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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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A Comparative Evaluation of YOLOv9 and DETR Models in Traffic Object Detection for Intelligent Surveillance Systems Jamil, Mohamad; Nagu, Nani; Said, Muhammad
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.v9i5.6556

Abstract

Object detection plays a crucial role in traffic surveillance, particularly in urban environments characterized by high vehicle density, diverse weather conditions, and limited computational resources. Although YOLOv9 and DETR have demonstrated strong performance in general object detection tasks, there is a lack of comparative research evaluating their effectiveness under specific challenges of traffic surveillance. These challenges include the need for real-time processing, accurate detection of small or partially occluded objects, and adaptability to complex traffic scenarios. This study addresses this gap by conducting a comparative evaluation of YOLOv9 and DETR using a custom traffic image dataset, with training iterations varied from 10 to 50 epochs to observe performance development. Evaluation metrics included mean average precision, precision, recall, F1-score, inference time, and object count per image. The results indicated that DETR achieved the highest accuracy across all metrics at the final training stage and detected up to 22 objects per image. However, the average inference time exceeded seven seconds per image, limiting the real-time applicability. Conversely, YOLOv9 achieved competitive accuracy with a significantly faster inference time of approximately 0.43 seconds per image. These findings provide practical insights into the trade-off between accuracy and processing efficiency, and offer guidance for model selection in operational traffic surveillance systems.
Image Classification of Rice Leaf Diseases with KNN Based Model using Stratified-KCV Rizqi, Tasya Anisah; Anwar Fitrianto; Kusman Sadik
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.v9i5.6590

Abstract

Rice is a staple food for people in the world, especially Indonesia. The rice harvest decreased in 2023, reducing harvest productivity and causing losses for farmers. Rice cultivation is often affected by diseases that hinder rice harvests. SKCV is a resampling method that performs more accurately because it can ensure that class frequencies are maintained. RGB and VGG16 are image processing methods that extract images into numerics. RGB image extraction is done by taking the average value of the red, green, and blue layers while VGG16 image extraction is done by taking the value of visual pattern features such as edges, textures, and object shapes. In this study, rice leaf diseases were classified using KNN-based models, including KNN, WKNN, CDNN, and ECDNN. This classification was performed to determine which method had better performance using SKCV and comparing the results of RGB and VGG16 image extraction. This classification also produces a comparison of SKCV and KCV results to determine the best resampling performance. The results of the analysis that have been carried out show that the ECDNN method produces the highest accuracy of 81.20% in classifying rice leaf diseases using SKCV with VGG16 extraction followed by CDNN and WKNN each at 68.80%, and KNN at 56.20% while RGB extraction only produces an accuracy of 43.8% using ECDNN and CDNN, 56.20% using WKNN, and 50% using KNN. The results of this rice leaf diseases classification analysis are expected to help farmers in increasing rice production in Indonesia.
From Serial to Parallel: Enhancing Needleman-Wunsch Performance through GPU-Based Computing Suharini, Yustina Sri; Kusuma, Wisnu Ananta; Nurdiati, Sri; Batubara, Irmanida
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.v9i5.6620

Abstract

The increasing demand for faster bioinformatics analysis calls for more efficient approaches for sequence alignment. In this study, we demonstrate that a GPU-based implementation of the Needleman-Wunsch algorithm can achieve up to 14.8× speedup compared to its traditional CPU-based serial counterpart, without compromising alignment accuracy. By leveraging the parallel processing capabilities and shared memory of an NVIDIA GeForce RTX 3060 Laptop GPU, we significantly accelerated global sequence alignment tasks. Using clinically relevant genes such as NRAS, BRCA1, BRCA2, and Saccharomyces cerevisiae from NCBI ensures realistic alignment challenges and biological significance. Performance evaluation across a wide range of sequence lengths demonstrates the scalability and efficiency of the parallel approach. More importantly, this study provides a unique contribution by showing that a commodity GPU, such as the NVIDIA GeForce RTX 3060 Laptop, can serve as a practical alternative when high-performance computing clusters are unavailable or prohibitively expensive, thereby offering an accessible and cost-effective pathway to high-throughput bioinformatics workflows.
IoT-Based Smart Infusion Monitoring and Control System Using ESP32 Simatupang, Frengki; Istas Pratomo Manalu; Ana Muliyana; Paian Manalu; Erna Meliana Manurung; Batara Hasintongan Nadapdap
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.v9i5.6632

