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Tri A. Sundara
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tri.sundara@stmikindonesia.ac.id
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The Indonesian Journal of Computer Science
Published by STMIK Indonesia Padang
ISSN : 25497286     EISSN : 25497286     DOI : https://doi.org/10.33022
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Indonesian Journal of Computer Science (IJCS) is a bimonthly peer-reviewed journal published by AI Society and STMIK Indonesia. IJCS editions will be published at the end of February, April, June, August, October and December. The scope of IJCS includes general computer science, information system, information technology, artificial intelligence, big data, industrial revolution 4.0, and general engineering. The articles will be published in English and Bahasa Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 65 Documents
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Search results for , issue "Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science" : 65 Documents clear
Investigasi Tantangan dalam Penerapan Tata Kelola Keamanan Informasi di Sektor-Sektor Utama: Analisis Komparatif Lintas Negara Saraswati, Karisa; Purwandari, Betty; Trisnawaty, Ni Wayan
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4637

Abstract

This study investigates the challenges of implementing information security governance across countries. Initial analysis was carried out from cases in Indonesian organizations. Using Kitchenham’s systematic literature review to identify challenges and information security governance expert interviews to validate the result, the research analyzes thirty-four issues and compares them with those in other developing and developed countries. The objective is to identify common challenges, highlight differences, and propose recommendations for improvement. Findings reveal that Indonesia faces difficulties that are similar to those of other developing nations, such as limited leadership support and resource constraints. In contrast, developed countries struggle with overlapping regulations and maintaining compliance despite having stronger frameworks. The study emphasizes the importance of cohesive frameworks, enhanced training, and management support to improve governance practices. These results provide actionable insights for policymakers and organizations to strengthen information security governance and address the increasing complexity of global cybersecurity challenges.
Optimasi Klasifikasi Gestur Tangan Menggunakan Metode CNN Dengan Implementasi Strategi Landmark Berbasis Warna Komplementer Agus Nugroho; Jasmir; M. Riza Pahlevi. B, S; Roby Setiawan
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4645

Abstract

The growth of hand gesture recognition technology has positively impacted various sectors. However, classification errors often occur due to the similarity of gesture shapes, which are challenging for models to differentiate. This study aims to develop a classification method based on Convolutional Neural Network (CNN) using a landmark modification approach with complementary colors. This approach applies significant color contrast to enhance the model’s ability to extract unique features from similar hand gestures. The dataset used includes gestures with color modifications on landmarks using an HSV-based color wheel to create maximum contrast. The data is then processed through bounding box creation, resizing, and transfer learning using the Teachable Machine architecture. The study results show a significant improvement in classification accuracy for models with landmark modifications compared to those without. Metrics analysis, including precision, recall, and F1-score, confirms that this approach effectively reduces classification errors caused by similar hand gestures.
A Novel Leak Detection Algorithm Based on SVM-CNN-GT for Water Distribution Networks Komba, Giresse
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4674

Abstract

Water Distribution Networks (WDNs) suffer substantial water losses due to pipeline leaks, resulting in economic ramifications and exacerbating global water scarcity concerns. This paper seeks to improve the precision of leak detection and the identification of leak locations within WDNs. The pervasive issue of leaks in WDNs poses significant challenges with economic and environmental implications for water utilities. Traditional leak detection methods are time-consuming, resource-intensive, and susceptible to inaccuracies and false alarms due to the random placement of sensors. The detection of concealed background leaks, invisible to the naked eye and situated beneath the surface, presents a particular challenge. This situation complicates efforts for their real-time identification and subsequent repairs. To address these challenges, this paper introduces the SVM-CNN-GT algorithm, an advanced ensemble supervised Machine Learning (ML) approach that incorporates Support Vector Machines (SVM), Convolutional Neural Network (CNN), and Graph Theory (GT). By combining multiple ML algorithms, the SVM-CNN-GT model takes into account various factors that influence leak detection and localization, resulting in more precise and reliable assessments of leak presence and location. The algorithm leverages automatic feature extraction and heterogeneous dual classifiers to accurately assess leaks based on data related to flow rate, pressure, and temperature. Furthermore, a combination probability scheme enhances leak detection efficiency by integrating diverse classifier models with distinct prediction outputs. Through the EPANET performance evaluations, the SVM-CNN-GT algorithm outperforms CNN and SVM algorithms, demonstrating remarkable proficiency with the highest average leak detection accuracy of 98%, followed by CNN at 82% and SVM at 78%.
Segmentasi Citra Daun Tomat Berpenyakit dengan Metode K-Means Clustering pada Ruang Warna HSV Haidar Ahmad Fajri; Safrizal Ardana Ardiyansa; Eric Julianto
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4685

