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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 1,164 Documents
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Stochastic Residual Selection in Simulated Annealing for Clusterwise Panel Optimization Luh Putu Widya Adnyani; Bagus Sartono; Asep Saefuddin; I Made Sumertajaya; Gerry Alfa Dito
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7608

Abstract

Modeling heterogeneity in panel data requires solving a complex combinatorial partition problem under structural constraints. Although clusterwise regression captures latent group structures with distinct parameters, determining the optimal partition remains computationally challenging due to the vast solution space and susceptibility to local minima. This study proposes a modified simulated annealing (SA) algorithm incorporating a Stochastic Residual Selection (SRS) mechanism, in which candidate units are selected from a high-residual subset rather than deterministically relocating only the unit with the largest residual. The stochastic candidate-size parameter was evaluated using m=1 and m=5, where m=1 represents deterministic selection of the largest residual unit, while m=5 randomly selects one unit from the five largest-residual units for clusterreassignment. The stochastic perturbation enhances global exploration and improves convergence stability in non-convex optimization landscape. Simulation experiments involving 200 individuals observed over three time periods demonstrate that the proposed SRS-SA outperforms standard SA, achieving an Adjusted Rand Index of approximately 0.95 at 1,000 iterations while producing lower Mean Absolute Bias and Mean Squared Error. An empirical application to improved sanitation data across districts and municipalities in Java, Indonesia, further confirms its effectiveness in identifying latent structural heterogeneity. These findings highlight the robustness and computational efficiency gained through stochastic diversification in metaheuristic optimization for constrained clusterwise panel modeling.
An Integrated RBV-KBV Conceptual Framework for Generative AI Adoption in Small Manufacturing Enterprises Ikhwan Arief; Alizar Hasan; Nilda Tri Putri; Hafiz Rahman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7622

Abstract

Small manufacturing enterprises remain economically important but continue to face recurring operational constraints in planning, scheduling, quality control, maintenance, and process-data use. Generative artificial intelligence offers increasingly accessible support for these bounded operational tasks, yet adoption remains uneven because many firms lack a coherent basis for linking digital opportunity to internal resources, organizational knowledge, and measurable operational improvement. This study develops a conceptual framework that integrates the resource-based view and the knowledge-based view to explain generative artificial intelligence adoption in small manufacturing enterprises. Using an evidence-grounded theory-development approach, the study builds a staged framework that separates foundational conditions, perceived operational AI opportunity, organizational translation mechanisms, and performance outcomes. The framework theorizes internal resources and knowledge assets as foundational antecedents, perceived generative artificial intelligence potential in operational functions as the adoption bridge, knowledge integration and dynamic capability as organizing mechanisms, and performance improvement as the downstream consequence. It further explains how conceptual clarity can support later empirical reduction without losing the richer logic needed for practical implementation. The study also clarifies how the framework can guide applied information-system design through data-readiness assessment, bounded decision-support use cases, human-in-the-loop verification, and operational KPI monitoring. The resulting architecture strengthens theoretical explanation and operational design logic for generative artificial intelligence adoption in constrained manufacturing environments, while preserving clear boundaries for subsequent validation and applied deployment.
Impact of Speckle Reduction Filters on Machine Learning-Based Detection of Polycystic Ovary Syndrome from Ovarian Ultrasound Images Fazrol Rozi; Syafrizal Sy; Adiwijaya; Admi Nazra; Primawati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7658

Abstract

Polycystic Ovary Syndrome (PCOS) is commonly assessed with ovarian ultrasonography, but speckle can conceal follicular margins and reduce the robustness of automated interpretation. Although automated PCOS studies increasingly employ machine learning, the contribution of conventional despeckling to subsequent segmentation and classification has not been examined consistently. This study compares five classical filters - Mean, Median, Lee, Frost, and Kuan - within an interpretable machine-learning pipeline for ovarian ultrasound analysis. From a public collection of 12,680 images, a balanced sample of 300 scans (150 PCOS and 150 non-PCOS) was selected. Two radiologists produced follicle annotations, and disagreements were resolved with a third expert to obtain consensus masks. Each filtered image was segmented by adaptive thresholding with morphological refinement, after which geometric and intensity descriptors were extracted. Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Logistic Regression (LR) were trained using a stratified 70/30 train-test split with cross-validated hyperparameter tuning. The Kuan-LR configuration yielded the strongest result, reaching 94.44% accuracy and an AUC of 0.98, together with the best edge-preservation score and segmentation agreement. The results indicate that preprocessing materially affects the reliability of an interpretable PCOS detection pipeline and provide quantitative guidance for selecting a speckle-reduction strategy before segmentation and classification.
Customer Churn Prediction in Waste Banks Using XGBoost and SMOTE Indri Rahmayuni; Rahmi Putri Kurnia; Yance Sonatha; Yulherniwati Yulherniwati; Afcha Arel Pratama
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7811

