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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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Comparative Analysis of CNN Architectures and Optimizers for ALL Classification Sugiyarto Surono; Fanita Damayanti; Frenky Riski Gilang Pratama; Attarik Mohammad; Maretta Mia Audina; Marfungah Wati
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.v10i7506

Abstract

Deep learning has become an important approach for medical image analysis, particularly for automated image classification. Among its various architectures, Convolutional Neural Networks (CNNs) are frequently employed to extract and distinguish visual patterns from medical images. However, the performance of a CNN may vary depending on the optimization algorithm used during the training process. This study investigates the effect of four optimization methods on different CNN architectures for the classification of Peripheral Blood Smear (PBS) images. A publicly available Kaggle dataset comprising 3,242 images and four diagnostic categories was used in the experiments. Six architectures, namely ResNet50V2, Xception, EfficientNetB2, EfficientNetB4, EfficientNetV2-S, and EfficientNetV2-M, were evaluated in combination with Adam, AdamW, NAdam, and RMSprop. Before training, the images underwent resizing, normalization, and augmentation procedures. The dataset was subsequently partitioned into training, validation, and test subsets using proportions of 80%, 10%, and 10%, respectively. Classification performance was analyzed using accuracy, loss, and confusion matrix-based evaluation. Among all tested combinations, Xception with RMSprop produced the best result, obtaining 99.69% accuracy and a loss value of 0.0194. Conversely, the lowest result was observed for ResNet50V2 with RMSprop, which achieved 93.85% accuracy. The experimental findings indicate that the optimizer can substantially influence CNN classification performance, and the effectiveness of an optimization method is dependent on the architecture with which it is combined.
Enhancing Software Defect Prediction Performance using NR-Clustering SMOTE to Address Class Imbalance Hairani Hairani; Muhamad Masjun Efendi; Gede Yogi Pratama; Rahayun Amrullah Husaini; M.Khaerul Ihsan
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.v10i7558

Abstract

Software defect detection is important to prevent system failures and increased maintenance costs. However, the complexity of modern software makes manual testing inefficient, so machine learning approaches are used. The main challenge of this approach is data imbalance, where defective cases are far fewer, causing the model to overlook the minority class and reducing detection capability, even though accuracy appears high. This study aims to address class imbalance in software defect detection by applying the NR-Clustering SMOTE method to improve machine learning performance—the classification methods using Random Forest. NR-Clustering SMOTE not only oversamples the minority class but also incorporates a noise-reduction mechanism to remove minority data that may degrade classification performance. The results show that NR-Clustering SMOTE improves the performance of Random Forest compared with the original data, SMOTE, and NR-Modified SMOTE across all evaluation metrics, namely accuracy, recall, and F1-score. These findings indicate that integrating noise reduction and SMOTE-based data balancing using Manhattan distance within each cluster produces a more representative data distribution, thereby improving the model’s ability to classify software defect cases more accurately. Therefore, this study confirms that NR-Clustering SMOTE effectively improves Random Forest performance for software defect detection compared with existing approaches.
An Improved Spectral Residual Algorithm for Sparrow Pest Detection in Rice Field CCTV Video Raja Ayu Mahessya; Yuhandri Yuhandri; Rini Sovia
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.v10i4.7775

Abstract

Agricultural pest surveillance in open field environments presents a persistent challenge for food security, particularly when early detection is limited by the inadequacy of traditional monitoring methods. Existing Spectral Residual (SR) algorithms, while capable of saliency-based object detection, exhibit notable limitations in adapting to dynamic agricultural conditions resulting in degraded segmentation quality and elevated false-positive rates. This study proposes an improved Spectral Residual algorithm that independently processes Red, Green, and Blue (RGB) channels via Fast Fourier Transform (FFT), preserving richer spectral information that is otherwise lost in conventional grayscale-based approaches. The proposed pipeline integrates spatial saliency maps with temporal motion cues extracted through frame-difference analysis, suppressing static background noise while reinforcing responses in regions exhibiting genuine movement. Adaptive binarization and Support Vector Machine (SVM) classification are further incorporated to strengthen object segmentation robustness across diverse lighting and background conditions. The model was evaluated on 136 frames extracted from 2MP Hikvision CCTV footage installed at rice field sites, benchmarked against the classical Spectral Residual baseline using accuracy, precision, recall, and F1-score. The proposed method achieved an accuracy of 98.15%, precision of 91.67%, recall of 98.98%, and F1-score of 95.18% — representing substantial improvements over the baseline (74.00%, 70.00%, 65.00%, and 69.00%, respectively). These results demonstrate that multi-channel spectral processing combined with motion-aware saliency yields a robust and computationally efficient saliency framework whose low algorithmic complexity makes it a strong candidate for real-time sparrow pest detection in CCTV-based agricultural monitoring systems.
Composite Loss-Enhanced DeepLabV3+ for Crack Segmentation in Photogrammetric Mining Imagery Yesri Elva; Iskandar Fitri; Syafri Arlis
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.v10i4.7807

Abstract

Crack detection in photogrammetric mining imagery remains challenging due to severe class imbalance, thin and irregular crack morphology, and complex soil textures that hinder accurate boundary delineation. This study proposes a DeepLabV3+ semantic segmentation model with a ResNet-50 backbone trained using a Composite Loss that integrates Focal Tversky Loss, Weighted Cross-Entropy Loss, and Boundary Loss into a unified multi-objective framework. The dataset comprised 425 photogrammetric crack images acquired from open-pit mining sites, divided into 380 training and 45 test images. Data augmentation, including horizontal flip, vertical flip, channel shuffle, and random brightness and contrast adjustment, was applied to improve model generalization under varying image conditions. The proposed configuration was evaluated against Default Loss, Binary Cross-Entropy Loss, and Weighted Cross-Entropy Loss. The model achieved a Global Accuracy of 0.9808, Mean Intersection over Union of 0.7890, and Mean Boundary F1-Score of 0.8642, outperforming all baseline configurations. Boundary fidelity showed the largest improvement, with a Mean BFScore gain of 27.3% over Default Loss, demonstrating the effectiveness of Boundary Loss for precise crack contour delineation. The trained model was also deployed in a GUI-based system supporting automated crack analysis and PDF report generation for practical field application.

Page 117 of 117 | Total Record : 1164

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