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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 1,185 Documents
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Quantifying Target Leakage in SEER Breast Cancer Mortality Prediction Sestri Novia Rizki; Vani Maharani Nasution; Eko Amri Jaya; Verra Budhi Lestari; Elfina Maulid
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 5 (2026): October 2026 (In progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i5.8105

Abstract

Recent machine learning studies on the SEER Breast Cancer dataset report 95–98% accuracy (2022–2025), but their methods are rarely examined. Because the dataset is imbalanced (84.7% Alive), accuracy alone is a poor discrimination metric — a trivial always-Alive classifier scores near 85% — so this audit quantifies leakage inflation on ROC-AUC, computed identically across 12 models. We measure how much performance stems from target leakage via Survival Months, which encodes retrospective survival time unavailable at prediction. Undetected, such leakage could lead clinicians to overtrust a model's risk estimates, risking inappropriate care decisions. We train 12 models under three conditions: a) naïve, with Survival Months and a non-stratified split; b) leakage-free with stratified evaluation; and c) fixed-horizon (36- and 60-month) binary mortality, excluding censored cases. Under Condition A, the best ROC-AUC is 0.889 (accuracy 0.899). Removing leakage yields a consistent AUC ceiling of 0.74–0.77 across all 12 models — an inflation of +0.07 to +0.20 AUC points. SHAP shows Survival Months dominates feature importance by 3.0–4.3× under leakage; imbalance-handling methods (BorderlineSMOTE, class weighting) raise minority-class F1 but lower mean Brier Skill Score to −0.585 and −0.597. The contribution is the audit methodology itself — a reusable protocol for detecting leakage-driven inflation in registry-based clinical prediction.
A Data-Quality-Aware Machine Learning Framework for Magnitude Homogenization in Indonesia's Earthquake Catalog Desi Ramayanti; Bias Yulisa Geni; Imam Mulya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 5 (2026): October 2026 (In progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i5.8137

Abstract

Indonesia’s national seismological agency, BMKG, reports earthquake magnitudes under ten distinct, non-interchangeable scale labels in its public catalog (2020–2025, 65,788 events), with no published conversion to a common Mw-equivalent scale. Existing machine learning studies using this catalog have assumed a homogeneous magnitude scale, and instrument-quality metadata in the catalog has never been used as a covariate for magnitude-conversion bias or uncertainty. This study introduces a powerful hybrid magnitude-homogenization framework. For events with a native moment-magnitude anchor (the body-wave family), we perform self-supervised internal calibration and tie it to the global Scordilis relation. For events without such an anchor, the local/regional family, we apply a literature-based conversion using the Egyptian National Seismic Network formula. A quality-aware machine learning model is then trained to estimate homogenized magnitude with calibrated uncertainty, conditioned on instrument-quality features. The validation process uncovers and quantifies a selection bias of approximately 1.8 to 2.1 magnitude units in BMKG’s internal moment-magnitude reporting, reflecting that natively Mw-labeled events are a non-representative, significance-selected subsample rather than ground truth. The quality-aware model reduces point-prediction error (RMSE) by 50 to 70% and achieves better-calibrated 80% prediction intervals than a baseline without quality features. This combination of a practical homogenization solution and an explicit diagnostic framework is intended to be of direct use not only to BMKG and Indonesian seismology, but to any national seismological agency maintaining a similarly heterogeneous magnitude catalog without paired, same-event, multi-scale readings.
HyRes-Boost: Hybrid Resampling for Sensitivity–False-Alarm Trade-offs in Network Intrusion Detection Achmad Affandi; Eko Setijadi; Endroyono Endroyono; Sri Rahayu; Prasetiyono Hari Mukti; Suwadi Suwadi; Fransiska Sisilia Mukti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 5 (2026): October 2026 (In progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i5.7579

