provided the best overall performance, while intensity augmentation was the most effective augmentation strategy, demonstrating that preserving anatomically meaningful image characteristics is more important than indiscriminately increasing data diversity.
subject: Data Science
  topic: section
subject: Kidney CT Images
  topic: keywords
subject: Data Augmentation
  topic: keywords
subject: Intensity Augmentation
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Medical Image Classification
  topic: keywords
subject: Learning Behavior Analysis
  topic: keywords
creator: Ardha Ardhana Putra Agustavada
creator: Aji Prasetya Wibawa
creator: Dafa Fadhilah Hilmi
creator: Abdullah Sholum
creator: Felix Andika Dwiyanto
rights: Copyright (c) 2026 Ardha Ardhana Putra Agustavada, Aji Prasetya Wibawa, Dafa Fadhilah Hilmi, Abdullah Sholum, Felix Andika Dwiyanto https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 300
ending page: 317
doi: 10.56705/ijodas.v7i2.443

Digital Burnout Risk Classification Based on Doomscrolling and Fear of Missing Out Using the Mamdani Fuzzy Method
type: item
description: Introduction: Intensive digital technology use among university students may contribute to Digital Burnout, particularly when accompanied by doomscrolling and Fear of Missing Out (FOMO). This study classifies Digital Burnout risk into low, moderate, and high categories using these two digital-behavior factors. Method: A Mamdani Fuzzy Inference System was implemented in MATLAB using Doomscrolling and FOMO as inputs and Digital Burnout as the output. Data were collected through validated self-report questionnaires from 414 students, resulting in 408 valid records. The system employed nine IF–THEN rules, trapezoidal and triangular membership functions within a normalized [0,100] domain, centroid defuzzification, and twelve parameter-adjustment iterations. Results and Discussion: The ground-truth distribution showed that 47.5% of students were categorized as having high Digital Burnout, 30.1% moderate, and 22.3% low. The optimal fuzzy configuration achieved 67.89% accuracy and a macro F1-score of 0.6725. The High category achieved very high precision of 0.9688 but moderate recall of 0.6392, indicating that some high-risk students remained undetected. Classification accuracy was higher among gamers (77.19%) than general respondents (61.18%). Conclusion: The Mamdani Fuzzy approach demonstrates the feasibility of classifying Digital Burnout risk from Doomscrolling and FOMO; however, its moderate accuracy and reliance on self-reported, non-clinical labels indicate that it should be considered an exploratory prototype requiring independent validation and expert-reviewed rules before practical deployment.
subject: Data Science
  topic: section
subject: Classification
  topic: keywords
subject: Digital Burnout
  topic: keywords
subject: Doomscrolling
  topic: keywords
subject: Fear of Missing Out
  topic: keywords
subject: Fuzzy Mamdani
  topic: keywords
creator: Matelda Yunanta Ambon
creator: Rosmasari
creator: Fahrul Agus
rights: Copyright (c) 2026 Matelda Yunanta Ambon, Rosmasari, Fahrul Agus https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 223
ending page: 237
doi: 10.56705/ijodas.v7i2.442

The Performance of Support Vector Machine in Classifying Public Sentiment toward Student Suicide Cases
type: item
description: Introduction: The rapid growth of social media has generated large volumes of user-generated content that can be analyzed to understand public responses to sensitive social issues. This study evaluates the performance of Support Vector Machine (SVM) in classifying public sentiment toward a widely discussed student suicide case based on YouTube comments. Method: A total of 5,000 comments were collected from a video on the Denny Sumargo YouTube channel using the YouTube Data API and categorized into positive and negative sentiments. Text preprocessing included cleaning, normalization, tokenization, stop-word removal, and stemming. Term Frequency-Inverse Document Frequency (TF-IDF) was used for feature extraction, while Synthetic Minority Over-sampling Technique (SMOTE) addressed class imbalance. The dataset was divided into 80% training and 20% testing data, and SVM was applied for binary sentiment classification. Results and Discussion: The SVM model achieved 99.96% training accuracy and 89.25% test accuracy, with precision, recall, and F1-score consistently around 89%. These results indicate that the TF-IDF, SMOTE, and SVM pipeline effectively classified Indonesian social media comments despite the linguistic complexity of discussions surrounding sensitive issues. Conclusion: SVM demonstrates effective and robust performance for classifying public sentiment in Indonesian YouTube comments and provides a useful approach for analyzing public responses to sensitive social phenomena.
subject: Data Science
  topic: section
subject: Sentiment Analysis
  topic: keywords
subject: Support Vector Machine
  topic: keywords
subject: YouTube Comments
  topic: keywords
subject: Text Mining
  topic: keywords
creator: I Gusi Gede Bagus Ngurah Sarjana
creator: Made Leo Radhitya
creator: Ni Wayan Suardiati Putri
rights: Copyright (c) 2026 I Gusi Gede Bagus Ngurah Sarjana, Made Leo Radhitya, Ni Wayan Suardiati Putri https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 202
ending page: 212
doi: 10.56705/ijodas.v7i2.431

