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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 1,046 Documents
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Application of YOLOv8 Algorithm for Coral Reef Disease Detection as an Effort to Prevent Marine Habitat Damage in Batam Rifa'atul Mahmudah Burhan; Refli Noviardi; M Abrar Masril; Firmansyah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6062

Abstract

Research in 2019 in Batam City showed that out of 19 coral reef fisheries support facilities, 16 were declared not good. Coral reef damage increased from 36.28% to 39.44%. This is due to the threat of coral reef damage due to international shipping lane areas, human activities such as destructive fishing, pollution, sedimentation, and global warming. These threats can cause coral diseases such as black band disease (BBD), brown band disease (BrB), Bleaching Coral, and yellow band disease (YBD). The Underwater Photo Transect (UPT) method collects data in the field in the form of underwater photos and analyzes them to obtain quantitative data. This method has a weakness, namely the low level of accuracy in detecting coral reef diseases. This study proposes coral reef disease detection using the YOLO model YOLO8l, YOLO8x, and YOLO8m. The results of the model evaluation test with a threshold value of 0.5 to 0.95 against the test data show that the three models can detect coral reef diseases with an accuracy of 99%. These results prove that the YOLOv8 model in this study is suitable for the real-time detection of coral reef diseases to replace the Underwater Photo Transect (UPT) method, which has low accuracy. Applying the YOLOv8 method will help Prevent Marine Habitat Damage in Batam City.
Software Product Line Engineering in Supply Chain Management Systems for Manufacturing Sector Saviero, Jehian Norman; Raihan, Muhammad; Komarudin, Oman; Azurat, Ade
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.5605

Abstract

Manufacturing companies are industrial enterprises that process raw materials and implement Supply Chain Management (SCM). SCM encompasses three stages: material management, planning and control, and production. While these stages are common across manufacturing companies, the workflows and strategies employed vary based on the type of goods produced. For example, one company typically approaches process orders based on requests, whereas the other processes orders based on stock availability. To address these similarities and differences, a software product line engineering (SPLE) approach can be utilized to develop SCM systems. This approach has already been proven effective in other cases, such as developing various product specifications for our Crowdfunding Application (Amanah CS UI) partner. SPLE follows the principle of mass customization, analyzing the commonalities and variabilities of the SCM system to meet diverse company needs. This approach improves the cost optimization and time efficiency in developing various SCM specifications to fulfill the requirements of each company. The development of the SCM system in this study adopts a delta-oriented programming paradigm and Abstract Behavioral Specification programming language. Subsequently, a comparison was made between the development of the SCM system using the SPLE approach and the clone-and-own approach. The research results in an enhanced SCM system developed through the SPLE, establishing it as the primary solution to existing development issues: reusing shared components and adding new custom components. Additionally, it includes an analysis that compares the SPLE approach with the clone-and-own method.
Prediction of Employee Recruitment Selection in Indonesia Pharmaceutical Company Using Backpropagation Networks Normalita, Apfia; jong, Jek Siang; Santoso, Halim Budi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6303

Abstract

PT K-24 Indonesia is one of the foremost companies in Indonesia, with a primary focus on distributing pharmaceutical products and healthcare services. During the last 2 years, PT K-24 has received more than 110,000 job applicants, with various position vacancies offered. The recruitment process begins with registration, online tests, and interviews. The need for manpower increases annually. More attention is required to select prospective employees who match the selection criteria. However, during the process, PT K-24 found that the recruitment process was less efficient because the applicants did not meet the company’s criteria. To overcome this problem, it is necessary to create a recommendation system for candidate selection. This study developed a recommendation using the multi-layer perceptron method, namely backpropagation. According to the prior literature, this method effectively reduces the error rate of prediction and recommendation results. This study also found that relevant data, the number of input parameters is not big enough, and the minimum network model can make better predictions with a considerable mean square error of 0.029. Our study contributes to the methodological approach by implementing real-world problems and measuring additional parameters that fit the selection requirements
Evaluating the Accuracy of a Hybrid Neural Model with RBF-Polynomial Kernel for Rainfall Prediction: A Comparative Analysis of Trainlm and Trainrp Functions Syaharuddin, Syaharuddin; Abdillah, Abdillah; Alfiana Sahraini; Lilis Suriani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6388

