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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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Comparative Evaluation of Deep Learning Models for Real-Time Waste Classification Sari Dewi Budiwati; Guntur Prabawa Kusuma; Nishanth Shanmugam; Sujai Samraj; Bagas Catur Santoso; Ova Syahdira Pramondari; Fakhira Nur Aini
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.v10i4.7455

Abstract

Waste management has become a pressing global challenge due to rapid urbanization and population growth, leading to increased environmental pollution and resource depletion. Automating waste segregation using deep learning can significantly improve recycling efficiency and reduce manual labor. In this study, five deep learning models: YOLOv8, YOLOv5, MobileNetV2, Single Shot Multibox Detector (SSD), and Google’s pre-trained EfficientNet, were evaluated for real-time waste detection and classification. A dataset consisting of 15,150 images across 12 waste categories was used for training and testing, representing common household waste materials and evaluated using real-world objects such as water bottles, glass bottles, cans, and used face masks. Testing was conducted on both a high-performance workstation and a Raspberry Pi 4 edge device to assess detection accuracy, inference speed, and practical deployment feasibility. The results indicate that YOLOv8 achieved the highest average accuracy of 83% with reliable performance across diverse object types, whereas EfficientNet achieved moderate accuracy of approximately 60% with higher inference latency. Lightweight models such as MobileNetV2 and SSD were computationally efficient but exhibited lower accuracy, at 42% and 41%, respectively, particularly when handling irregular or overlapping objects. These findings demonstrate that YOLOv8 provides the most effective balance between accuracy and real-time performance, making it well suited for intelligent waste sorting systems.
Benchmarking Lightweight Machine Learning for Disaster Prediction: Accuracy, Latency, and Readiness Muhammad Amanulloh Mz; Oky Dwi Nurhayati; Jatmiko Endro Suseno
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.v10i4.7570

Abstract

Natural disaster early warning systems are often constrained by high computational latency, which impedes the timely dissemination of critical information. This study proposes a disaster impact prediction framework designed to optimize inference speed through a lightweight machine learning approach. Utilizing a comprehensive historical dataset from the Indonesian National Board for Disaster Management (BNPB) comprising 28,773 disaster records across 34 provinces from January 2018 to May 2024, this research evaluates the performance of XGBoost and LightGBM against a Gated Recurrent Unit (GRU) model for predicting infrastructure damage. SMOTE was applied exclusively during the training phase to address class imbalance without affecting inference speed. The results demonstrate that tree-based models significantly outperform GRU in both accuracy and speed. XGBoost achieved the lowest Mean Absolute Error (MAE of 1.1649) and the fastest inference latency (0.0026 ms per sample), followed by LightGBM (MAE of 1.4006, latency of 0.0064 ms), while GRU yielded a substantially higher error (MAE of 1.4189) and latency (0.2493 ms). Statistical validation via the Wilcoxon signed-rank test confirmed the significance of these performance differences (p < 0.001). Beyond technical benchmarking, these findings support the development of Intelligent Decision Support Systems for disaster mitigation, where organizational readiness and technology acceptance are critical for successful adoption. This study discusses practical implications for deploying lightweight models on resource-constrained edge computing devices such as Raspberry Pi 4, demonstrating sub-millisecond response speed, horizontal scalability, and affordability under $100 per node.
Cross-Dataset Evaluation of Boosting Models for Hypertension Prediction Bety Wulan Sari; Dewi Ayu Murtiningsih; Donni Prabowo; Yoga Pristyanto; Ika Nur Fajri; Ike Verawati
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.v10i4.7620

