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INDONESIA
EDUMATIC: Jurnal Pendidikan Informatika
Published by Universitas Hamzanwadi
ISSN : -     EISSN : 25497472     DOI : 10.29408
Core Subject : Science, Education,
Computer Science & IT Education
EDUMATIC: Jurnal Pendidikan Informatika (e-ISSN: 2549-7472) adalah jurnal ilmiah bidang pendidikan informatika yang diterbitkan oleh Universitas Hamzanwadi dua kali setahun yaitu pada bulan Juni dan Desember. Adapun fokus dan skup jurnal ini adalah (1) Komputer dan Informatika dalam Pendidikan; (2) Model Pembelajaran dan Model TIK; (3) Pengembangan Media Pembelajaran Berbasis Teknologi Informatika; (4) Interaksi Manusia dan Komputer; (5) Sistem Informasi dan Teknologi Informasi.
Arjuna Subject : -
Articles 464 Documents
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Revisiting Resampling Strategies under Extreme Class Imbalance: Evidence from Large-Scale Online Payment Fraud Detection Ardiansyah, Mursyid; Abidin, Ali Asgar Zainal
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33272

Abstract

Extreme class imbalance in online payment fraud detection creates an accuracy paradox and an operational risk in which improving fraud capture can generate costly false alarms. This study uses a quantitative, experiment-based design to evaluate the operational impact of common resampling strategies under extreme skew using interpretable linear decision rules. The Online Payments Fraud dataset (6.36 million transactions) from Kaggle is analysed using six monetary balance/amount variables (amount, oldbalanceOrg, newbalanceOrig, oldbalanceDest, newbalanceDest) plus the rule-based isFlaggedFraud indicator to predict the isFraud label. Five training variants (no resampling, ROS, RUS, SMOTE, ADASYN) are compared with two linear decision rules: an ordinary least squares linear scoring model (thresholded at 0.5) and a linear SVM, using a leakage-free protocol in which resampling is applied only to the 80% training split and performance is assessed on an untouched, highly imbalanced 20% test set. The findings indicate that LinReg–RUS achieves the most balanced operating point (Precision 65.938%, Recall 47.718%, F1 55.367%, ROC-AUC 98.720%), whereas ADASYN increases recall but collapses precision (~2.1%), yielding F1 ≈4.17%. These results contribute controlled, large-scale evidence that under extreme imbalance, simpler resampling–model combinations can provide more deployable precision–recall trade-offs than aggressive synthetic sampling, supporting interpretable baselines for capacity-constrained payment screening.
Feature Interaction and Performance Analysis of RankSum-Based Extractive Summarization in Indonesian Scientific Articles Adityya, Verrino; Yohannes, Yohannes
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33443

Abstract

The extractive summarization of Indonesian scientific articles is hindered by a domain mismatch where established methodologies rely on news-corpus assumptions, whereas Indonesian scientific discourse follows rigid, IMRaD-driven structural and lexical patterns. This study aims to systematically analyze feature interaction effects and saturation behaviour in RankSum-based extractive summaries for Indonesian scientific articles. Designed as a controlled comparative experiment, this research evaluates a RankSum framework integrating variables, such as graph-based, semantic-thematic vectors, and structural heuristics. The dataset comprises 2,897 Indonesian journal articles (2021-2025) collected via web scraping from open-access university repositories. Analysis across 31 scenarios demonstrates that for Indonesian scientific articles, the assumption that increasing feature density improves performance is flawed, instead a feature saturation effect occurs. Results show that a 4-feature combination maximizes unigram lexical precision (ROUGE-1 0.3564), whereas the full 5-feature fusion is necessary to preserve global semantic integrity, structural flow, and stable (ROUGE-L 0.2018; BERTScore 0.6977). This study establishes a generalizable principle for domain-aware ATS by demonstrating that overcoming domain mismatch relies on navigating feature saturation through selection aligned with the document’s inherent logic rather than raw feature quantity.
Soybean Seed Quality Classification using Magnitude-Enhanced Multiple Channel LBP and SVM Chandra, Adrian; Yohannes, Yohannes
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33519

Abstract

Soybean is a major source of plant-based protein The quality of soybean seed affects resulting food. Therefore, image processing for classifying soybean seed quality is needed. Previous studies mainly used handcrafted or deep learning features and not evaluated local texture representations that using magnitude information for multi-class problems with high visual similarity. Traditional texture descriptors such as LBP or GLCM mainly using sign-based or global statistics and have limitations in representing colour-texture variations. This study aims to classify soybean seed quality using SVM with Multiple Channel Local Binary Pattern (MCLBP) and its enhanced variant with magnitude information (MCLBP+M) for feature extraction by utilizing correlations between colour channels through multi-radius approach. The dataset used is Soybean Seeds includes five classes: intact, spotted, immature, broken, and skin-damaged. This research conduct dataset splitting using 10-fold cross validation, data balancing (SMOTE), feature extraction, SVM model training and testing, and performance evaluation. The results show that MCLBP+M with Lab colour space and RBF kernel achieves accuracy of 86.30%, precision of 86.32%, recall of 85.99%, and F1-score of 86.07%. The results show that magnitude information in MCLBP+M consistently stable and improves classification performance across colour spaces and kernels, making it suitable for soybean seed quality classification.
An Integrated Safety Stock and Net Promoter Score System for Inventory and Customer Loyalty Badruzzaman, Arya Putra; Irawan, Yudie; Adiyono, Soni
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33557

