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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Published by Ikatan Ahli Informatika Indonesia
ISSN : 25800760     EISSN : 25800760     DOI : https://doi.org/10.29207/resti.v2i3.606
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan hasil dari penelitian dan pemikiran untuk pengabdian pada Masyarakat luas dan sebagai sumber referensi akademisi di bidang Teknologi dan Informasi. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) menerima artikel ilmiah dengan lingkup penelitian pada: Rekayasa Perangkat Lunak Rekayasa Perangkat Keras Keamanan Informasi Rekayasa Sistem Sistem Pakar Sistem Penunjang Keputusan Data Mining Sistem Kecerdasan Buatan/Artificial Intelligent System Jaringan Komputer Teknik Komputer Pengolahan Citra Algoritma Genetik Sistem Informasi Business Intelligence and Knowledge Management Database System Big Data Internet of Things Enterprise Computing Machine Learning Topik kajian lainnya yang relevan
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,145 Documents
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Indonesian Hate Speech Detection under Class Imbalance Using a Soft-Voting Ensemble of IndoBERTweet and IndoRoBERTa Muhammad Alkaff; Eka Setya Wijaya; Fadliyanur Fadliyanur; Muhammad Bahit; Sinar Nadhif Ilyasa
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7681

Abstract

Hate speech detection on Indonesian social media remains challenging due to the coexistence of formal and highly colloquial language, as well as the moderate class imbalance typical of real-world datasets. Models trained under these conditions often skew toward the majority class and generalize poorly across linguistic registers. This study investigates whether a simple, training-free model-level ensemble can improve Indonesian hate speech detection under such conditions without resampling the data. IndoBERTweet and IndoRoBERTa, pretrained respectively on informal Twitter text and broader formal corpora, serve as complementary base models, and their class probabilities are combined through equal-weight soft voting. On the Indonesian Hate Speech Superset (N = 14,306), evaluated across five random seeds with paired significance testing, the soft-voting ensemble attains a macro-averaged F1 of 0.898 ± 0.003 and a macro recall of 0.899 ± 0.003. It significantly outperforms a TF-IDF SVM baseline and the IndoRoBERTa base model, while showing no significant difference from the stronger IndoBERTweet base model and a trained logistic-regression stacking ensemble. Notably, the ensemble matches the stacking ensemble without any additional training stage or meta-learner, and a calibration analysis shows it improves probability calibration over both base models. These results indicate that equal-weight probability averaging is a simple, reproducible, and competitive strategy for Indonesian hate speech detection under moderate class imbalance.
Lightweight Convolutional Neural Network for Robust and Label-Efficient Animal Sound Classification Ivan Saputra; Rikman Aherliwan Rudawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7366

Abstract

Automated bioacoustic monitoring is increasingly important for biodiversity observation, yet practical deployment is often limited by computational constraints and the scarcity of annotated audio data. This study introduces MelCNN, a custom compact end-to-end architecture designed to mitigate over-parameterization and negative transfer in animal sound classification under small-data conditions. A balanced dataset of 600 audio clips from three animal classes (Cat, Dog, and Cow) was standardized to 4.8 seconds, converted into Log-Mel spectrograms, and evaluated using 5-fold stratified cross-validation. The proposed Lightweight CNN with Global Average Pooling was compared against YAMNet-based transfer learning baselines and three additional modern lightweight CNN baselines, namely MobileNetV3-Small, ShuffleNetV2-1.0x, and EfficientNet-Lite0-style. The proposed model achieved 97.00% ± 2.01% Accuracy and 97.01% ± 2.00% Macro-F1 while retaining a compact parameter size of approximately 0.39 million, outperforming both the strongest transfer learning baselines and the added modern lightweight baselines in the present setting. The model also maintained performance above 90% at 12 kHz and preserved approximately 90.5% Macro-F1 when trained with only 30% of the available labeled data. These findings indicate that a domain-specific lightweight architecture can provide a favorable accuracy-efficiency trade-off for controlled small-data animal sound classification in resource-constrained monitoring scenarios.
Explainable XGBoost Early-Warning Framework for Academic Stress-Based Student Mental Health Risk Mapping Supriyono; Heru Noviyanto Firmansyah; Soni Adiyono
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7812