Abstract

Accurate rainfall prediction is crucial for effective water management and disaster mitigation. This study introduces a novel hybrid neural model that employs a fourth-degree polynomial kernel and provides the first empirical comparison of the trainlm and trainrp functions to enhance forecasting accuracy. This study explored the application of a neural network algorithm with RBF-Polynomial (degree 4) kernel for training and testing data in rainfall forecasting. This study focused on monthly rainfall data collected from Mataram City, Indonesia. We developed a hybrid BP-RVM algorithm as the main algorithm that offers a predictive approach to compare the trainlm and trainrp functions. We conducted 20 trials with combinations of learning, momentum, and gamma-RBF at internal values of 0.01-0.9. The training results from trainrp with more than 118 iterations yielded the best performance with learning rate 0.8 and momentum 0.2; MSE value of 2,236.25 and RMSE of 47.29. These results indicate a relatively low error rate for the proposed method. In contrast, the trainlm method, which only requires 18 iterations with a learning rate of 0.6 and momentum of 0.4, produces an MSE of 2,689.25 and RMSE of 51.86, showing its efficiency in reducing the computation time but with a slightly higher error rate than trainrp. Overall, the trainrp method was more accurate in capturing actual rainfall patterns with lower error rates, whereas the trainlm method exhibited good stability but greater sensitivity to parameter variations. This comparative analysis highlights the potential of trainrp to achieve more precise rainfall predictions within the study area.
Optimization of Accuracy Improvement through Modified ShuffleNet Architecture in Rice Classification Ahmad, Abdullah; Hartama, Dedy; Windarto, Agus Perdana; Wanto, Anjar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6411

Abstract

Accurate rice classification is essential to determine the quality and market value of rice. Traditional methods of rice classification are often time-consuming and error-prone, so a more efficient and accurate solution is needed. This study aims to optimize rice classification using Convolutional Neural Networks (CNN) combined with the ShuffleNet architecture, which offers high computational efficiency without sacrificing accuracy. The dataset used comes from Kaggle, containing 8750 rice grain images divided into five classes: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. The uniqueness of this study is the application of ShuffleNet Proposed in rice classification, which provides improved performance compared to basic CNN models such as MobileNet, ShuffleNet, and RestNet. The results showed that the MobileNet model achieved 80% accuracy, RestNet 94%, and ShuffleNet achieved 100% accuracy with precision, recall, and F1 values also 100%. However, the ShuffleNet model experienced overfitting when tested with new data, resulting in an accuracy of only 20%. To overcome this, further optimization was carried out on the model. The results of statistical tests (paired t-test and Wilcoxon test) show significant differences between ShuffleNet Proposed and other models, which proves that the improvements applied to this model provide significant improvements. The implications of this study can improve the efficiency and accuracy of rice classification, which has the potential to improve the quality and market value of rice in the agricultural industry.
Early Stroke Disease Prediction Based on Lifestyle Factors Applied with Machine Learning Suastika Yulia Riska; Lia Farokhah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6495

Abstract

Stroke prediction has many supporting features and variables. Some forecasts focus more on health or elements that are already present. Predicting stroke risk by identifying habitual factors provides more advantages for preventive action. In addition, the complexity of features or variables is a concern in predicting stroke risk. In this study, we used a public dataset from Kaggle with 10 features or variables. In this study, we propose to collaborate algorithms and preprocessing in feature selection using Pearson Correlation and Principal Component Analysis (PCA) dimension reduction to unravel the complexity of variables and data processing computing. This aims to predict stroke risk more simply. The results of the experiment show that feature selection using Pearson Correlation between features and labels produces maximum results using 5 features out of 10 provided features. This approach produces the best performance on the Naïve Bayes, Iterative Dichotomiser Tree (ID3), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Logistic Regression with 100% accuracy and reduces features by 50% to support the reduction of the complexity of prediction variables and data processing computing.
Stacking Ensemble Learning Model for Intrusion Detection in Electrical Substation Alam, Mohammad Mahruf; Pribadi, Feddy Setio; Rizky Ajie Aprilianto; Arvina Rizqi Nurul’aini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6502

Abstract

Electrical substations are crucial infrastructure in power transmission and distribution but are increasingly vulnerable to cyber threats. However, existing intrusion detection systems (IDS) face challenges such as high false positive rates, limited adaptability to emerging attack patterns, and imbalanced detection across different intrusion types. This study proposes a Stacking Ensemble Learning model to enhance intrusion detection accuracy in electrical substations. The proposed model integrates Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost (XGB) as base models with XGB acting as the meta-model. A real-world electrical substation IEC 60870-5-104 network traffic dataset comprising 319,949 instances with multiple attacks, such as DoS, Port Scan, NTP DdoS, IEC 104 Starvation, Fuzzy Attack, Flood Attack, and MITM, was used for this study. The results showed that the stacking model had the best accuracy (0.99990), precision (0.99990), recall (0.99990), and F1-score (0.99990), beating out the base, Bagging, and Boosting models. T-test results further confirmed statistical significance, with p-values of 0.00428 (LR), 0.04237 (SVM), 0.00000 (XGB), 0.00057 (KNN), 0.00549 (Boosting), and 0.00000 (Bagging) reinforcing the superiority of the Stacking Ensemble Learning approach. These findings highlight the effectiveness of Stacking Ensemble Learning in enhancing the detection accuracy of IDS for electrical substations and outperforming traditional models and other ensemble learning methods by minimizing false positives and false negatives.
Eye Disease Detection and Classification Optimization Using EfficientNet-B5 with Emphasis on Data Augmentation and Fine-Tuning Anggi Muhammad Rifai; Muhammad Fatchan; Ahmad Turmudi Zy; Donny Maulana; Sufajar Butsianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6519