Interpreting Reactive Species Interdependencies in Plasma-Activated Saline: An Exploratory Multivariate Workflow
type: item
description: Introduction: Interpreting Plasma-Activated Saline (PAS) is challenging because reactive oxygen, nitrogen, and chlorine species are strongly interdependent, making it difficult to isolate their individual contributions to physicochemical properties such as Oxidation-Reduction Potential (ORP). This study explores these relationships using a multivariate chemometric workflow. Method: A small experimental dataset comprising 10 PAS observations generated by a serial DBD-GAPJ reactor was analyzed using Exploratory Data Analysis and Multiple Linear Regression. ORP was modeled as a function of O3, H2O2, HClO, NO3−, and NO2−. Model interpretation was supported by Variance Inflation Factor analysis, standardized coefficients, Leave-One-Out Cross-Validation, and residual diagnostics. Results and Discussion: H2O2 showed the strongest bivariate correlation with ORP (r = 0.86). The regression model achieved R² = 0.84 and adjusted R² = 0.65, but LOOCV produced Q² = −0.33, indicating poor out-of-sample prediction. Strong multicollinearity among reactive species complicated coefficient interpretation, and none of the individual predictors reached conventional statistical significance. Standardized coefficients identified H2O2 as the strongest relative contributor, while O3 and HClO remained chemically plausible interdependent contributors. Conclusion: The proposed workflow is valuable for interpreting small, collinear chemical datasets, but the results should be regarded as hypothesis-generating rather than predictive, emphasizing the importance of standardization, cross-validation, and multicollinearity diagnostics
subject: Applied Statistics
  topic: section
subject: Plasma-Activated Saline
  topic: keywords
subject: Reactive Oxygen and Nitrogen Species
  topic: keywords
subject: Multiple Linear Regression
  topic: keywords
subject: Multicollinearity
  topic: keywords
subject: Oxidation-Reduction Potential
  topic: keywords
creator: Nilton Francelosi Azevedo Neto
creator: Orisson Ponce Gomes
creator: Lucas Pereira Piedade
creator: Felipe Souza Miranda
creator: Rodrigo Savio Pessoa
rights: Copyright (c) 2026 Nilton Francelosi Azevedo Neto, Orisson Ponce Gomes, Lucas Pereira Piedade, Felipe Souza Miranda, Rodrigo Savio Pessoa https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 376
ending page: 387
doi: 10.56705/ijodas.v7i2.417

A Pilot Study on Machine Learning Models for Predicting Student Pass/Fail Outcomes Using LMS Activity Logs and Interactive Dashboard Analytics
type: item
description: Introduction: Learning Management Systems (LMS) generate extensive behavioral data that can support early identification of students at risk of academic failure, yet such data are often underutilized for predictive and intervention purposes. Method: This exploratory pilot study developed a machine learning pipeline following the Knowledge Discovery in Databases (KDD) framework using LMS activity logs from 36 students enrolled in a Discrete Mathematics course. Student-level behavioral features were extracted and modeled using Logistic Regression, Random Forest, and XGBoost. Model performance was evaluated using repeated 5-fold cross-validation, while an interactive learning analytics dashboard was designed to translate predictions into risk alerts and actionable recommendations for lecturers. Results and Discussion: Random Forest achieved the best overall performance with 87.0% accuracy, 0.88 precision, 0.94 recall, a macro-averaged F1-score of 0.92, and an AUC of 0.89, outperforming XGBoost and Logistic Regression. Assignment regularity and average quiz scores were identified as the most influential predictors. Qualitative feedback from two lecturers indicated that integrating risk information within a familiar LMS interface could support more practical and timelier student intervention. However, the small single-course sample limits generalizability. Conclusion: The proposed approach demonstrates the feasibility of integrating machine learning prediction and learning analytics dashboards as an early-warning mechanism, while further validation using larger, multi-course, and multi-institutional datasets is required.
subject: Data Science
  topic: section
subject: Machine Learning
  topic: keywords
subject: Pass/Fail Prediction
  topic: keywords
subject: Learning Management System
  topic: keywords
subject: Educational Data Mining
  topic: keywords
subject: Learning Analytics Dashboard
  topic: keywords
subject: KDD Framework
  topic: keywords
creator: Pratiwi Rachmadi
creator: Lucia Sri Istiyowati
creator: Nani Krisnawaty Tachjar
rights: Copyright (c) 2026 Pratiwi Rachmadi https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 183
ending page: 189
doi: 10.56705/ijodas.v7i2.415

An LLM-Based AI Task Agent for Academic Task Management with n8n and Telegram
type: item
description: Introduction: Managing multiple academic tasks with overlapping deadlines remains challenging for university students, while conventional task-management applications still require substantial manual organization and prioritization. This study develops an LLM-based AI Task Agent that enables conversational academic task management through Telegram and workflow automation. Method: The proposed system integrates Telegram as the interaction interface, n8n for workflow orchestration, an LLM-based AI agent for natural-language interpretation and tool selection, Google Sheets for task-data operations, PostgreSQL for conversational memory, and scheduled workflows for automated reminders. The system supports Create, Read, Update, and Delete operations, contextual priority recommendations based on deadline, urgency, and lecturer strictness, and proactive reminders. Functional performance and response time were evaluated across the primary system functions. Results and Discussion: Create, Read, Update, and Delete operations achieved 100% functional accuracy, while priority recommendation and automated reminder functions achieved 95%. Recorded processing times ranged from 3.1 to 3.5 seconds, with an average of approximately 3.32 seconds. The results demonstrate that separating LLM-based interpretation from predefined external tool execution enables reliable conversational task management while maintaining controlled data operations. Conclusion: The proposed LLM-based AI Task Agent demonstrates the feasibility of integrating conversational interaction, executable task-management functions, contextual prioritization, memory, and proactive reminders within a unified Telegram-based academic workflow.
subject: Data Science
  topic: section
subject: SQL Injection
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Long Short-Term Memory
  topic: keywords
subject: Convolutional Neural Network
  topic: keywords
subject: Embedding
  topic: keywords
creator: Nabila Widiyanti
creator: Tasrif Hasanuddin
creator: Huzain Azis
rights: Copyright (c) 2026 Huzain Azis, Nabila Widiyanti https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 363
ending page: 375
doi: 10.56705/ijodas.v7i2.412