Abstract

Customer retention has become an important challenge in waste bank programs because declining member participation may reduce operational sustainability and weaken community-based waste management initiatives. However, churn prediction studies in non-commercial environmental programs such as waste banks remain limited. This study proposes a machine learning approach for customer churn prediction using operational transaction data from a waste bank managed by the Environmental Agency of Padang City, Indonesia. The dataset consisted of 34,188 transaction records representing 1,015 members collected between May 2024 and April 2026. Customer behavioral features were constructed from transaction history indicators, while class imbalance was handled using SMOTE and churn classification was performed using XGBoost under a leakage-aware customer-level train-test separation, ensuring a realistic evaluation on previously unseen members. Experimental results showed that the proposed model achieved an accuracy of 0.84, an F1-score of 0.73, and a ROC AUC value of 0.898. Feature analysis revealed that recency and transaction frequency were among the strongest predictors of churn behavior. The findings demonstrate the potential of machine learning to support participation monitoring and sustainability management in community-based waste bank systems.  
Predicting High-Risk Pregnancies Using Machine Learning Algorithms and Explainable AI Sari Puspita; Gusrino Yanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7071

Abstract

Pregnancies classified as high-risk play a significant role in increasing health complications and deaths among mothers and newborns, making early risk identification essential for timely clinical intervention. Although machine learning has shown promising performance in predicting pregnancy risk, most existing studies rely on binary classification and provide limited model interpretability. This study proposes an interpretable multiclass machine learning framework that predicts pregnancy risk using maternal health records from primary healthcare facilities. A total of 2,553 maternal medical records collected from five community health centers in Koto Tangah District, Padang City, Indonesia, were analyzed. The proposed framework integrates data preprocessing, StandardScaler, Synthetic Minority Over-sampling Technique (SMOTE), GridSearchCV-based hyperparameter optimization, and Shapley Additive exPlanations (SHAP). Four machine learning algorithms, which are Logistic Regression, Decision Tree, Support Vector Machine, and Random Forest, were systematically assessed with the application of Accuracy, Precision, Recall, F1-score, and ROC-AUC. Of the machine learning models considered, Random Forest performed best, achieving 97.26% accuracy, 90.44% precision, 98.06% recall, 93.65% F1-score, and 99.79% ROC-AUC. SHAP analysis identified Heart Rate, Blood Glucose, Diastolic Blood Pressure, and Systolic Blood Pressure as the most influential predictors, while also improving model transparency through feature contribution and interaction analysis. The results indicate that the suggested framework delivers precise and interpretable multiclass pregnancy risk prediction, demonstrating how it can assist with the early detection of pregnancy risks within primary care environments.
Transforming Assessment with Self-Correcting AI Pipelines: Towards Adaptive and Validated LMS Practices Sergiy Yevseyev
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7191

Abstract

The purpose of the study was to develop an effective approach to automated generation and validation of educational assessment materials in the context of distance learning using Large Language Models (LLM). While current research often overlooks the dynamic transformation of evaluation practices using sequential AI applications, this study addresses this gap by introducing a novel self-correcting AI pipeline that iteratively refines and validates questions based on structured feedback. As part of the study, an experimental methodology combined qualitative and quantitative methods, including computer modelling. Researchers conducted a comparative analysis of the results from three different LLMs: ChatGPT-4o, Google Gemini PRO 1.5, and Claude 3.5 Sonnet. Finally, the generated questions were validated based on pedagogical expediency, age compliance, and Bloom’s taxonomy. To test the effectiveness of the approach, three stages of research were conducted. In the first stage, it was found that only 23% of questions generated without updated prompts met the stated criteria. In the second stage, after introducing clarifications to the prompts, this indicator increased to 63%. The highest results were achieved in the third stage, where an iterative hint refinement model using structured feedback was implemented: Claude 3.5 Sonnet achieved 92% of valid questions with the minimum number of clarifications (16), ChatGPT-4o 80% (40 clarifications), and Gemini 72%, but with the highest number of corrections (108). The process included question generation, answer verification, external validation, and iterative correction. The findings showed that effective AI-based development of multi-level computer science questions requires high-quality language models, clear instructions, and automated verification of cognitive level. The proposed algorithm enables educational institutions, platform developers, and teachers to generate assessment materials adapted to students’ age and knowledge levels in distance or blended learning
Probabilistic Machine Learning Early Warning for Urban PM2.5 in SEA Cities Baharuddin Mide; Dhimas Tribuana; Usman Sattar; Dayanti Dayanti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i7199

