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Tri A. Sundara
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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 1,114 Documents
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Klasifikasi Gerakan Olahraga Panahan Menggunakan YOLO dan Metode LSTM Adriansyah, Ahmad Rio; Wibowo, Edi; Panji, Krisna
The Indonesian Journal of Computer Science Vol. 14 No. 3 (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.v14i3.4664

Abstract

The classification of archery movements presents a significant challenge in the development of technology to support athlete training. This study aims to develop an archery movement classification system using a combination of YOLO for pose detection and Long Short-Term Memory (LSTM) for temporal classification. The system processes archery training videos into a sequence of images. The aim of this research is to train LSTM model to recognize patterns from four predefined archery movement classes: stand, extend, hold, and release. Evaluation results show that the system achieved an accuracy of 64.47%. Furthermore, analysis using precision, recall, and F1-score metrics indicates varying performance across the movement classes, with the highest F1-score of 83.75% achieved in the release class. This study contributes to the development of machine learning-based technology to support sports training, particularly in archery, by offering a data-driven approach capable of automatically recognizing and evaluating athletes' movements.
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%.
Analisis Sentimen Komentar Video Youtube Review Starlink Indonesia oleh Gadgetin menggunakan VADER dan TextBlob Absharina, Eriene Dheanda; Fatihani Nurqolbiah
The Indonesian Journal of Computer Science Vol. 14 No. 3 (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.v14i3.4686

Abstract

Satellite-based internet services such as Starlink have attracted great attention in Indonesia, especially among gadget fans, since their launch six months ago. Public sentiment analysis is necessary to evaluate market acceptance and improve service quality. Objective: This research aims to identify public sentiment towards Starlink Indonesia through analysis of YouTube comments on Starlink Indonesia video reviews by the Gadgetin channel. Methods: With a sentiment analysis approach on a dataset containing 10,591 comments. Using VADER and TextBlob, to classify comments into positive, neutral, and negative sentiment. Conclusion: Analysis obtained with VADER identified 44.7% of comments as neutral, 40.2% positive, and 15.1% negative. Meanwhile, TextBlob shows 50.5% neutral comments, 35.5% positive, and 14% negative.
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 Culture-Aware Bidirectional IsiXhosa-English Neural Machine Translation Model Using MarianMT Moape, Tebatso; Mohale, Thuto Siyamthanda; Bester, Chimbo
The Indonesian Journal of Computer Science Vol. 14 No. 3 (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.v14i3.4714

Abstract

Machine translation for low-resource African languages faces significant challenges due to limited data availability and complex linguistic features such as rich morphology, agglutinative grammar, and rich cultural expressions. This study proposes and develops a culturally aware machine translation model for isiXhosa-English language pairs using the MarianMT transformer-based model. We combine traditional parallel corpora with culturally enriched datasets, addressing the unique challenges of isiXhosa's linguistic intricacies. The proposed model was trained on a carefully curated dataset of 127,690 parallel sentences and used SentencePiece tokenization for handling agglutinative morphology. Our approach achieved a BLEU score of 58.79, marking a substantial improvement over previous methods, typically scoring between 20.9 and 37.11. The results demonstrate that integrating cultural context and linguistic specificities into the translation model substantially improves translation quality for low-resource languages. The study's findings suggest that considering cultural context, combined with appropriate model architecture and data preprocessing strategies, can lead to more accurate and culturally aware machine translation systems.
Perancangan Alat Penyemprotan Tanaman Cabai Sistem Kontrol Otomatis Berbasis IoT Rizkitinus, Fina; Eko Purnomo, Fendik; Adzar Prayoga, Fahrizal; ifnil mubarok, Diki; Ristaulia, Nasikhatul; Reza Rabani, Muhammad
The Indonesian Journal of Computer Science Vol. 14 No. 3 (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.v14i3.4721

Abstract

The application of Internet of Things (IoT) technology in the agricultural sector provides innovative solutions in managing chili plant pests. This study aims to design an IoT-based pesticide sprayer with automatic control using Arduino, ESP32, and solar panel energy sources. The Research and Development (R&D) method is used, including problem identification, planning, product development, trials, evaluations, and field validation. The test results show that this tool is capable of spraying water or pesticides up to a distance of 5 meters with a spraying area of 10 m² per cycle. With a spray capacity of up to 12 liters per cycle, this tool has succeeded in reducing the whitefly pest population by 70% and increasing chili plant productivity by 30% compared to manual methods. Energy usage analysis shows the efficiency of the tool through the use of 120 Wp solar panels and 12V 100Ah batteries that support pump operations for 1 hour 48 minutes. This study provides a sustainable solution for managing plant pests and increasing crop yields effectively by utilizing renewable energy.
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.
A Multi-Tenant Platform for Web-Based Library Applications Using Extreme Programming Methods Azhar, Muhamad Faqih; Munir, Sirojul; Imaduddin, Zaki
The Indonesian Journal of Computer Science Vol. 14 No. 3 (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.v14i3.4756

Abstract

This research aims to develop a web-based platform for book collection lending management with a multi-tenant concept using the Extreme Programming (XP) development method, which includes the frontend and backend parts. This platform is designed to support the needs of library units and book collectors, allowing each entity to operate independently in one system to improve operational efficiency and effectiveness. The Research and Development (R&D) approach used in this research includes the following stages of planning, designing, implementing, and testing within the XP method framework. The technology used to develop this web-based platform includes Golang for the backend and React TypeScript for the frontend, with PostgreSQL as the database. Data was collected through observation and literature studies, while system testing used the Black Box Testing method. This platform provides various main features, such as library or collector registration, book collection management, borrowing and returning processes, and transaction history recording. The study results showed that platform development was carried out in six iterations over three months with a sprint duration of two weeks, resulting in an average work speed per sprint of 13.6 points. The test results showed that all features functioned according to user needs without any functional errors, with a system success rate of 100%. Overall, this platform is stated to be of good quality, ready to operate, and expected to support the management of digital book collections effectively and efficiently.

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