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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 1,113 Documents
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K-Nearest Neighbor Performance Optimization for Multiclass Imbalance of Intrusion Detection Data Using SMOTE and Distance Variation-Based Parameter Tuning Hairani Hairani; Christopher Michael Lauw; Sri Farida Utami; Afrig Aminuddin; Abu Tholib
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): Juni 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7489

Abstract

The increasing use of computer networks and internet-based services has made cybersecurity threats more complex. Intrusion Detection Systems (IDS) play a crucial role in identifying network attacks; however, conventional signature- or rule-based approaches are limited in handling novel attacks and dynamically changing attack patterns. Therefore, machine learning approaches are applied to enhance the adaptive capabilities of IDS. Nevertheless, the use of machine learning in IDS still faces a major challenge: data imbalance, where normal traffic significantly outweighs attack traffic. This condition biases models toward the majority class, leading to suboptimal detection of minority attacks. Based on this issue, this study aims to improve the performance of the K-Nearest Neighbor (KNN) method in network attack detection by applying the Synthetic Minority Over-sampling Technique (SMOTE) and parameter tuning. The study employs KNN with parameter tuning and SMOTE to address multiclass data imbalance in network attack detection. Parameter tuning is conducted to determine the optimal value of k and distance functions, including Euclidean, Manhattan, and Cosine Similarity. The results show that KNN with k = 3 and Manhattan distance on SMOTE-balanced data achieves the highest accuracy of 96.51%, outperforming Euclidean and Cosine Similarity distances. These findings conclude that applying SMOTE and appropriately selecting k and distance metrics significantly improve KNN performance in network attack detection and increase overall detection accuracy.
Resolving Visual Ambiguity in Wood Grain Classification Using InceptionV3 and Label Smoothing Rianto Rianto; Sulistyo Dwi Sancoko; Eko Setyo Humanika
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): Juni 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7599

Abstract

Wood type classification in the timber industry is frequently hindered by the high visual similarity of grain patterns, a structural challenge that is particularly pronounced with small-scale datasets. This study systematically resolves this fine-grained visual ambiguity by developing a rigorously optimized InceptionV3 framework. Initial baseline evaluations conducted on a dataset of 800 images encompassing four wood species, namely Pterospermum javanicum (Bayur), Magnolia champaca (Cempaka), Tectona grandis (Jati), and Melia azedarach (Mindi), revealed that a standard ResNet50 architecture experienced a severe performance degradation, generating a random guess accuracy of merely 25 percent. Addressing this severe architectural inadequacy, the proposed InceptionV3 model integrates highly targeted spatial augmentations, a Dropout rate of 0.6, and L2 regularization. Furthermore, the optimization strategy deploys Stochastic Gradient Descent with Nesterov momentum and Label Smoothing to explicitly mitigate intra-class visual similarities. Consequently, the proposed framework achieved a robust overall accuracy of 89 percent, surpassing the baseline by a substantial 64 percent, alongside a macro-averaged F1 Score of 0.89. These empirical findings substantiate that specific architectural fine-tuning and advanced stochastic regularization are highly essential for capturing subtle wood-grain patterns, thereby offering a highly reliable, automated quality-control solution for the timber industry.
Classification of Pestalotiopsis sp. Leaf Fall Disease Severity in Rubber Plants using UAV Multispectral Vegetation Indices and 1-D Convolutional Neural Networks Solikin; Yeni Herdiyeni; Annisa; Lilik Budi Prasetyo; Tri Rapani Febbiyanti; Imas Sukaesih Sitanggang; Sri Nurdiati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): Juni 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7601

Abstract

Leaf-fall disease caused by Pestalotiopsis sp. is a major threat to rubber (Hevea brasiliensis) plantations because it suppresses photosynthetic activity, accelerates defoliation, and reduces latex productivity. In operational practice, severity assessment is still dominated by visual field inspection, which is subjective, time-consuming, costly, and difficult to standardize across large plantation areas. This study develops a disease severity classification model for Pestalotiopsis sp. using a Convolutional Neural Network (CNN) based on vegetation-index features derived from UAV multispectral imagery. The model classifies disease severity into four levels: L1 (Light Infection), L2 (Moderate Infection), L3 (Severe Infection), and L4 (Very Severe Infection). To represent temporal and biological variability in disease expression, multispectral data were collected from multiple rubber clones over two observation periods. Feature construction focused on NDRE, LCI, CI, NDVI_NDRE_Interaction, and GCI_Ratio, which capture chlorophyll-related and canopy condition responses to infection. Because severity classes were imbalanced, the Synthetic Minority Over-sampling Technique (SMOTE) was applied before model training. A one-dimensional CNN was then trained to learn nonlinear patterns among index-based predictors for multilevel severity classification. Hyperparameter tuning improved overall accuracy from 85.30% to 90.00%. Class-wise F1-scores changed from 0.91 to 0.94 (L1), 0.83 to 0.84 (L2), 0.75 to 0.88 (L3), and 0.97 to 0.84 (L4), with the largest improvement in L3 recall (0.67 to 0.94). These results indicate that the selected vegetation indices and interaction terms are informative predictors for objective and scalable disease severity classification under heterogeneous plantation conditions.

Page 112 of 112 | Total Record : 1113

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