cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Yuhefizar
Contact Email
jurnal.resti@gmail.com
Phone
+628126777956
Journal Mail Official
ephi.lintau@gmail.com
Editorial Address
Politeknik Negeri Padang, Kampus Limau Manis, Padang, Indonesia.
Location
,
INDONESIA
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
 110   111   112   113   114 
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): June 2026
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): June 2026
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): June 2026
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.
Negative Content Detection Model to Classify Text of a Website Using Machine Learning Method Budi Sunaryo; Muhammad Ilhamdi Rusydi; Ariadi Hazmi; Oluwarotimi Williams Samuel
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7119

Abstract

Harmful online content can negatively influence users and create social risks. This study develops a machine learning model to detect harmful website content using Naïve Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The analysis focuses on website text extracted from the HTML Document Object Model (DOM). Four classes were used: gambling, pornography, phishing, and whitelist content. The dataset consisted of 2911 URLs collected from the UT1 Blacklist repository. Text preprocessing and TF-IDF feature extraction with unigram and bigram representations produced 71,967 tokens. Experimental results show that SVM achieved the best performance with 90.50% accuracy on 2821 active URLs. A real-time Flask-based web application was also developed to classify URLs from user input. The findings demonstrate that combining NLP and machine learning provides an effective and practical solution for harmful website content detection.
Improving Remote Sensing Classification with Ensemble Learning and XAI-based Interpretability Radius Tanone; Li-Hua Li; Ramli Ahmad; Alok Kumar Sharma; Agus Cahyo Nugroho
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7121

Abstract

The challenge of classifying remote sensing images primarily stems from the unclear image quality produced by satellites during data collection. Relying on multiple models to recognize remote sensing images can sometimes lead to suboptimal performance. To address this issue, this study integrates transfer learning and ensemble learning to enhance the accuracy of single-model classification. This research employed pre-trained models, including EfficientNetB7, Vision Transformer, and ConvNeXt, and evaluated them on benchmark datasets RSI-CB256 and NWPU RESISC45. The results demonstrate that ensemble learning significantly boosts model performance beyond that of individual models. For the RSI-CB256 dataset, the average and geometric mean ensemble methods achieved the highest accuracy of 0.9985. For another dataset, namely NWPU RESISC45, the geometric mean ensemble achieved the best accuracy of 0.9720. Furthermore, this study explores model transparency and interpretability through eXplainable Artificial Intelligence (XAI) techniques. In addition to transparency, this study uses Grad-CAM to identify critical regions influencing the model’s predictions in remote sensing image classification tasks.
Predicting Mandarin Vocabulary Learning Outcomes Using Data-Driven Machine Learning Yan Qin; Joseph Teguh Santoso; Agus Wibowo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7143

Abstract

The rapid expansion of language learning in higher education highlights the need for data-driven approaches to monitor student progress and provide timely instructional support. This study aims to develop a predictive framework for Mandarin vocabulary mastery using supervised machine learning. A dataset of 147 undergraduate students was analyzed, incorporating study hours, number of exercises, pre-test scores, and attendance as predictors of learning outcomes. Logistic Regression, Random Forest, and XGBoost algorithms were trained and evaluated, with XGBoost achieving the highest performance (accuracy 88%, F1-score 0.88), demonstrating its superior ability to capture complex learning patterns. Analysis of feature importance revealed that pre-test scores and the number of exercises were the most influential predictors of student success. Furthermore, a prototype graphical user interface (GUI) was developed to visualize predictions in real time, enabling instructors to identify at-risk students and adjust teaching strategies accordingly. The novelty of this study lies in integrating predictive analytics with pedagogical applications, bridging machine learning and educational practice. Beyond its technical contributions, this research provides practical insights for higher education stakeholders, showing how predictive models can support early intervention, enhance curriculum design, and promote evidence-based decision-making in Mandarin vocabulary instruction.
Spell Correction for the Minangkabau Language Using BERT-Based Embeddings Dewi Soyusiawaty; Abdul Fadlil; Sunardi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7182