Abstract

Hypertension remains a significant global risk factor for cardiovascular disease and related mortality, necessitating reliable early risk prediction models. Although boosting algorithms have demonstrated strong performance in structured medical data, limited studies have examined their consistency across heterogeneous datasets. This study aims to evaluate the cross-dataset performance and stability of three boosting models, such as XGBoost, LightGBM, and CatBoost, for hypertension prediction under multiple train–test split ratios. Two independent structured datasets were analyzed using 60:40, 70:30, 80:20, and 90:10 splits. To identify the optimal hyperparameters, grid search was performed using repeated stratified 5-fold cross-validation with three repetitions. Model effectiveness was measured using the evaluation metrics of accuracy, precision, recall, F1-score, and AUC. Results show that Dataset 1 gained consistently high predictive performance (accuracy > 0.98; AUC ≈ 1.00), indicating strong and well-separated predictive signals, whereas Dataset 2 demonstrated substantially lower discriminative ability (accuracy ≈ 0.71–0.72; AUC ≈ 0.50), suggesting limited predictive structure. Across both datasets, CatBoost consistently obtained the highest accuracy, particularly at the 90:10 split ratio. These findings demonstrate that dataset characteristics critically determine model effectiveness and that among the evaluated boosting algorithms, CatBoost delivered the strongest overall predictive performance.
Multimodal Contextual Learning for Maize Disease Diagnosis: Fusing Visual and Environmental Data Hilton Kudzai Chironga; Ary Murti Muhammad; Erwin Susanto
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.v10i4.7671

Abstract

Maize Lethal Necrosis (MLN) and Maize Streak Virus (MSV) threaten food security in Zimbabwe, yet traditional image-based deep learning models lack adaptability to varying agro-environmental conditions. This study presents a multimodal contextual learning framework that fuses visual symptoms with environmental data to improve diagnostic accuracy and interpretability. A multimodal Convolutional Neural Network integrates leaf imagery with six environmental parameters (temperature, humidity, rainfall, soil moisture, leafhopper count, days after planting) using a late-fusion architecture with multi-task learning for simultaneous disease classification and five-stage severity estimation, enhanced by Gradient-weighted Class Activation Mapping (Grad-CAM) for visual explanation. A synthesized dataset (n=9,356) representative of Zimbabwean conditions was used. The multimodal approach achieved 94.3% accuracy versus 91.5% for image-only baselines (p<0.001, 95% CI), a 33.2% relative error reduction, with MSV-MLN confusion decreasing by 40.7% (113 to 67 misclassifications). Multi-task performance reached 94.5% for classification and 81.5% for severity estimation (F1=0.805). Grad-CAM analysis revealed environmental integration enhanced attention by 38.1% and localization by 45.2%, with leafhopper count showing the strongest correlation (r=0.62). Ablation studies confirmed that environmental features provided the largest accuracy gain (+3.71%, p<0.001). This work demonstrates that integrating environmental context with visual symptoms enhances diagnostic accuracy and model interpretability, establishing a foundational proof-of-concept for context-aware agricultural AI.
Temporal Semantic Flow Networks for Analyzing Topic Evolution in Educational Data Mining Firman Edi; Ambiyar; Waskito; Samsir
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.v10i4.7755

Abstract

This study aims to provide a comprehensive longitudinal analysis of the evolution of Educational Data Mining (EDM) research from 2014 to 2024, with a particular emphasis on learning analytics and student success prediction. We have developed and implemented the Temporal Semantic Flow Networks with Attention-based Topic Evolution (TSFN-ATE) methodology, which reconceptualizes the evolution of scientific discourse as continuous semantic flows. This framework integrates multi-scale temporal attention mechanisms and dynamic network representations to analyze Scopus-indexed keyword data from 436 EDM articles. The analysis identified significant transformations within the discourse of EDM, with a 333% increase in research intensity related to predictive modeling and an unprecedented 2,650% growth in the application of deep learning from 2014 to 2024. Deep learning attained the highest semantic flow score (0.94), signifying its successful integration across various EDM subfields, while machine learning emerged as the central methodological bridge, exhibiting the strongest network centrality (0.74). Multi-scale temporal attention analysis revealed differential patterns across time scales, with machine learning receiving the highest recent attention (0.85 at a 1-year scale), whereas learning analytics demonstrated sustained long-term influence (0.75 at a 5-year scale). Concept drift detection identified four major paradigm shifts, with the 2018-2019 deep learning integration representing the most significant conceptual disruption (semantic stability index 0.45). Collectively, these four identified paradigm shifts — from traditional machine learning through deep learning integration to the emergent ethical turn — reveal that EDM is not simply accumulating methods but undergoing continuous conceptual reorganisation; understanding and anticipating these shifts is therefore essential for researchers, institutions, and policymakers seeking to align educational technology development with evolving scientific and societal priorities.

Page 115 of 115 | Total Record : 1145

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