Comparing ECLAT and Decision Tree for Drug Therapy Recommendation Rules on Multi Label Clinical Data
type: item
description: Introduction: Accurate sorting of plastic waste using Resin Identification Codes (RICs) is essential for improving recycling quality. However, conventional deep learning approaches generally require large labeled datasets, which are difficult and costly to collect for small RIC symbols on plastic packaging. Method: This study employed a Few-Shot Learning approach based on Prototypical Networks using a 7-way 5-shot episodic training configuration. A self-collected dataset of 350 images covering seven RIC categories was used, and three backbone architectures, ConvNet4, ResNet-18, and EfficientNet-B2, were compared. An ablation study evaluated support-set augmentation, followed by supervised fine-tuning of the selected model. Results and Discussion: EfficientNet-B2 achieved the highest episodic accuracy of 93.36%, outperforming ResNet-18 at 88.71% and ConvNet4 at 54.14%. EfficientNet-B2 with light augmentation achieved 85.71% accuracy on the fixed 42-image test set. Most errors occurred between visually similar HDPE and PP symbols. Fine-tuning corrected five of six misclassifications, increasing test accuracy to 90.48% and the F1-score from 0.857 to 0.903. Conclusion: Prototypical Networks with an EfficientNet-B2 backbone and cosine distance provide an effective approach for RIC classification under limited-data conditions and offer a practical foundation for automated plastic-waste sorting systems.
subject: Data Science
  topic: section
subject: ECLAT
  topic: keywords
subject: Decision Tree
  topic: keywords
subject: Association Rule Mining
  topic: keywords
subject: Drug Recommendation
  topic: keywords
subject: Clinical Decision Support
  topic: keywords
creator: Muh Rayhan Fahreza Rayhan
creator: Harlinda
creator: Herdianti Darwis
creator: Roesman Ridwan Raja
rights: Copyright (c) 2026 Muh Rayhan Fahreza Rayhan, Harlinda, Herdianti Darwis, Roesman Ridwan Raja https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 168
ending page: 182
doi: 10.56705/ijodas.v7i2.410

Handling Imbalanced Data in Concept Map Proposition Quality Classification: A Comparative Study of Resampling and Class-Weighted SVM
type: item
description: Introduction: Concept maps are widely used in education to represent students’ knowledge structures, while automatic proposition-level quality classification can support faster and more consistent assessment. However, imbalanced class distributions may cause classification models to favor majority classes and reduce performance on minority categories. Method: This study compared several imbalance-handling techniques for concept map proposition quality classification using Support Vector Machine (SVM) with TF-IDF features. The dataset comprised 691 propositions grouped into four quality classes, with class 3 dominating the distribution. The evaluated approaches included baseline training, Random OverSampler, SMOTE, ADASYN, SMOTE-Tomek, SMOTE-ENN, and cost-sensitive SVM with class_weight='balanced'. Data were divided using an 80:20 stratified split, with balancing applied only to the training set. Results and Discussion: The cost-sensitive SVM achieved the best overall performance with 0.8417 accuracy, 0.7098 macro precision, 0.7320 macro recall, 0.7149 macro F1-score, 0.8455 weighted F1-score, and 0.2158 MAE. Among resampling approaches, ADASYN and Random OverSampler were the most competitive, whereas SMOTE-based variants produced lower macro-level performance. Conclusion: Class-weighted SVM is more effective than the evaluated resampling techniques for sparse, imbalanced concept map proposition data, highlighting the importance of macro-level and ordinal-aware metrics in educational text classification.
subject: Data Science
  topic: section
subject: Concept Map
  topic: keywords
subject: Proposition Quality Classification
  topic: keywords
subject: Imbalanced Data
  topic: keywords
subject: Support Vector Machine
  topic: keywords
subject: TF-IDF
  topic: keywords
subject: Class-Weighted SVM
  topic: keywords
creator: Nurul Rismayanti
creator: Lukman Syafie
creator: A Sinra
creator: Rahmadani Rahmadani
creator: Roesman Ridwan Raja
rights: Copyright (c) 2026 Rahmadani Rahmadani, Nurul Rismayanti, Lukman Syafie, A Sinra, Roesman Ridwan Raja https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-17
publication date: 2026-03-17
starting page: 1
ending page: 11
doi: 10.56705/ijodas.v7i1.482