Abstract

Existing university mental health monitoring often depends on voluntary help-seeking or manual questionnaire interpretation, which may delay early support for students experiencing academic stress. This study proposes an explainable XGBoost-based early-warning framework for non-clinical mapping of student mental health risk from academic stress indicators. The single-site dataset comprised 1,002 anonymized student records from Universitas Muria Kudus. K-Means clustering was used to transform DASS-21 depression, anxiety, and stress scores into low, moderate-, and high-risk categories, while XGBoost predicted the cluster-derived labels using seven single-item academic stress indicators and engineered aggregate and interaction features. On a stratified hold-out testing set of 201 records, the model achieved weighted precision, recall, and F1-score values of 0.8907, 0.8905, and 0.8906, respectively, with class-level F1-scores of 0.9109 for low risk, 0.8900 for moderate risk, and 0.8713 for high risk. Additional ablation, clustering sensitivity, subgroup, threshold, and SHAP stability analyses were conducted to strengthen robustness and interpretability. The findings show that cumulative academic stress and interaction features involving parental expectations, exam anxiety, and learning-method adaptation were consistently influential predictors. The framework is intended to support early institutional prioritization and counseling referral, not clinical diagnosis. Generalization remains limited by the single-institution sample and the use of single-item academic stress indicators; therefore, local retraining and recalibration are required before institutional deployment, including implementation of the Streamlit prototype.
Schema-Guided Prompt Strategies for Text-to-SQL over Relational Databases Using Local LLMs Nurjayanti Nurjayanti; Adiwijaya Adiwijaya; Ade Romadhony; Alfian Akbar Gozali
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.6966

Abstract

Text-to-SQL systems translate natural language questions into executable SQL queries, allowing users without SQL expertise to access structured data stored in relational databases. Although Large Language Models (LLMs) have substantially improved SQL generation capabilities, many state-of-the-art Text-to-SQL approaches continue to rely on cloud-based models with high computational requirements. Such dependence limits their deployment in environments with limited computing resources. This study addresses this limitation by proposing schema-guided prompting strategies for Text-to-SQL generation using local LLMs. A chat-based application was developed using the Django Web Framework, while model inference was performed through the Ollama platform to enable the deployment of local LLMs. The proposed framework incorporates database schema information, including table structures and column attributes, into structured prompts to improve the alignment between natural language questions and SQL generation. Experiment results across multiple databases demonstrate that schema-guided prompting significantly improves Text-to-SQL performance. The highest accuracy was achieved by LLaMA 3 (8B) with objective-aware prompting, reaching an Exact Matching (EM) accuracy of 71.96%. These findings suggest that structured prompt engineering provides a practical alternative to model fine-tuning for locally deployed LLMs, offering an effective balance between SQL generation accuracy, computational efficiency, and data privacy. Future work will investigate fine-tuning strategies, example selection methods, and cross-domain evaluation to enhance SQL generation.
Modelling Plantar Pressure in a Smart Wearable Insole System Using Piezoresistive Sensors Sekhah Ulyana; Husneni Mukhtar; Willy Anugrah Cahyadi; Nigel Bryan Tang
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7044