Abstract

Manual and separate inventory management and customer loyalty monitoring often lead to information delays, record-keeping errors, low operational efficiency, and an unmonitored relationship between stock availability and customer perception. The aim of our research was to develop a web-based sales and loyalty information system that integrates Safety Stock and Net Promoter Score (NPS) methods into a single decision support framework. Our research is a study of system development using the Waterfall model, which includes the stages of requirements analysis, system design, implementation, testing, and maintenance, supported by use cases and activity diagrams. The findings of this study are an integrated system that is able to calculate minimum stock levels, safety stocks, risk of stock-outs, and display real-time NPS visualizations. The test results obtained through black box testing on system access, inventory processing, and NPS reporting show that all key functions are running well and to specification. The implications of this study suggest that the proposed system can improve inventory accuracy, reduce the risk of stock shortages, improve operational efficiency, and support objective, responsive, and sustainable managerial decision-making for small and medium-sized distributors through an integrated and reliable information system.
Mapping Digital Sentiment Landscapes of Hotel Reviews: A Machine Learning-Based Cross-Platform Analysis Ridwan, Muhammad Kholid; Irawan, Yudie; Setiawan, Raden Rhoedy
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33701

Abstract

The expansion of online travel agencies (OTAs) has produced large volumes of user-generated hotel reviews, offering important resources for sentiment analysis of consumer perceptions. However, prior studies largely rely on single-platform datasets and focus on classification performance, with limited attention to cross-platform sentiment consistency and the impact of data imbalance. This study aims to analyse and compare sentiment patterns across Traveloka, Tiket.com, and Accor, while evaluating a machine learning framework under imbalanced data conditions. This study adopts a quantitative experimental design using 3,000 Indonesian-language reviews collected via web scraping. The independent variable is reviewing text, and the dependent variable is sentiment classification (positive/negative). Data were preprocessed and transformed using TF-IDF, and classified using Multinomial Naïve Bayes, with performance evaluated by accuracy, precision, recall, and F1-score. The results show that positive sentiment consistently dominates across all platforms, with Accor achieving the highest performance, followed by Tiket.com and Traveloka. However, very high recall values for the positive class indicate substantial class imbalance, which biases predictions and reduces sensitivity to negative sentiment. This study provides empirical evidence of cross-platform sentiment consistency and highlights the importance of addressing data imbalance in sentiment modelling.
Mitigating Class Imbalance in Indonesian Sarcasm Detection: A Cross-Platform Transformer Study Maulana, A. Salky; Agastya, I Made Artha
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33724

Abstract

Sarcasm detection in Indonesian social media remains challenging due to implicit pragmatic expressions, severe class imbalance, and strong domain variation across platforms. Unlike prior Indonesian sarcasm studies that predominantly focus on in-domain accuracy using conventional balancing methods, this study provides the first systematic cross-platform analysis of generative data balancing under domain shift. We empirically examine whether GPT-4o based generative balancing improves robustness rather than accuracy-centric evaluation in Transformer-based sarcasm detection. Models trained on Twitter data are evaluated across Twitter, Reddit, and TikTok as an unseen domain. The results show that generative balancing yields limited gains in in-domain evaluation but consistently improves cross-domain robustness by increasing sarcasm recall, particularly for Base models. Notably, XLM-R Base achieves an absolute F1-score improvement of +10.8 points on TikTok, while IndoBERT-Large attains the highest in-domain F1-score of 0.7444. These findings indicate that generative augmentation partially mitigates class imbalance by enhancing robustness under domain shift, thereby repositioning sarcasm detection as a robustness-oriented problem and highlighting generative balancing as a complementary strategy rather than a substitute for larger Transformer models in cross-platform NLP settings.
Beyond Predictive Accuracy: Enhancing Parameter Stability in Multicollinear Time Series Forecasting via Regularisation Faisa, Daffa Kumara Khiar; Salam, Abu
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33925