Resin Code Classification on Plastic Packaging Using Few-Shot Learning
type: item
description: Introduction: Accurate sorting of plastic waste using Resin Identification Codes (RICs) is essential for improving recycling quality. However, conventional deep learning approaches generally require large labeled datasets, which are difficult and costly to collect for small RIC symbols on plastic packaging. Method: This study employed a Few-Shot Learning approach based on Prototypical Networks using a 7-way 5-shot episodic training configuration. A self-collected dataset of 350 images, comprising 50 images for each of seven RIC categories—PETE, HDPE, PVC, LDPE, PP, PS, and OTHER—was used. Three backbone architectures, ConvNet4, ResNet-18, and EfficientNet-B2, were compared. An ablation study evaluated support-set augmentation, followed by supervised fine-tuning of the selected model. Results and Discussion: EfficientNet-B2 achieved the highest episodic accuracy of 93.36%, outperforming ResNet-18 at 88.71% and ConvNet4 at 54.14%. EfficientNet-B2 with light augmentation attained 85.71% accuracy on the fixed 42-image test set, with perfect recall for PVC, LDPE, and PP. Most errors involved visually similar HDPE and PP symbols. Fine-tuning corrected five of six misclassifications, increasing test accuracy to 90.48% and the F1-score from 0.857 to 0.903. Conclusion: Prototypical Networks with an EfficientNet-B2 backbone and cosine distance provide an effective solution for RIC classification under limited-data conditions and offer a practical foundation for automated plastic-waste sorting systems.
subject: Data Science
  topic: section
subject: Few-Shot Learning
  topic: keywords
subject: Prototypical Network
  topic: keywords
subject: RIC Classification
  topic: keywords
subject: EfficientNet-B2
  topic: keywords
subject: Plastic Waste Sorting
  topic: keywords
creator: Alyani Noor Septalia
creator: Anindita Septiarini
creator: Masna Wati
rights: Copyright (c) 2026 Anindita Septiarini, Alyani Noor Septalia, Masna Wati https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 157
ending page: 167
doi: 10.56705/ijodas.v7i2.406

Drug Recommendation Using Multilabel Classification with Decision Tree Based on Patient Complaints and Diagnoses
type: item
description: This study develops a drug recommendation system using multilabel classification with the Decision Tree algorithm based on patient complaint and diagnosis data from electronic medical records. The dataset consists of patient visit records from a community health center in Pangkajene and Kepulauan Regency and is transformed using multi-hot encoding. Model performance is evaluated under three dataset scenarios (N=500, N=800, and N=1000) using multilabel metrics, including Micro-F1, Samples-F1, Hamming Loss, Jaccard Similarity, Hit@5, Precision@K, and Recall@K. The best Decision Tree model achieved a Micro-F1 score of 0.292, Samples-F1 of 0.281, and Hit@5 of 0.690 on the N=1000 dataset scenario. Bootstrap validation with 1000 iterations indicates relatively stable performance, with narrow confidence intervals across evaluation metrics. These results show that the multilabel Decision Tree model is capable of capturing relationships between patient complaints, diagnoses, and drug therapies while maintaining an interpretable decision structure
subject: Data Science
  topic: section
subject: Multilabel Classification
  topic: keywords
subject: Decision Tree
  topic: keywords
subject: Drug Recommendation System
  topic: keywords
subject: Electronic Medical Records
  topic: keywords
subject: Clinical Decision Support
  topic: keywords
creator: Muh Aristsyah Malik
creator: Harlinda
creator: Herdianti Darwis
rights: Copyright (c) 2026 Muh Aristsyah Malik, Harlinda, Herdianti Darwis https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 142
ending page: 156
doi: 10.56705/ijodas.v7i1.397

Hybrid Deep Learning Models For Gold Price Prediction: Enhancing Forecast In Volatile Financial Markets
type: item
description: Introduction: Gold is widely regarded as a long-term store of value and a hedge against inflation, yet its short-term price volatility creates significant challenges for investment decision-making and requires accurate forecasting methods. This study evaluates a hybrid deep learning approach combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to capture both short-term fluctuations and long-term temporal dependencies in gold price movements. Method: Historical daily gold closing-price data comprising 2,735 observations from 2015 to 2025 were collected and normalized using Min-Max Scaling. The data were divided chronologically into 80% training and 20% testing sets. A hybrid CNN–LSTM model was trained using the Adam optimizer with a learning rate of 0.0001, dropout of 0.2, a timestep of 30, and batch sizes of 16, 32, and 64. Model performance was evaluated using Root Mean Square Error (RMSE). Results and Discussion: The batch size of 16 achieved the best performance, producing the lowest validation RMSE of 0.0929 and an RMSE of 11.518535% after denormalization, outperforming batch sizes of 32 and 64. The model also followed actual gold-price trends more closely, while the inclusion of Dense and Dropout layers improved generalization. Conclusion: The CNN–LSTM hybrid model, particularly with a batch size of 16, provides an effective approach for forecasting volatile gold prices by integrating local pattern extraction with long-term temporal modeling.
subject: Data Science
  topic: section
subject: Gold price prediction
  topic: keywords
subject: Hybrid Deep Learning
  topic: keywords
subject: Convolutional Neural Network
  topic: keywords
subject: Long Short-Term Memory
  topic: keywords
creator: Ni Luh Wiwik Sri Rahayu Ginantra
creator: Ni Wayan Yeni Pratiwi
creator: Christina Purnama Yanti
creator: Wayan Gede Suka Parwita
rights: Copyright (c) 2026 Ni Luh Wiwik Sri Rahayu Ginantra, Ni Wayan Yeni Pratiwi , Christina Purnama Yanti, Wayan Gede Suka Parwita https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 213
ending page: 222
doi: 10.56705/ijodas.v7i2.394