Abstract

Biomechanical analysis plays a crucial role in evaluating movement patterns and pressure distribution. It enables early detection of abnormal gait and posture that may lead to long-term musculoskeletal problems. By identifying pressure imbalances, biomechanical assessment supports injury prevention, rehabilitation monitoring, and personalised orthotic design. However, conventional tools are often bulky and expensive, limiting accessibility. This study introduces an insole system that integrates piezoresistive sensors with Radial Basis Function (RBF) interpolation to provide cost-effective, real-time visualisation of plantar pressure. Tests involving five subjects, conducted during standing, stair climbing, and walking, revealed distinct load patterns with a strong correlation to reference data (R² = 0.925). The static standing test demonstrated balanced pressure and an average MAPE of 24.9%, while the walking cycle exhibited consistent heel-to-toe transitions and proper weight transfer. Operating at a 10 Hz sampling rate, the wearable insole accurately quantified plantar pressure variations, demonstrating its potential for real-time gait assessment and rehabilitation monitoring.
Enhancing E-Commerce Churn Prediction Accuracy Through the Combination of Gradient Boosting and Feature Selection Yogi Prasetyo Hernoto; Dandy Pramana Hostiadi; Putu Desiana Wulaning Ayu
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7076

Abstract

As the number of e-commerce users continues to expand, analyzing user activity has become essential for improving service quality, since customers are a key factor in strengthening competitiveness and business performance in this industry. Churn, in which customers discontinue service due to dissatisfaction, requires accurate detection to support effective retention strategies. To improve churn prediction accuracy, this study explores the optimal combination of feature selection methods and machine learning classifiers. ANOVA F-Test, Information Gain, Chi-Square, Recursive Feature Elimination (RFE), Variance Threshold, and Least Absolute Shrinkage and Selection Operator (LASSO) are the six feature selection techniques that are assessed in this study. These are combined with three classification algorithms: Gradient Boosting (GB), Random Forest (RF), and Logistic Regression (LR). Gradient Boosting with either Information Gain or LASSO yields the best results, achieving approximately 98% accuracy, 97% precision, 92% recall, 96% F1-score, and 99% ROC-AUC, based on testing on a publicly available Kaggle dataset. This approach outperforms prior studies, providing a robust framework for e-commerce developers to anticipate customer loss. By identifying key predictors of churn, the proposed model offers actionable insights to enhance service and enable proactive customer management.
Optimized Ensemble Learning Using Boosting, Stacking, and Voting for Early Stunting Risk Prediction Munawir Munawir; M. Khairul Anam; Liza Fitria; Nurul Fadillah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7107

Abstract

Stunting remains a pressing health issue in Indonesia’s coastal communities, where uneven access to nutrition services and maternal–child care can limit early prevention efforts. This study develops a machine learning framework to estimate stunting risk among children under five in the eastern coastal region of Aceh. The modeling process began with data cleaning and preparation, followed by class rebalancing with SMOTE and automated parameter tuning using Optuna. Four standalone classifiers were evaluated: Logistic Regression, Gaussian Naïve Bayes, Support Vector Machine, and Random Forest. Their outputs were then extended through three ensemble configurations, namely XGBoost-assisted boosting for each baseline model, a stacking scheme named Stackstun with Logistic Regression as the final learner, and an optimized weighted soft-voting model referred to as Votsstun. Performance was measured using accuracy, precision, recall, and F1-score. Among the single classifiers, Logistic Regression achieved the best result, with an accuracy of about 0.93. The strongest overall performance was obtained by the boosted Random Forest model, which reached an accuracy of 0.9952 and produced almost perfect class-level precision, recall, and F1-score. Votsstun also performed consistently, recording an accuracy of approximately 0.986, while its macro and weighted F1-scores approached 0.99. These results indicate that the combined use of class rebalancing, automated optimization, and ensemble learning can improve the robustness of stunting-risk classification. The proposed framework may assist local health agencies in identifying vulnerable children earlier, prioritizing limited intervention resources, and strengthening prevention programs in coastal communities.
Social Feature Integration for Entertainment Hoax Detection: Machine Learning and DistilBERT-Fusion Dodo Zaenal Abidin; Agus Siswanto; Chindra Saputra; Bhetantio Bhetantio
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7208