Abstract

Spell checking is an essential component of natural language processing, as it directly influences applications such as sentiment analysis, text classification, and machine translation. Developing a reliable system for low-resource languages like Minangkabau is challenging due to frequent spelling variations and limited annotated data. This study proposes a contextual spell correction model using pre-trained IndoBERT and multilingual BERT (mBERT) embeddings applied without additional training. The method masks misspelled words, extracts the contextual embedding of the [MASK] token, and compares it with candidate embeddings generated through dictionary filtering and Levenshtein Distance. Evaluation was conducted on the Spell Error Corpus for Minangkabau Language (SPEML), which includes insertion errors, deletion errors, substitution errors, transposition errors, punctuation errors, real-word errors, and loanword errors. Results show that mBERT consistently outperformed IndoBERT, achieving an average F1-score of 0.83 compared to 0.75. Statistical validation using paired t-test and Wilcoxon signed-rank test further confirmed that the performance difference between the two models was significant. Both models reached perfect scores (1.0) in real-word and loanword categories, and strong results in insertion_medium (0.97 for mBERT and 0.95 for IndoBERT). The lowest performance occurred in deletion_short (0.52 for IndoBERT) and long words cases (0.57 for mBERT). In addition, a small-scale external validation using 100 Twitter/X sentences was conducted to assess the applicability of the proposed method to real-world social media text. Overall, the findings confirm the effectiveness of contextual embeddings for Minangkabau spelling correction while highlighting challenges in long misspelled words, deletion errors, and informal real-world text.
Convergence and Empirical Performance of Tanh-Based Adaptive Particle Swarm Optimization Joko Riyono; Aina Latifa Riyana Putri; Sofia Debi Puspa; Supriyadi Supriyadi; Christina Eni Pujiastuti; Fayza Nayla Riyana Putri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7247

Abstract

Particle Swarm Optimization (PSO) is a widely used population-based optimization method but faces challenges in premature convergence, leading to suboptimal solutions. To address this issue, this study proposes a Tanh-Based Acceleration Coefficient PSO (TB-PSO), where the acceleration coefficients are modified using the hyperbolic tangent (tanh) function. The smooth and continuous behavior of tanh enables gradual coefficient updates, limits excessive particle velocities, and maintains swarm diversity, thereby improving convergence stability and balancing exploration and exploitation. The convergence theorem analysis confirms that TB-PSO meets stability criteria before being evaluated on unimodal and multimodal benchmark functions in 10 and 30 dimensions. Its performance is compared against several PSO variants, including TVAC-PSO, SCAC-PSO, NDAC-PSO, and SAC-PSO. In the 10-dimensional experiments, TB-PSO achieves the best overall final ranking based on the average and standard deviation of best solution, ranking first for functions f₃ and f₅, second for f₂ with only a marginal difference from the best-performing method, and remaining competitive for f₁ and f₄. These results indicate superior solution quality and stable convergence. For the 30-dimensional benchmark functions, TB-PSO ranks first for f₂, second for f₅, and third for f₁, f₃, and f₄ based on the same evaluation criteria. Although its ranking decreases compared to the 10-dimensional case, TB-PSO remains competitive, reflecting the increased complexity of high-dimensional optimization problems. Overall, the results demonstrate that the tanh-based acceleration coefficient modification effectively enhances PSO performance, particularly in lower-dimensional search spaces, while maintaining robustness in higher-dimensional scenarios.
Fuzzy-IOWA-Based Group Decision Support System (GDSS) for Interpreting DASS Scores with Dynamic Expert Weighting Wiharto; Eka P. Meravigliosi; Umi Salamah; Esti Suryani; Vihi Atina; Pradityo Utomo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7340

Abstract

This research develops a Group Decision Support System (GDSS) to address subjectivity and ambiguity in the interpretation of Depression Anxiety Stress Scales (DASS-21) scores, particularly in cases of symptom overlap across dimensions. The system introduces a structured weighting mechanism in which the influence of each expert is determined based on objective criteria, including clinical experience, education level, and academic contributions. The methodology applies Simple Additive Weighting (SAW) to quantify the relative importance of five clinical experts, resulting in Expert 2 (27.66%) and Expert 1 (27.36%) having the highest influence within the group. These weights are then incorporated into a Fuzzy-Induced Ordered Weighted Averaging (Fuzzy-IOWA) framework to aggregate expert judgments into a unified consensus model. The results indicate that the proposed approach is able to produce a consistent interpretation structure, with a tendency toward the Anxiety dimension in cases of overlapping symptoms. By integrating expert consensus with patient self-report scores, the system generates a structured interpretation profile of DASS-21 responses. The proposed GDSS provides a systematic and transparent aggregation framework that reflects expert reasoning. However, it is intended as a methodological support tool rather than a substitute for clinical diagnosis. The novelty of this work lies in the integration of SAW-based objective expert capability quantification with Fuzzy-IOWA aggregation and QGDD-based consensus characterization, forming a transparent and mathematically accountable GDSS pipeline that has not been previously applied in the context of DASS-21 interpretation.
Metaheuristic-based Clustering Algorithms with Principal Coordinate Analysis for Shoe Market Segmentation Ridho Ananda; Samuel Sinaga; Budi Pratikno; Nur Afrina Huda Zulkainain; Tri A. Sundara
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7369