The Effect of Clinical Rule-Based Domain Filtering on the Performance of FP-Growth-Based Drug Recommendation Systems
type: item
description: This study analyzes the effect of domain filtering on drug recommendation systems based on association rule mining using the FP-Growth algorithm with Neural Collaborative Filtering (NCF) as a comparison. The dataset used was derived from patient medical records containing attributes such as complaints, diagnoses, and drug therapies, with a total of 1,000 patient transactions. To avoid data leakage, the dataset was randomly divided into 70% training data and 30% test data before the modeling process was carried out. Domain filtering was applied by limiting the rule structure so that complaints and diagnoses acted as antecedents and drugs as consequents. The performance of the recommendation system was evaluated using the Precision@5, Recall@5, and Normalized Discounted Cumulative Gain (NDCG@5) metrics. The results of the experiment show that the FP-Growth approach with domain filtering produces higher Precision@5 and NDCG@5 values than the non-filtering approach. The Wilcoxon Signed-Rank test shows that the difference is statistically significant, while effect size analysis using Cliff's Delta shows a practically meaningful impact. Furthermore, a comparison with Neural Collaborative Filtering shows that the collaborative filtering-based approach is less effective on transactional clinical prescription data with limited historical interactions. These findings indicate that integrating medical domain knowledge into FP-Growth can improve the clinical relevance and quality of drug recommendation rankings
subject: Data Science
  topic: section
subject: Association Rule Mining
  topic: keywords
subject: FP-Growth
  topic: keywords
subject: Domain Filtering
  topic: keywords
subject: Drug Recommendation System
  topic: keywords
subject: Medical Data
  topic: keywords
creator: Muhammad Zaqly Luluang
creator: Irawati
creator: Herdianti Darwis
rights: Copyright (c) 2026 Muhammad Zaqly Luluang, Irawati, Herdianti Darwis https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 92
ending page: 104
doi: 10.56705/ijodas.v7i1.398

Applicant Data Segmentation and Pattern Analytics for University Admissions Strategy Using Hybrid SOM and K-Means
type: item
description: Introduction: Effective university admissions strategies require a clear understanding of applicant demographics, academic characteristics, geographic origins, and information channels. This study applies a hybrid clustering approach to identify meaningful applicant segments that can support targeted recruitment and resource allocation. Method: Historical applicant records from Universitas Sahid Surakarta covering 2021–2025 were preprocessed through data cleaning, one-hot encoding of categorical variables, and Min-Max normalization. A hybrid Self-Organizing Map (SOM) and K-Means framework was employed, where SOM projected high-dimensional applicant characteristics into a lower-dimensional topological representation and K-Means partitioned the resulting prototypes into distinct clusters. Clustering quality was evaluated using the Davies–Bouldin Index (DBI) to determine the optimal number of segments. Results and Discussion: The lowest DBI was obtained for three clusters, indicating the most appropriate segmentation structure. The resulting groups were characterized as proximity-driven local applicants, regional career-oriented applicants dominated by vocational-school backgrounds, and high-achieving out-of-region applicants with stronger academic performance and greater reliance on institutional websites and search channels. These patterns provide actionable insight for differentiated recruitment strategies. Conclusion: The hybrid SOM–K-Means approach effectively identifies interpretable applicant segments and provides descriptive intelligence that can support more targeted marketing, channel selection, scholarship strategies, and admissions resource allocation in higher education
subject: Data Science
  topic: section
subject: Davies-Bouldin Index
  topic: keywords
subject: Data Mining
  topic: keywords
subject: K-Means
  topic: keywords
subject: Prediction
  topic: keywords
subject: Segmentation
  topic: keywords
subject: SOM
  topic: keywords
creator: Diyah Ruswanti
creator: Dahlan Susilo
rights: Copyright (c) 2026 Diyah Ruswanti, Dahlan Susilo https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 291
ending page: 299
doi: 10.56705/ijodas.v7i2.388

Implementation of an AI Agent Chatbot with a Dynamic Knowledge Base from Google Drive for Journal Information Service
type: item
description: This study presents the implementation of an AI agent chatbot to support journal information services on the Open Journal Systems (OJS) platform and the Telegram messaging application using a dynamic knowledge base sourced from Google Drive. The chatbot provides automated responses to user inquiries related to journal scope, publication fees, submission procedures, and review timelines, while allowing journal administrators to update information content without modifying the system. Functional testing results indicate that the chatbot delivers accurate and consistent information with acceptable response times across both platforms. The implementation demonstrates that integrating an AI agent chatbot with a dynamic knowledge base can enhance information accessibility, reduce administrative workload, and improve service efficiency in academic journal management
subject: Data Science
  topic: section
subject: AI agent chatbot
  topic: keywords
subject: Open Journal Systems
  topic: keywords
subject: Dynamic Knowledge Base
  topic: keywords
subject: Journal Information Services
  topic: keywords
subject: Telegram
  topic: keywords
creator: Huzain Azis
creator: Randika sakha Ramadani Putra Ahmad
creator: Irawati Syahriawan
rights: Copyright (c) 2026 Huzain Azis, Randika sakha Ramadani Putra Ahmad, Irawati Syahriawan https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 58
ending page: 65
doi: 10.56705/ijodas.v7i1.384