Abstract

Tomatoes have health benefits and high economic value, but are susceptible to diseases that can reduce yields by 50-60%. Early detection of tomato leaf diseases is necessary to reduce losses. Manual identification is time-consuming and costly, so an efficient technique is needed. This research proposes an image processing-based preprocessing technique using contrast stretching, clustering, background removal, and conversion to Hue-Saturation-Value color space. The results show that the proposed technique is able to identify septoria spot, mosaic virus, and bacterial spot, which are 94.99%, 92.83%, and 94.57%, respectively. Bacterial spot also had the highest sensitivity of 88.02%. This indicates that the technique is effective in detecting the disease, hovewer mosaic virus has a lower sensitivity of 82.53%. This value indicates that several cases were not correctly identified. Bacterial spot had the highest value of 87.74% in F_1-score followed by septoria spot at 87.01% and mosaic virus at 85.59%.
Machine Learning Techniques for Early Detection and Diagnosis of Breast Cancer Prediction Al-Duais, Mohammed; Abdualmajed A.G. AL- Khulaidi; Fatma Susilawati Mohamad; Walid Yousef; Belal AL-Futhaidi; Murshid Al-Taweel; Mumtazimah Mohamad; Mohd Nizam Husen
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4690

Abstract

Currently breast cancer is considering very serious disease of death among women. The main reason for this cause is late of detected and diagnosis. The early detected and diagnosis help women for longer on live. Machine learning techniques is providing a best technique for early detected, diagnosis and predication of breast cancer. The objective of this study applied and compare two different techniques of machine learning (ML) to determent which give superior performance for predication for breast cancer. The method focuses on to achieve the objectives of this study, there are many steps has been done such as: Data collection and data preprocessing, design the proposed model. Two techniques have been used traditional and ensemble machine learning techniques. The traditional includes several algorithm such as Support vector machine (SVM), Naïve Bayes(NB), Logistic Regression (LR), K-Nearest Neighbor (KNN), and decision tree(DT) while the ensemble ML techniques covers several algorithm such as Random frost (RF), XGBoost and Adaboot.’ To evaluate the performance of these techniques, this study used several measurements such as accuracy, precision, recall, Fl scores for evaluation the performance . The results show that the ensemble ML technique gives superior classification than traditional ML technique. However, the average accuracy of the ensemble ML technique is 0.97, while the average accuracy of Traditional ML techniques is 0.96.Conclusion: The ensemble machine learning techniques outperform than traditional ML technique for detection diagnosis and prediction of breast cancer.
A Robust Bayesian Dynamic Stackelberg Game Theory Detection Scheme for Man-in-the-Middle Attack in Mobile Edge Computing Networks Moila, Lerato; Mthulisi Velempini
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4723

Abstract

Mobile Edge Computing (MEC) networks are emerging technologies transforming how data is processed, stored, and delivered at the edge network, enhancing performance and reducing latency. However, the technology introduces significant cybersecurity challenges, specifically Man-in-the-Middle (MitM) attacks. These attacks compromise sensitive data and can disrupt normal services. This study proposes a robust detection scheme based on Bayesian Dynamic Stackelberg Game Theory to address these vulnerabilities. By incorporating Bayesian inference, the scheme considers uncertainties in the attacker’s behaviour and the network environment, enabling the defender to update its strategies dynamically based on observed actions. The simulation results show that the proposed scheme significantly improves the detection scheme for MitM attacks in MEC networks, outperforming other schemes considered in the study. The findings show that integrating Game Theory with Bayesian analysis provides a promising approach for developing adaptive and resilient cybersecurity strategies in the evolving landscape of edge computing.
Perbandingan Seleksi Fitur Forward Selection dan Backward Elimination pada Algoritma Support Vector Machine Suharmin, Wandayana Nur'Amanah; Hasan, Isran K.; Yahya, Nisky Imansyah
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4755