Abstract

Air pollution, particularly fine particulate matter (PM₂.₅), poses a critical threat to public health in rapidly urbanizing regions. Reliable early-warning systems are essential for mitigating exposure risks, yet challenges remain in cities with heterogeneous sensor coverage and event frequency. This study aimed to develop and evaluate a probabilistic, portable across cities early-warning framework for PM₂.₅ exceedances in Southeast Asia, focusing on Jakarta, Singapore, and Bangkok. Using a staged experimental design (Exp-A through Exp-E), we integrated regression-based back-casts with classification-based exceedance alerts, applied variant selection across thresholds and horizons, and validated for operational readiness through model freezing, documentation, and online simulation. Results showed that Jakarta achieved near-perfect exceedance prediction up to 6-hour horizons (F1 ≈ 0.99), Singapore maintained strong performance at short horizons (F1 ≈ 0.91 at 2–3 hours), while Bangkok yielded moderate but actionable signals at very short horizons (F1 ≈ 0.62 at 1 hour). Regression components provided stable situational awareness, and online smoothing reduced false alarms by approximately 15–20% without degrading performance. The framework demonstrated that calibrated exceedance probabilities can serve as an effective basis for city-level air quality alerts, with reliability strongly influenced by data density and event prevalence. This work contributes a reproducible, transparent, and computationally efficient approach that bridges machine learning innovation with practical environmental management. The findings emphasize the importance of horizon-specific calibration and adaptive strategies, offering both theoretical insights and practical value for policymakers in urban air quality governance.
Comparative Study of Machine Learning Algorithms Using Bagging and XGBoost Techniques for Breast Cancer Classification Rully Pramudita; Dwi Ismiyana Putri; Bambang Kriswantara; Vina Zahrotun Nazah; Rahmat Budiarto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7216

Abstract

Machine learning (ML) has become an important data-driven approach for classification and prediction, including applications in medical diagnosis. Ensemble methods can improve classifier performance by combining complementary learning mechanisms. However, systematic evidence on the sequential use of Bagging and XGBoost across different classifier architectures remains limited. This study develops a staged ensemble framework in which five classifiers—Support Vector Machine (SVM), Neural Network (NN), Logistic Regression (LR), Decision Tree (DT), and K-Nearest Neighbours (KNN)—are first optimized through Bagging and subsequently enhanced using XGBoost. The experiments were conducted on the Breast Cancer Wisconsin (Diagnostic) dataset under a consistent 70:30 train–test protocol. Performance was assessed using accuracy, confusion matrices, ROC curves, and Area Under the Curve (AUC), while repeated experiments were used to examine statistical significance. The results show that the staged Bagging–XGBoost approach improves both predictive accuracy and class discrimination across the evaluated classifier types. Neural Network achieved the largest improvement, with mean accuracy increasing from 93.1% to 97.0% across repeated experiments. The findings demonstrate that the sequential framework can improve non-tree-based as well as tree-based classifiers, providing empirical evidence for broader use of staged ensemble integration in breast cancer classification.
Deep Sentiment Analysis of Halal Tourism: An Enhanced IndoBERT with Attention-Based and Coupling Latent Sampling on User-Generated Feedback Rio Andika Malik; Marta Riri Frimadani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7378

Abstract

Public sentiment analysis regarding government policies, particularly in niche sectors like Halal tourism, often faces significant computational challenges due to data scarcity and severe class imbalance. Traditional augmentation methods, such as Synthetic Minority Over-sampling Technique (SMOTE) applied to raw text or TF-IDF vectors, often degrade semantic integrity, while standard fine-tuning of pre-trained models like IndoBERT tends to bias predictions toward majority classes. To address these limitations, this study proposes IndoBERT-LSS (Latent Space Sampling), a novel decoupled two-stage deep learning framework. The first stage employs a Representation Learning approach integrating an Attention-Pooling mechanism with Supervised Contrastive Loss (SupCon) to enforce compact intra-class clustering in the embedding space. The second stage introduces a Latent Space Sampling strategy, where SMOTE is applied to the extracted high-dimensional embeddings rather than the raw text, followed by a final classification using a Multi-Layer Perceptron. Validated on a dataset of 1,051 textual responses regarding Halal tourism in Pariaman, Indonesia, the proposed model achieved an accuracy of 93.84% and a macro F1-score of 0.94. Notably, the model demonstrated exceptional robustness in identifying minority classes, achieving a 0.99 F1-score for neutral sentiments. These results conclude that decoupling representation learning from feature balancing in the latent space significantly enhances model performance on imbalanced short-text datasets compared to standard fine-tuning baselines.
Deephoax Image Detection based on Deep Learning using Convolutional Neural Network Architectures SY Yuliani; Nasywa Naura Aulia
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i7403

Abstract

The rapid growth of generative artificial intelligence has increased the spread of deephoax images, creating significant challenges for digital security, public trust, and the reliability of online information. This study evaluates the performance of MobileNet and Xception architectures for classifying facial images into fact and hoax categories while analyzing different feature representation levels within the Xception architecture through an ablation-based fine-tuning strategy. The proposed framework consists of data preparation, model architecture design, training procedures, and performance evaluation using confusion matrix analysis and metrics such as accuracy, precision, recall, and F1-score. Two publicly available datasets, DeepDetect2025 and FF-GenAI were utilized to evaluate the robustness of the proposed models. Experimental results show that Xception consistently outperformed MobileNet across all evaluation metrics, with the Middle Level Layer configuration achieving the best performance at 99.71% accuracy and F1-score. The findings indicate that intermediate feature representations are the most effective for capturing structural inconsistencies, texture irregularities, and synthetic blending artifacts commonly found in AI-generated facial images. In contrast, low-level representations were less discriminative, while highly abstract semantic representations slightly reduced sensitivity to localized manipulation artifacts. Overall, this study demonstrates the effectiveness of Xception-based feature refinement for deephoax image detection and contributes to AI-based approaches for digital content verification, cybersecurity, and visual misinformation mitigation.      

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