Comparison of Naïve Bayes and SVM in Sentiment Analysis of ChatGPT for Learning on X and YouTube
type: item
description: The rapid development of artificial intelligence technology has encouraged users to actively express opinions on social media platforms such as X and YouTube, including discussions on the use of ChatGPT as a learning support tool. This study aims to analyze public sentiment toward the use of ChatGPT in learning contexts by comparing the performance of the Naïve Bayes and Support Vector Machine (SVM) classification methods. A total of 5,500 comments from platform X and 5,543 comments from YouTube were collected through a crawling process using relevant keywords during the period from January 2023 to December 2025. The data were preprocessed and labeled into three sentiment classes (positive, negative, and neutral) using a lexicon-based approach with the INSET Lexicon. Feature extraction was conducted using the Term Frequency–Inverse Document Frequency (TF-IDF) method, and the dataset was divided into training and testing sets with an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results show that the SVM classifier consistently outperformed the Naïve Bayes method on both platforms. On platform X, SVM achieved an accuracy of 76.67%, while Naïve Bayes obtained 74.60%. On YouTube, SVM achieved an accuracy of 73.10%, significantly higher than Naïve Bayes at 62.04%. These findings indicate that SVM is more effective for sentiment analysis of social media data related to the use of ChatGPT in learning environments
subject: Data Science
  topic: section
subject: Naive Bayes
  topic: keywords
subject: Support Vector Machine(SVM)
  topic: keywords
subject: Social Media
  topic: keywords
subject: Sentiment Analysis
  topic: keywords
subject: Chat GPT
  topic: keywords
creator: Ni Putu Eka Swari
creator: Ni Wayan Jeri Kusuma Dewi
creator: Ni Ketut Utami Nilawati
creator: Aniek Suryanti Kusuma
creator: Ni Luh Wiwik Sri Rahayu Ginantra
rights: Copyright (c) 2026 Ni Putu Eka Swari, Ni Wayan Jeri Kusuma Dewi, Ni Ketut Utami Nilawati, Aniek Suryanti Kusuma, Ni Luh Wiwik Sri Rahayu Ginantra https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 66
ending page: 78
doi: 10.56705/ijodas.v7i1.382

Kodály Hand Sign Recognition from Hand Landmarks Using XGBoost
type: item
description: Introduction: Angklung is a traditional Indonesian musical instrument that continues to evolve through digital technology. However, computer vision–based gesture recognition for controlling physical angklung instruments remains limited. This study investigates landmark-based recognition of Kodály hand signs and evaluates its application for real-time angklung interaction. Method: Hand landmarks were extracted using MediaPipe Hands from RGB camera input. Each gesture was represented by 63 normalized numerical features derived from 21 landmarks. The dataset consists of 8,000 images representing eight Kodály gesture classes (Do–Do'). Gesture classification was performed using the Extreme Gradient Boosting (XGBoost) algorithm. Model evaluation applied a subject-independent two-fold scheme using accuracy, precision, recall, F1-score, and confusion matrix analysis. Real-time system trials were conducted under different lighting conditions and capture distances, and TCP communication with an ESP32 controller was evaluated. Results: The model achieved 96.63% accuracy in Fold 1 and 96.40% in Fold 2. Misclassifications were mainly observed between visually similar gestures, particularly La and Mi. Separate real-time system trials showed consistent recognition under bright lighting, while accuracy decreased under dim lighting, especially for Do (90%) and Mi (86.7%). Gesture recognition remained reliable up to approximately 1.5 m. TCP testing over 200 command events recorded 0% failed acknowledgments with a mean round-trip time of 87.36 ms. Conclusion: These indicate that landmark-based Kodály gesture classification using MediaPipe Hands and XGBoost can support real-time angklung interaction under controlled conditions, although improvements are needed for low-light environments and visually similar gestures
subject: Data Science
  topic: section
subject: Angklung
  topic: keywords
subject: Gesture Recognition
  topic: keywords
subject: XGBoost
  topic: keywords
subject: MediaPipe Hands
  topic: keywords
subject: Kodály Hand Sign
  topic: keywords
creator: Achmad Zulfikar
creator: Farniwati Fattah
creator: Andi Widya Mufila Gaffar Gaffar
rights: Copyright (c) 2026 Achmad Zulfikar, Farniwati Fattah, Andi Widya Mufila Gaffar https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 79
ending page: 91
doi: 10.56705/ijodas.v7i1.391

Comparing Sentiment Labeling with RoBERTa and IndoBERTweet on Public Opinion of Program Makan Bergizi Gratis
type: item
description: The Program Makan Bergizi Gratis (MBG) is a flagship program of the Prabowo Subianto administration launched in 2024, triggering diverse public responses on social media. Sentiment analysis using deep learning models offers an effective approach to understanding public opinion at scale. However, selecting the appropriate model for Indonesian social media text remains challenging. This study aims to compare the performance of two pretrained transformer models, RoBERTa Base and IndoBERTweet Base, in conducting automatic sentiment labeling on Indonesian tweets related to the MBG program using a zero-shot labeling approach without human-annotated ground truth. A total of 1,831 tweets were collected from platform X and preprocessed using case folding, normalization, and stopword removal. Both models were applied in parallel to label each tweet with sentiment categories (positive, neutral, negative) along with confidence scores. The comparison was evaluated using agreement rate, Cohen's Kappa, and confidence score analysis. RoBERTa Base exhibits a conservative tendency with 75.20% neutral labels, while IndoBERTweet Base produces a more balanced distribution (68.16% neutral). The comparison shows 77.28% agreement with Cohen's Kappa of 0.490 (Moderate Agreement). RoBERTa Base achieves higher confidence (mean: 0.9559, 83.01% above 0.95) compared to IndoBERTweet Base (mean: 0.9236, 68.65% above 0.95). IndoBERTweet Base is more effective in detecting negative sentiment, identifying nearly twice as many negative tweets (13.54% vs. 7.48%). This study recommends IndoBERTweet Base for exploratory research requiring sensitive sentiment detection and RoBERTa Base for precision-critical applications. An ensemble approach combining both models is recommended for production-critical applications
subject: Data Science
  topic: section
subject: Makan Bergizi Gratis (MBG) Program
  topic: keywords
subject: IndoBERTweet
  topic: keywords
subject: Public Opinion, RoBERTa
  topic: keywords
subject: Sentiment Analysis
  topic: keywords
subject: Transformer
  topic: keywords
creator: Putri Nur Rezky
creator: Dolly Indra
creator: Herdianti
rights: Copyright (c) 2026 Putri Nur Rezky, Dolly Indra, Herdianti https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 105
ending page: 118
doi: 10.56705/ijodas.v7i1.381