Abstract

Support Vector Machine (SVM) is an effective and robust classification method, particularly when applied to high-dimensional data. However, high-dimensional data often contain irrelevant features that can lead to suboptimal SVM performance. Therefore, a feature selection process is necessary to optimize classification performance by eliminating irrelevant and redundant features from the original dataset. This research aims to compare the Forward Selection and Backward Elimination feature selection methods within the Support Vector Machine Algorithm for classification using the Poverty Depth Index data in Papua Province. The results indicated that applying the Support Vector Machine with Forward Selection feature selection achieved a classification accuracy of 93%, whereas Backward Elimination feature selection achieved a classification accuracy of 97%. Based on these classification accuracy results, it can be concluded that applying Support Vector Machine with Backward Elimination feature selection results in better performance than Forward Selection.
Pengembangan Sistem Sensor berbasis Tekanan Udara untuk Deteksi Kontak Kaki Robot Dwi Prasetyo, Wahyu Agung; Darmawan, Adytia; Dewanto, Raden Sanggar; Alfathdyanto, Khairurizal
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4757

Abstract

Legged robot is preferred choice for travesing uneven terrain. Robot leg can be positioned dynamically to achieve better locmotion. Detection of the leg contact point became more of essential part for the unpredictable course. The common method by deploying resistive force sensor provides a binary condition of whether the leg has touches surface. This paper explores the possibility of implementing air pressure sensor on a sensor system to provide more information at robot leg contact point. Air pressure sensor can provide a more wide and continuous range of value that fluctuates along the contact rate of the leg. Verification of the study uses single leg part of dog-type quardruped. The sensor testing gave the output value with average error of 1,3%. The pressure sensor provides readings at around ± 40ms with maximum readable pressure of 1,5 kPa.
Heart Failure Disease Classification Using Random Forest Algorithm with Grid Search Cross Validation Technique Septia, Rapindra; Junadhi; Susi Erlinda; Wirta Agustin
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4765

Abstract

Heart failure is one of the leading causes of death worldwide and requires early detection to reduce the risk of serious complications. However, the imbalance in medical data poses a challenge in developing accurate prediction models. This study developed a heart failure classification model using the Random Forest algorithm, optimized with Grid Search Cross Validation to find the best combination of hyperparameters. The dataset consisted of 300 observations with 12 medical features and 1 target feature. Data preprocessing included outlier removal using the Interquartile Range (IQR) and Winsorize methods. The Synthetic Minority Oversampling Technique (SMOTE) was applied to address class imbalance, resulting in a more balanced training data distribution. The dataset was split into 80% training and 20% testing data using stratified sampling to maintain class proportions. The model was evaluated using accuracy, precision, recall, and F1-score metrics, with results showing 90% accuracy, 0.94 precision for class 0, 0.80 precision for class 1, 0.91 recall for class 0, and 0.86 recall for class 1. The model was implemented in a Streamlit-based application, allowing users to input health parameters and receive interactive predictions. This study demonstrates that optimizing the Random Forest algorithm with Grid Search Cross Validation can improve heart failure classification performance, providing a practical solution for supporting heart failure classification. Keywords: Heart Failure Classification, Random Forest, Hyperparameter Optimization, SMOTE, Model Evaluation.
Analisis Kepuasan Pengguna pada Aplikasi Spotify di Kota Palembang dengan Menggunakan Metode Usability Tri Wulandari, Dinda; M. Rudi Sanjaya; Dedy Kurniawan; Endang Lestari Ruskan
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4778

Abstract

Usability level is one of the methods that can affect the comfort of mobile application users, and evaluation is very important to do. Spotify, as an app for listening to music and podcasts, has users from different parts of the world. The evaluation is carried out with the aim of improving the user experience, so that the application can evolve and be easier to use. The method applied in this evaluation is usability testing. The results obtained show that the Spotify application is of good quality, effective, efficient, and able to provide satisfaction to users, although there is still room for improvement to meet user expectations even better.

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