Abstract

Class imbalance in network intrusion detection systems (NIDS) creates a trade-off between intrusion sensitivity and false-alarm control. While hybrid resampling methods often improve minority class representation, their effectiveness varies depending on imbalance severity and operational needs. This study presents HyRes-Boost, a Generate-then-Clean hybrid resampling framework that integrates Adaptive Synthetic Sampling (ADASYN), Edited Nearest Neighbours (ENN), and XGBoost to examine how post-generation editing affects the sensitivity and false-alarm balance. HyRes-Boost is evaluated on UNSW-NB15, CICIDS2018, and CICIoT2023 using a leakage-safe protocol with predictive metrics, operational indicators, asymmetric error costs, and fold-level statistical analysis. Results indicate that HyRes-Boost produces context-dependent changes in operating points rather than universal improvements. In the attack-minority UNSW-NB15 scenario, HyRes-Boost achieves a recall of 99.74%, increasing intrusion sensitivity but also raising the false-alarm rate. In CICIoT2023, where benign traffic is the minority class, HyRes-Boost lowers the false alarm rate to 2.23% from 13.72% in the unbalanced baseline, though attack recall drops to 98.75%. For the highly separable CICIDS2018 dataset, additional resampling yields minimal improvement, with the F1-score remaining near 99.90%. These results suggest that hybrid resampling should not be evaluated solely by aggregate accuracy or ranking. The suitability of HyRes-Boost depends on class prevalence, boundary ambiguity, and the relative costs of false positives and false negatives.
Deterministic Texture-Adaptive Steganography for IoT Edge: Achieving Optimal Rate-Distortion With O(N) Complexity Saepudin Nirwan; Naufal Fachrudin Nirwan; Agus Perdana Windarto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 5 (2026): October 2026 (In progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i5.7582

Abstract

The rapid expansion of Internet of Things (IoT) edge systems has increased the demand for image steganography methods that provide strong security, high visual quality, and low computational overhead under resource-constrained environments. Existing adaptive steganography techniques, such as HUGO, WOW, and S-UNIWARD, achieve high embedding performance but generally rely on computationally intensive optimization procedures that limit their applicability to real-time IoT edge devices. This study addresses this limitation by proposing a Deterministic Texture-Adaptive Steganography framework based on a Texture Complexity Analyzer (TCA) integrated with Syndrome-Trellis Codes (STC). The TCA module generates a deterministic pixel-wise distortion cost map using second-order directional filtering, while the STC encoder performs minimum-distortion message embedding through trellis-based optimization with linear computational complexity. The proposed framework was evaluated on 40,000 images from the LIRMM CroppedBOSSbase dataset using five-fold stratified cross-validation. Experimental results demonstrate a mean PSNR of 59.63 dB, an SSIM of 0.9996, and a detection error of 0.50 against SR-Net steganalysis, indicating high visual imperceptibility and strong statistical security. The framework also achieved an average execution time of 80 ms per 256 × 256 image with an estimated energy consumption of 0.28 J, substantially reducing computational cost compared with iterative baseline methods. These findings demonstrate that the proposed deterministic framework provides an efficient balance between security, imperceptibility, and computational efficiency, making it well suited for practical deployment in resource-constrained IoT edge environments where predictable execution time and energy efficiency are essential.
Hybrid DenseNet201-ViT Architecture for Robust Solid Waste Classification Lailia Rahmawati; Catur Supriyanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 5 (2026): October 2026 (In progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i5.7669

Abstract

Automatic sorting of solid waste is difficult because discarded items are often badly deformed and some material classes, such as transparent plastic and glass, look almost identical. Conventional Convolutional Neural Networks (CNNs) mainly learn local patterns; as a result, they tend to overlook global context and to overfit when only a small and unevenly distributed dataset is available. This study develops a hybrid deep learning model that pairs the dense local feature learning of DenseNet201 with the global context modeling of a Vision Transformer (ViT) in order to make waste classification more robust. An ImageNet-pretrained DenseNet201 serves as the backbone, and its high-level feature maps are converted into a sequence of patch tokens that is processed by a custom ViT encoder. Experiments were conducted on a public garbage image dataset with six unevenly represented classes, namely cardboard, glass, metal, paper, plastic, and trash. On-the-fly data augmentation and early stopping were employed during training to limit overfitting. The hybrid model reached an overall accuracy of 87% and a macro F1-score of 0.85, exceeding the CNN baselines trained under identical settings. It was especially accurate on fibrous materials, with precision values of 0.95 for cardboard and 0.89 for paper. These results indicate that coupling self-attention with densely connected convolutional layers alleviates the main weaknesses of pure CNNs and provides an accurate, interpretable, and computationally efficient basis for practical automated waste sorting systems.

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