Deep Learning-Based Blood Cell Image Classification Using ResNet18 Architecture
type: item
description: The classification of white blood cells (WBC) plays a critical role in haematological diagnostics, yet manual examination remains a labour-intensive and subjective process. In response to this challenge, this study investigates the application of deep learning, specifically the ResNet18 convolutional neural network architecture, for the automated classification of blood cell images into four classes: eosinophils, lymphocytes, monocytes, and neutrophils. The dataset used comprises microscopic images annotated by cell type and is divided into training and validation sets with an 80:20 ratio. Standard pre-processing techniques such as image normalization and augmentation were applied to enhance model robustness and generalization. The model was fine-tuned using transfer learning with pre-trained weights from ImageNet and optimized using the Adam optimizer. Performance was evaluated through a comprehensive set of metrics including accuracy, precision, recall, F1-score, mean squared error (MSE), and root mean squared error (RMSE). The best model achieved a validation accuracy of 86.89%, with macro-averaged precision, recall, and F1-score of 0.8738, 0.8690, and 0.8688, respectively. Lymphocyte classification yielded the highest F1-score (0.9515), while eosinophils posed the greatest classification challenge, as evidenced by lower precision and higher misclassification rates in the confusion matrix. Error-based evaluation further supported the model’s consistency, with an MSE of 0.7125 and RMSE of 0.8441. These results confirm that ResNet18 is capable of learning discriminative features in complex haematological imagery, providing an efficient and reliable alternative to manual analysis. The findings suggest potential for practical implementation in clinical workflows and pave the way for further research involving multi-model ensembles or cell segmentation pre-processing for improved precision
subject: Data Science
  topic: section
subject: Blood Cell Classification
  topic: keywords
subject: ResNet18
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Medical Imaging
  topic: keywords
subject: Multi-class Evaluation
  topic: keywords
creator: Thomas Edyson Tarigan
creator: Agung Budi Prasetyo
creator: Emy Susanti
rights: Copyright (c)
date: 2025-12-02
publication date: 2025-12-02
starting page: 294
ending page: 300
doi: 10.56705/ijodas.v6i2.300

Sentiment Analysis of Student Comments on Facilities and Infrastructure at Instiki Using Retrieval Augmented Generation
type: item
description: This research was conducted to analyze the sentiment of student comments on infrastructure facilities at the Indonesian Institute of Business and Technology (INSTIKI) to overcome the problem of comment analysis that was previously done manually. The data used is in the form of student comments in 2024. The method used in this study is Retrieval Augmented Generation (RAG) with data labeling using Lexicon-Based. The test was carried out on three Large Language Models (LLMs), namely indobenchmark/indobert-base-p1, TinyLlama/TinyLlama-1.1B-Chat-v1.0, and w11wo/indonesian-roberta-base-sentiment-classifier. The test results showed that the indobenchmark/indobert-base-p1 model produced the highest accuracy of 80% in both test sessions compared to other models. The TinyLlama/TinyLlama-1.1B-Chat-v1.0 model produced 60% accuracy in session 1 and 65% in session 2, while the w11wo/indonesian-roberta-base-sentiment-classifier model produced 60% accuracy in both test sessions. The difference in the performance of these three LLMs shows that the model's understanding of Indonesian can affect the results of sentiment predictions.
subject: Data Science
  topic: section
subject: Sentiment Analysis
  topic: keywords
subject: Facilities and Infrastructure
  topic: keywords
subject: Retrieval Augmented Generation (RAG)
  topic: keywords
subject: Lexicon-Based
  topic: keywords
subject: Student Comments
  topic: keywords
creator: Ni Putu Juliana Dewi
creator: I Kadek Dwi Gandika Supartha
creator: I Putu Yoga Indrawan
creator: Ketut Jaya Atmaja
rights: Copyright (c) 2025 Ni Putu Juliana Dewi, I Kadek Dwi Gandika Supartha, I Putu Yoga Indrawan, Ketut Jaya Atmaja https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 575
ending page: 587
doi: 10.56705/ijodas.v6i3.377

Sales Forecasting Analysis Using Fuzzy Time Series and Simple Linear Regression Methods at Toko Ari
type: item
description: Introduction: Forecasting, often referred to as prediction, can actually help assess conditions or predict future sales. In the business world forecasting is crucial because it can help companies plan their future operations especially when faced with sudden increases and decreases in sales and stockpiles. Especially in retail forecasting is extremely helpful in purchasing merchandise, managing inventory in the warehouse, and reducing losses due to changing customer preferences. Ari's shop, located on Jalan Raya Samu, Singapadu Kaler, Gianyar, Bali, also experiences increases and decreases in monthly sales. Therefore, it is hoped that this sales forecasting can help maintain more stable and smooth operations. Methods: This study used two methods to forecast sales: Fuzzy Time Series (FTS) and Simple Linear Regression (SLR), to predict figures from Ari's shop's monthly sales data. Both methods use the same dataset, which is Ari's Store sales data for 13 months, from January 2024 to January 2025. The forecast results are then compared using the Mean Absolute Percentage Error (MAPE), which measures the model's accuracy in predicting results. Results: Based on the sales forecasts performed, both models produced fairly accurate predictions due to their low MAPE values, below 10%. Of the two methods, Simple Linear Regression provided more accurate results with a MAPE of 3.57%. Meanwhile, the Fuzzy Time Series method produced a MAPE of 5.53%. This difference in values indicates that the linear regression model is more appropriate for Ari's Store sales data, especially since the data pattern tends to follow a linear trend.
subject: Data Science
  topic: section
subject: Sales Forecasting
  topic: keywords
subject: Fuzzy Time Series
  topic: keywords
subject: Simple Linear Regression
  topic: keywords
subject: MAPE
  topic: keywords
subject: Retail Business
  topic: keywords
creator: Ni Luh Sri April Yanti
creator: Ni Wayan Jeri Kusuma Dewi
creator: I Gede Made Yudi Antara
creator: Desak Made Dwi Utami Putra
creator: Putu Wirayudi Aditama
rights: Copyright (c) 2025 Ni Luh Sri April Yanti, Ni Wayan Jeri Kusuma Dewi, I Gede Made Yudi Antara, Desak Made Dwi Utami Putra, Putu Wirayudi Aditama https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 473
ending page: 480
doi: 10.56705/ijodas.v6i3.368