Abstract

Infusion is a common medical procedure used to treat conditions such as gastric acid and typhoid, where precise fluid administration is critical. This study presents the development of an IoT-based smart infusion monitoring and control system using an ESP32 microcontroller, designed to automatically monitor infusion volume and regulate drip rate in real-time. The system integrates a load cell sensor to measure infusion fluid weight, a photodiode sensor to detect drip rate, and a servo motor to adjust the flow rate adaptively. It features web-based monitoring, buzzer alerts, and an LCD display for local feedback. The system was tested in a clinical simulation with an infusion requirement of 1500 mL per 24 hours and various drip factors (15, 20, and 60 drops/mL). The infusion volume status is automatically categorized into three levels: FULL (>350 mL), HALF (150–350 mL), and WARNING (<150 mL). Based on 10 test scenarios, the system accurately classified volume levels and triggered warnings when volume dropped below 150 mL. For example, in Test-08 to Test-10, volumes of 139.67 mL, 87.34 mL, and 40.53 mL were correctly detected as “WARNING” with buzzer alerts activated. The load cell sensor achieved excellent accuracy, with an error margin between 0.02% and 0.06%, while the system maintained drip-rate stability within a ±5% tolerance range. It also dynamically adjusted the servo angle to correct under- or over-drip conditions. These results confirm that the system delivers accurate, automated, and responsive infusion control, making it suitable for healthcare settings with limited staff to improve safety and efficiency.
Optimization Techniques and Programming for Developing Cost-Effective and Balanced Diet Schedules for Preschoolers Mohd Lip, Norliana; Sufahani, Suliadi Firdaus; Mohd Fahmi Teng, Nur Islami
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.v9i5.6650

Abstract

Proper nutrition is important for the growth, motor and cognitive development of young children since the foods consumed determine how well-rounded a child's diet is. However, preschool menu planning is complex because it requires balancing multiple constraints such as cost, dietary guidelines, and food variety. This study introduces a computational approach to menu planning for preschools through Linear Programming (LP), Integer Programming (IP), and Binary Programming (BP). This study highlights algorithmic design, constraint modelling, and computational efficiency in solving optimization problems, rather than focusing primarily on dietary outcomes. The models were tested using Malaysian food database to evaluate both feasibility and efficiency. The findings indicate that all models successfully fulfilled the Recommended Nutrient Intakes (RNI 2017) for children aged 4 to 6, ensuring adequate levels of energy, protein, calcium, carbohydrates, and fat. In terms of cost, the LP model was the most economical at RM4.20 per day, but impractical due to fractional servings. The IP model produced a more realistic balance between cost and practicality at RM4.40 per day. The BP model generated the most diverse and implementable menus at RM5.00 per day, though at a higher cost. Overall, these optimization methods provide decision-support tools for enhancing the efficiency of preschool menu planning.
Hyperparameter Tuning with Optuna to optimize the YOLOv11n Model for Weed Detection Candhy Fadhila Arsyad; Pulung Nurtantio Andono; Moch Arief Soeleman
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.v9i5.6682

Abstract

Accurate weed detection is essential for maintaining the cleanliness and aesthetic appeal of residential yards. This study aimed to optimize YOLOv11n, a lightweight object detection model, to achieve high precision in weed identification under real-world conditions. The novelty of this study lies in the application of Optuna, an automatic hyperparameter optimization framework, to enhance model performance while maintaining computational efficiency for resource-limited devices such as drones and IoT systems. The research involved data augmentation techniques including crop (0–20% zoom), hue (±20°), saturation (±30%), brightness (±20%), exposure (±15%), and mosaic augmentation. These augmented images were used to train four YOLO nano variants (v5n, v8n, v11n, v12n), which were evaluated using standard metrics: Precision, Recall, F1-Score, and mean Average Precision (mAP). Among the models tested, YOLOv11n with Custom Optuna configuration delivered the highest performance, achieving a 94.6% F1-score and 97.8% mAP@0.5. These results demonstrate that the optimized YOLOv11n model can support accurate and efficient real-time weed detection in household environments, particularly on edge devices with limited hardware capabilities. This makes it a viable solution for practical implementation in precision agriculture and smart gardening.
Automated Ripeness Detection of Oil Palm Fruit Using a Hybrid GLCM-HSV-KNN Model Mirfan; Billy Eden William Asrul; Mila Jumarlis; Juliani, Juliani
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.v9i5.6683

Abstract

Accurately determining the ripeness of oil palm fruit is crucial for ensuring the quality of palm oil. However, traditional manual methods are often time-consuming and less accurate. This study aimed to develop an automated system for detecting the ripeness of oil palm fruit by combining the Hue Saturation Value (HSV) model, Gray Level Co-occurrence Matrix (GLCM), and K-Nearest Neighbor (KNN) algorithms. This system utilizes K-Nearest Neighbors to classify the relationship between color features extracted using the HSV model and texture features derived from GLCM analysis to categorize fruit ripeness. The color features represent the fruit's chromatic characteristics associated with ripeness, while the texture features provide information regarding surface patterns related to ripeness. The color features represent the fruit's color characteristics associated with ripeness, whereas the texture features provide information about the surface patterns related to ripeness. The results indicate that the system can classify oil palm fruit into four distinct categories: Over-Ripe, Ripe, Half-Ripe, and Raw. The dataset was divided with an 80:20 ratio, where 80% was allocated for training data and the remaining 20% for test data. An accuracy rate of 85% was achieved. The results of this study demonstrate that the developed system effectively classifies oil palm fruit images based on ripeness levels. This system supports a sustainable automated palm oil production model through accurate ripeness detection, thereby reducing reliance on manual methods and enhancing consistency and productivity in palm oil processing. These findings indicate that the proposed hybrid method is feasible for integration into an automated classification system to support decision-making in oil palm harvesting
Early Detection of Grasserie Disease in Silkworms Using Computer Vision and Machine Learning Sania Thomas; Binson V A; Sini Rahuman; Sivakumar K S
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.v9i5.6705