Abstract

Multicollinearity in feature-based time series regression arises as a structural consequence of lagged and rolling feature construction. However, existing studies on Ridge and ElasticNet regularization adopt an accuracy-driven evaluation paradigm, with limited attention to parameter stability, shrinkage behavior, and sensitivity to regularization strength. This study shifts the evaluation of regularized linear models from predictive accuracy toward stability-oriented assessment. Using daily electricity consumption data from the UCI Repository, Linear Regression, Ridge, and ElasticNet models are examined under engineered temporal features derived from stability-based lag pruning, rolling statistics, and correlation-informed feature selection. Model evaluation focuses on bias–variance behavior, coefficient shrinkage, regularization sensitivity, and training–testing performance gaps. The results show that regularization improves stability, with the performance gap decreasing from 0.0961 in Linear Regression to 0.0608 under ElasticNet. These comparisons show that regularization stabilizes regression models via distinct shrinkage mechanisms, informing model selection beyond accuracy. Ridge exhibits conservative shrinkage averaging 6.06%, whereas ElasticNet induces stronger shrinkage averaging 46.32% and shows higher sensitivity to penalty strength. These findings provide methodological evidence that regularization in feature-based time series regression should be treated as a stability strategy rather than an accuracy optimization tool, offering guidance for electricity load forecasting under structurally redundant temporal features.
Citizen-Centric Decision Support Systems for Housing Assistance: A Low-Barrier E-Government Approach Manopo, Ben Shalom; Kenap, Audy Aldrin; Santa, Kristofel
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33945

Abstract

Digital public services often require account-based authentication that can create access barriers for citizens with limited digital literacy. In housing assistance services, such barriers may reduce the accessibility of systems intended to support vulnerable groups. This study aimed to develop and evaluate a low-barrier decision support system for housing assistance by integrating Guest Mode access with the Simple Additive Weighting (SAW) method. The study used a design-oriented development approach in collaboration with Dinas Perkim Minahasa. System requirements were identified through staff interviews, and the prototype was tested in the agency environment using real housing data. Validation was conducted by comparing system-generated SAW results with manual calculations performed by agency staff and by testing the main system functions operationally. The results showed that the system produced the same ranking results as the manual calculations in the tested cases, while staff evaluation indicated that the Guest Mode design simplified the submission process and reduced administrative barriers. This study contributes to inclusive e-government practice by proposing a citizen-centered, low-barrier architecture for housing assistance services that maintains decision support accuracy while improving service accessibility.
Mutual Information-Driven Feature Selection for Efficient DDoS Detection Using Modern Boosting Ensembles Fansuri, Muhammad Febrian; Kusrini, Kusrini
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.34012

Abstract

Distributed Denial of Service (DDoS) attacks generate high-dimensional network traffic that poses significant challenges for machine learning-based detection systems in terms of predictive accuracy and computational efficiency. This study presents a systematic evaluation of Mutual Information (MI) based feature selection applied to three modern boosting algorithms, namely XGBoost, LightGBM, and CatBoost, using the CIC-DDoS2019 dataset. A controlled experimental design was employed, where data partitioning was performed prior to resampling, and SMOTE was applied exclusively to the training set to prevent data leakage. Feature selection was conducted by identifying the top 25 features based on MI score saturation analysis. The results demonstrate that MI-based feature selection consistently improves classification performance while substantially reducing training time across all models. Among the evaluated methods, LightGBM achieves the best trade-off between accuracy and computational efficiency, reaching an accuracy of 99.88% with significantly reduced training cost. These findings indicate that feature quality plays a critical role in shaping the learning behaviour of boosting algorithms and that MI-based feature selection functions as a structural mechanism for enhancing model stability and scalability in high-dimensional DDoS detection scenarios.
On the Effectiveness of Lightweight CNN Architectures for Fine-Grained Coffee Bean Classification Burhanudhin, Akbar Muhamad; Sudibyo, Usman; Meindiawan, Eka Putra Agus
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.34044

Abstract

Distinguishing coffee bean varieties remains a significant challenge in the agricultural industry due to high inter-class similarity and the subtle morphological differences between species. This study aims to conduct a comparative evaluation of MobileNetV2 and EfficientNetB0 for fine-grained coffee bean classification, specifically investigating how efficiency-oriented architectural mechanisms such as depthwise separable convolution and compound scaling influence feature extraction. The research employed a quantitative experimental method using a private dataset of 2,400 images comprising Arabica, Robusta, and Liberica varieties. Data preprocessing included resizing to 224×224 pixels and augmentation, followed by training the two architectures using transfer learning under a controlled experimental framework. The results showed that EfficientNetB0 achieved superior performance with a testing accuracy of 99.17%, while MobileNetV2 attained a competitive accuracy of 98.33% with lower computational complexity. These results demonstrate that while EfficientNetB0 is optimal for high-precision industrial sorting, MobileNetV2 offers a highly efficient alternative for resource-constrained mobile applications. This study provides a scalable framework for automating quality control, effectively balancing architectural efficiency with the sensitivity required for accurate coffee variety identification.

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