Medium Range Meteorological Drought Prediction Based on SPEI-3 Using Ensemble Machine Learning and Deep Learning in North West Province, South Africa
type: item
description: Meteorological drought monitoring is a pivotal action in everyday humankinds’ activities around the globe. It evaluates atmospheric conditions using weather observation instruments to measure atmospheric variables. Due to the highly sophisticated atmospheric environment, errors in drought monitoring and uncertain observation have been observed. Therefore, this research paper develops a lightweight Machine Learning (ML) and Deep Learning (DL) framework to forecast medium term meteorological drought in North West, South Africa using Standardized Precipitation Evapotranspiration Index at 3 -months (SPEI-3) timescale. This time scale reflects moisture deficits directly impacting agricultural production, early warning decisions and water management. The dataset used in this research study was obtained from South African Weather Services through a formal data request submission and not publicly accessible over a period of 10 years. Furthermore, the dataset consists of 20085 data entries and 11 data columns collected from 10 weather stations. The proposed models include SVR-RF, and, CNN-LSTM-ANN, compared to benchmark models, such as SVR, RF, CNN, LSTM, ANN, CNN-LSTM evaluated using statistical metrics, such as MSE, MAE, and . The results demonstrated irregular drought patterns during the defined period with SPEI-3 values clustered below normal conditions. Similarly, validation results showed that SVR demonstrated strong predictive performance with competitive MSE of 0.28, low MAE of 0.34 and of 0.86. Although, the proposed CNN-LSTM-ANN and SVR-RF models did not exhibit competitive performance compared to benchmarking models, the result provides valuable comprehension, data collection, distribution, architecture, and computational power
subject: Data Science
  topic: section
subject: Ensemble Machine Learning
  topic: keywords
subject: Meteorological Drought Prediction
  topic: keywords
subject: NWP
  topic: keywords
subject: SPEI
  topic: keywords
creator: Reatlegile Phiri
creator: Bukohwo Michael Esiefarienrhe
creator: Ibidun Christiana Obagbuwa
rights: Copyright (c) 2025 Reatlegile Phiri, Bukohwo Michael Esiefarienrhe , Ibidun Christiana Obagbuwa https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 393
ending page: 411
doi: 10.56705/ijodas.v6i3.354

Sentiment Analysis of BRImo Reviews on Google Play Store Using SVM and KNN
type: item
description: The rapid growth of digital banking has increased user interaction through mobile banking apps such as BRImo (Bank Rakyat Indonesia). Google Play Store reviews provide valuable insight into app quality, but their unstructured format makes manual analysis inefficient. This study analyzes user sentiment toward BRImo and compares the performance of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) for sentiment classification. Reviews were collected using Google Play Scraper from May 2024 to May 2025, yielding 15,945 raw reviews. After cleaning (removing duplicates, symbols, links, emojis) and language filtering, 15,233 valid reviews remained. Sentiment labels were generated using two lexicon-based methods: INSET and VADER. Using INSET, the data consisted of 6,238 positive, 4,987 negative, and 4,383 neutral reviews, producing 11,225 reviews for modeling. Using VADER, 10,496 positive, 2,903 negative, and 1,834 neutral reviews were obtained, totaling 13,399 reviews. Datasets were split into 80% training and 20% testing with stratified sampling. Features were extracted using TF-IDF unigrams. Classification was performed using linear SVM and KNN, with the optimal K=3 selected via Grid Search. Models were evaluated using 5-fold cross-validation, reporting mean accuracy, precision, recall, and F1-score (macro-average for INSET; weighted-average for VADER due to class imbalance). Results show SVM consistently outperforms KNN, achieving 98.36% mean accuracy and 98.34% mean F1-score on INSET, and 95.59% mean accuracy and 95.56% mean F1-score on VADER. Overall, BRImo user sentiment is predominantly positive, and findings can guide developers in improving app stability and quality
subject: Data Science
  topic: section
subject: Sentiment Analysis
  topic: keywords
subject: BRImo
  topic: keywords
subject: Application Performance
  topic: keywords
subject: Reviews Google Play Store
  topic: keywords
subject: SVM
  topic: keywords
subject: KNN
  topic: keywords
creator: Olivia Sutriani Jelni
creator: Made Leo Radhitya
creator: Gede Wirya Wardhana
creator: Ni Wayan Jeri Kusuma
creator: Ni Made Mila Rosa Desmayani
rights: Copyright (c) 2025 Olivia Sutriani Jelni, Made Leo Radhitya, Gede Wirya Wardhana, Dewi, Ni Made Mila Rosa Desmayani https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 548
ending page: 562
doi: 10.56705/ijodas.v6i3.365