Abstract

Micro, Small, and Medium Enterprises (MSMEs) are vital to Indonesia's economy, yet many struggle with ineffective marketing strategies, as seen in Bintang Sepatu Purwokerto, an MSME shoemaker facing stagnant sales. To overcome this, this study proposed a metaheuristic-based clustering (MBC) algorithm combined with principal coordinate analysis (PCoA) for optimal customer segmentation. The developed algorithm successfully overcomes the limitations of Kmeans in mixed datasets, containing categorical and numeric data. Furthermore, the procedure for updating centroids in Kmeans that risks falling into a local optimum is also solved. In this study, six MBC algorithms were developed based on six state-of-the-art metaheuristic optimizations utilized. Then, the developed MBC algorithms are compared with benchmark algorithms, namely Kmeans and KmeansQLDE, based on the near-optimal clustering obtained, t-test, and required running time. The comparison results show that the MBC clustering using gray wolf optimization (GWOClustering) outperforms the benchmark algorithms, achieving an average Silhouette score of 0.7491. In addition, this algorithm significantly outperforms Kmeans based on t-test conducted and results in relatively low runtime. The GWOClustering simulation yielded four near-optimal clusters in the customer segmentation of Bintang Shoes Purwokerto MSME. The analysis of each cluster's characteristics indicates the need for distinct marketing strategies for this MSME. Marketing based on offline purchasing services, store conditions, and product layout is appropriate for consumers in Cluster 1. Meanwhile, digital marketing with attractive, informative graphic content is suitable for consumers in Clusters 2 and 4. Furthermore, strategies with competitive pricing, discounts, or bundling strategies are appropriate for Cluster 3.

Page 112 of 115 | Total Record : 1145

 110   111   112   113   114 

Filter by Year

2017 2026


Reset
Filter By Issues
All Issue Vol 10 No 4 (2026): August 2026 (in progress) Vol 10 No 3 (2026): June 2026 Vol 10 No 2 (2026): April 2026 Vol 10 No 1 (2026): February 2026 Vol 9 No 6 (2025): December 2025 Vol 9 No 5 (2025): October 2025 Vol 9 No 4 (2025): August 2025 Vol 9 No 3 (2025): June 2025 Vol 9 No 2 (2025): April 2025 Vol 9 No 1 (2025): February 2025 Vol 8 No 6 (2024): December 2024 Vol 8 No 5 (2024): October 2024 Vol 8 No 4 (2024): August 2024 Vol 8 No 3 (2024): June 2024 Vol 8 No 2 (2024): April 2024 Vol 8 No 1 (2024): February 2024 Vol 7 No 6 (2023): December 2023 Vol 7 No 5 (2023): October 2023 Vol 7 No 4 (2023): August 2023 Vol 7 No 3 (2023): Juni 2023 Vol 7 No 2 (2023): April 2023 Vol 7 No 1 (2023): February 2023 Vol 6 No 6 (2022): Desember 2022 Vol 6 No 5 (2022): Oktober 2022 Vol 6 No 4 (2022): Agustus 2022 Vol 6 No 3 (2022): Juni 2022 Vol 6 No 2 (2022): April 2022 Vol 6 No 1 (2022): Februari 2022 Vol 5 No 6 (2021): Desember 2021 Vol 5 No 5 (2021): Oktober2021 Vol 5 No 4 (2021): Agustus 2021 Vol 5 No 3 (2021): Juni 2021 Vol 5 No 2 (2021): April 2021 Vol 5 No 1 (2021): Februari 2021 Vol 4 No 6 (2020): Desember 2020 Vol 4 No 5 (2020): Oktober 2020 Vol 4 No 4 (2020): Agustus 2020 Vol 4 No 3 (2020): Juni 2020 Vol 4 No 2 (2020): April 2020 Vol 4 No 1 (2020): Februari 2020 Vol 3 No 3 (2019): Desember 2019 Vol 3 No 2 (2019): Agustus 2019 Vol 3 No 1 (2019): April 2019 Vol 2 No 3 (2018): Desember 2018 Vol 2 No 2 (2018): Agustus 2018 Vol 2 No 1 (2018): April 2018 Vol 1 No 3 (2017): Desember 2017 Vol 1 No 2 (2017): Agustus 2017 Vol 1 No 1 (2017): April 2017 More Issue