Abstract

Detecting entertainment hoaxes remains a pressing challenge in the social media era, where short texts, provocative headlines, and rapid dissemination complicate verification. This study compares the effectiveness of classical machine learning models and the transformer DistilBERT, both with and without the integration of social features. The modified GossipCop dataset, consisting of 22,140 entries, includes news titles as the primary text representation and social features such as tweet count, tweet density, and viral indicators. Text was represented using TF-IDF for classical models and DistilBERT tokenization for the transformer, with performance evaluated under stratified 10-fold cross-validation. Results show that incorporating social features consistently improves classical models, with XGBoost + Social achieving the best performance (PR-AUC 0.84; F1-score 0.76), surpassing DistilBERT-Fusion (PR-AUC 0.79). McNemar’s test confirmed significant differences in error distributions, strengthening the reliability of these findings. To reduce reliance on a single empirical dataset, the social feature integration pipeline was additionally validated on the PolitiFact benchmark from a different domain, where incorporating minimal social signals yielded measurable and statistically significant gains over text-only models (Wilcoxon p = 0.0137), confirming the robustness of the social feature effect beyond the entertainment domain. Overall, the results highlight that for short-text entertainment news under the conditions examined in this study, boosting models enriched with social signals can outperform transformer-based approaches. While DistilBERT-Fusion provided competitive results, its improvement over text-only DistilBERT remained limited due to the short-text nature of the dataset, indicating opportunities for richer fusion strategies in future research.
A Hybrid Approach of Factor Analysis and Decision Tree for Epilepsy Onset Prediction Using Public Datasets Eka Sabna; Des Suryani; Oktavia Dewi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7253

Abstract

Early prediction of epilepsy onset is crucial for supporting timely clinical intervention and reducing seizure-related complications. However, the high dimensionality and complexity of Electroencephalogram (EEG) and clinical data remain major challenges for conventional predictive models. This study proposes an interpretable hybrid framework integrating Exploratory Factor Analysis (EFA) and Decision Tree classification for early epilepsy onset prediction using public datasets. EFA was employed to reduce 15 observed variables into five clinically meaningful latent factors, which were subsequently used as inputs for the Decision Tree model. . According to experimental data, the suggested framework demonstrated steady classification performance with an accuracy of 88.14% and specificity of 88.73%, while its efficacy in identifying epilepsy beginning cases for early screening is highlighted by its sensitivity of 80.77%.. The latent factor representing clinical neurological abnormalities was identified as the most influential predictor in the classification process. Compared with conventional black-box machine learning approaches, the proposed model provides transparent decision rules and clinically meaningful interpretation while maintaining reliable predictive capability. All things considered, the suggested architecture is a viable path for creating clinically interpretable prediction models that facilitate transparent and trustworthy early epilepsy screening.
A Hybrid VADER–IndoBERT Framework for Robust Sentiment Analysis of Long and Ambiguous Indonesian Texts Margareta Valencia Suci Handayani; Ruri Suko Basuki; Muljono; Raden Arief Nugroho; Dhendra Maruhto; Yo Ceng Giap; Deshinta Arrova Dewi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7377

Abstract

The rapid expansion of digital learning platforms has increased the reliance on user-generated reviews for service evaluation and quality monitoring. However, sentiment analysis of Indonesian reviews remains challenging due to the prevalence of long sentences, mixed sentiments, and ambiguous linguistic expressions. This study introduces a Hybrid VADER–IndoBERT framework designed to improve sentiment classification robustness on complex Indonesian texts. A dataset of 4,904 Ruangguru application reviews was collected through web scraping and processed using a hybrid pipeline consisting of preprocessing, translation-based silver-standard sentiment labeling with VADER, and class balancing via Random Oversampling (ROS). The IndoBERT classifier was evaluated against a Bidirectional Long Short-Term Memory (BiLSTM) baseline. Experimental results show that IndoBERT achieved 90.9% accuracy, outperforming BiLSTM at 86.4%, demonstrating the superiority of Transformer-based architectures in capturing long-range dependencies and handling ambiguous sentiment cues. These findings highlight the effectiveness of integrating lexicon-based and Transformer-based approaches to achieve more robust sentiment analysis on linguistically complex Indonesian texts.

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