Abstract

Silkworm diseases are a significant threat to the sericulture industry, with early detection remaining a major challenge due to limited resources. Timely identification of infected silkworms is essential to curb the spread of disease and reduce economic damage. This study focuses on diagnosing Grasserie disease, a highly contagious condition that can devastate silkworm populations, leading to substantial financial losses for farmers. To address the shortcomings of expert manual inspections, this study employed camera-captured images of silkworms for automated disease detection. A newly compiled dataset, consisting of 668 healthy silkworms and 574 infected with Grasserie disease, forms the basis of the investigation. The study applies machine learning techniques for image analysis, combining Histogram of Oriented Gradients (HOG) for feature extraction, Kernel Principal Component Analysis (KPCA) for dimensionality reduction, and supervised classification models. The results highlight the effectiveness of this approach in differentiating healthy silkworms from diseased ones. The machine learning model HOG integrated with KPCA and Decision Trees (DT) achieved strong performance, with accuracy, recall, and precision scores of 94.28%, 94.56%, and 92.48%, respectively. While these outcomes are encouraging, further research is needed to develop a practical IoT-based tool that enables sericulture farmers to quickly detect infections and take preventive measures, minimizing unexpected losses. This study marks a crucial advancement in silkworm disease detection, offering a pathway toward greater sustainability and economic stability in the sericulture sector.
A Data-Driven Comparison of Linear Mixed Model and Mixed Effects Regression Tree Approaches for Dairy Productivity Analysis Achmad Fauzan; Fatkhurokhman Fauzi; Rhendy K P Widiyanto; Khairil Anwar Notodiputro; Bagus Sartono
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.v9i5.6751

Abstract

Dairy productivity studies often involve hierarchical and longitudinal data structures that violate the assumptions of linear regression. This study compares two modeling approaches, Linear Mixed Model (LMM) and Mixed Effects Regression Tree (MERT), in predicting dairy productivity based on the 2024 National Dairy Productivity Survey data. Predictive performance was evaluated using MSEP, RMSEP, MAD, and MAPE, with MERT consistently outperforming LMM in accuracy and robustness. Permutational Multivariate Analysis of Variance (PERMANOVA) test results reinforced this finding, yielding a pseudo-F value of 224.7 and a p-value of 0.001, indicating statistically significant differences in model performance. Key predictors of MERT model included farm altitude, the previous week’s milk production, and the amounts of concentrate feed given, which are part of significant predictor variables in LMM. This finding underscores MERT’s superiority in modeling complex agricultural datasets while providing interpretable insights through data-driven segmentation. The study advocates policy focus in sustainable milk production as well as the availability of high quality of feed and altitude-based dairy farms location to improve milk productivity. Should these focuses implemented by the industry, combined with the MBG Program, Indonesia would be progressing better towards achievement of SDGs Goal 2 and 3.
Explainable Ensemble Learning for Maternal Health Risk in Low-Resource Settings Widyawati, Lilik; Sulistianingsih, Neny
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.v9i5.6765

Abstract

Maternal health remains a global challenge, particularly in low-resource settings where accurate and timely risk prediction is essential to reducing maternal mortality. This study proposes an explainable machine learning framework for predicting maternal health risks by integrating ensemble learning methods with SHAP (Shapley Additive exPlanations) for interpretability. This study utilized the publicly available Maternal Health Risk Data Set (MHRDS), comprising physiological features such as systolic and diastolic blood pressure, blood sugar level, body temperature, and age. A total of 18 machine learning models including Random Forest, XGBoost, LightGBM, Neural Networks, and TabNet were evaluated to compare individual classifiers and ensemble approaches comprehensively. The selection of this diverse set of models is grounded in the need to benchmark different algorithmic paradigms, as variations in inductive bias, learning capacity, and robustness to clinical data noise can influence predictive performance and generalizability. This comprehensive comparison enables the identification of optimal model types for integration into ensemble frameworks. Evaluation was performed across three different test scenarios (test sizes of 10%, 20%, and 30%) to assess model consistency under varying data partitions. Stacking, Voting, and Histogram-based Gradient Boosting showed consistently high performance, with Stacking achieving the highest accuracy of 87.2%, followed by Histogram Gradient Boosting (86.9%) and Voting (86.7%) at test size 0.2. SHAP analysis identified blood sugar, systolic blood pressure, and maternal age as the top predictors across all test scenarios. The best-performing models were deployed into a web-based clinical decision support system designed for healthcare practitioners in Indonesia. The proposed approach balances predictive accuracy and model transparency, offering a practical solution for improving maternal care in data-limited environments.

Page 101 of 105 | Total Record : 1046

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