Abstract

Eye diseases such as glaucoma, cataract, and diabetic retinopathy pose significant global health challenges, underscoring the need for accurate and efficient diagnostic systems. This study employed the EfficientNet-B5 model to enhance the detection and classification of eye diseases by incorporating advanced data augmentation and fine-tuning techniques. The research utilizes the Ocular Disease Intelligent Recognition (ODIR) dataset, consisting of 4,217 fundus images categorized into four classes: normal, glaucoma, cataract, and diabetic retinopathy. The methodology comprises three phases: baseline model training, model training with data augmentation, and fine-tuning. The baseline model achieved an accuracy of 60.43%, which improved to 63.03% with data augmentation—an increase of 2.6 percentage points. Fine-tuning further elevated the accuracy to 93.23%, representing a notable improvement of 33.8 percentage points over the baseline. Model performance was evaluated using standard classification metrics including accuracy, precision, recall, and F1-score. These findings demonstrate the technical efficacy of combining augmentation and fine-tuning to enhance model generalization. The proposed approach offers a robust framework for developing dependable AI-driven diagnostic tools to support early detection and facilitate informed clinical decision-making.
Spatial-Temporal Analysis of Earthquakes in Indonesia with Deep Learning Models: Performance Evaluation of CNN, LSTM, and Hybrid CNN-GRU Susandri, Susandri; Fajrizal, Fajrizal; Bakri Nasution, Feldiansyah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6538

Abstract

Indonesia, located along the Pacific Ring of Fire, experiences high seismic activity with over 6,000 earthquakes annually. Accurate earthquake prediction remains a major challenge because of the complexity of geological dynamics and limitations of traditional methods in capturing nonlinear seismic patterns. Although deep learning approaches have shown promise, previous studies have often treated spatial and temporal analyses separately, limiting holistic predictive performance. This study proposes a novel hybrid CNN-GRU deep learning model that integrates spatial feature extraction CNN and temporal sequence modeling GRU, and compares its performance with of that CNN, LSTM, GRU, and Bidirectional LSTM using a dataset of 117,251 earthquake events in Indonesia (2008–2024). The results show that Bidirectional LSTM achieved the best temporal accuracy (R² 0.653, RMSE 0.592), while the hybrid CNN-GRU provided balanced spatial-temporal performance (R² 0.587). Notably, the performance gap between Bidirectional LSTM and other models (e.g., Hybrid CNN-GRU) was statistically validated via paired t-test (p < 0.05). The proposed models generalize well to unseen regions such as Maluku-Papua. The key contribution is the hybridization of spatial-temporal learning in a single-model architecture - where CNN processes latitude-longitude coordinates via 1D convolutions while GRU handles temporal sequences - an approach lacking in earlier works. This directly improves early warning systems in seismically active areas by providing 32% higher accuracy than conventional methods.
Application of Reinforcement Learning to Solve Rubrik’s Cube with Markov Decision Process Defni; Andi Fathul Mukminin; Ainil Mardhiah; Titin Ritmi; Junaldi; Yuhefizar; Fibriyanti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6552

Abstract

The Rubik's Cube is a complex puzzle with an enormous number of possible configurations, making it a challenging problem for both humans and computational methods to solve. While traditional solving algorithms rely on predefined strategies, this study explores the application of reinforcement learning (RL) to develop an adaptive and efficient solution model. This study aims to create an RL_based solver using the Markov Decision Process (MDP) framework, optimizing for speed, move efficiency, and solving steps. The proposed model employs Q-learning and Monte Carlo Tree Search (MCTS) to determine the optimal actions at each game state, which are trained through extensive Rubik's Cube simulations. The key novelty of this study lies in the integration of MCTS with Q-learning to enhance decision-making efficiency by reducing the number of moves compared with conventional methods. The experimental results demonstrate that the model achieves near-optimal solutions with fewer moves, outperforming basic rule-based approaches. Additionally, a web-based application was developed to provide real-time solving strategies based on user-input cube configurations. This study contributes to the advancement of RL applications in combinatorial puzzles and offers a practical tool for Rubik's Cube enthusiasts seeking to improve their solving techniques.

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