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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 1,106 Documents
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Geospatial Mapping of Kurdistan Region Water Bodies Using Google Maps for Agricultural and Climate Resilience Planning Haje, Umran Abdullah; Abdulrahman, Akam Aziz; Ahmed, Halgurd Rasul
The Indonesian Journal of Computer Science Vol. 14 No. 5 (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.v14i5.5015

Abstract

Lakes, ponds, and reservoirs are essential for water security, agriculture, and climate adaptation in the Kurdistan Region of Iraq. This study presents the first region-specific, field-verified dataset of surface water bodies whose boundaries and KurdishEnglish names have been added to Google Maps, where they were previously absent or incomplete. Using high-resolution satellite imagery, GPS-based ground surveys, and collaboration with local authorities, we identified and classified 31 water bodies (dams, lakes, and seasonal ponds) across the five governorates of Erbil, Sulaymaniyah, Duhok, Kirkuk, and Halabja. These water features are now openly accessible in Google Maps with dual-language labels and delineated outlines. By filling long-standing data gaps, the dataset enhances visibility of local water resources, supports irrigation planning, flood risk assessment, and environmental protection, and provides a replicable model for other data-poor and climate-stressed regions. This integration of modern geospatial methods with traditional knowledge demonstrates a practical step toward sustainable water management and climate resilience in semi-arid environments.
A Comparative Study of Multi-Class Classification Based on Imbalanced Data: A Review Abdulkareem, Rojan; Abdulazeez , Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 14 No. 5 (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.v14i5.5020

Abstract

Classification of unbalanced multiclass datasets is still a major challenge in machine learning in many fields of applications, including medical diagnostics, fraud detection, and picture classification, where minority classes are the most crucial, but at the same time under-represented. Classical classification algorithms designed for balanced data tend to overfit the majority classes deeming a large number of minority classes misclassified and, as a result, compromising the model's performance. This review covers the main state-of-the-art techniques for class imbalance problems including under-sampling and over-sampling techniques, ensemble approaches, cost-sensitive learning, and producing synthetic data via SMOTE (synthetic minority oversampling technique). Recently, GANs (Generative Adversarial Networks) have also been employed to generate synthetic data, specifically valuable for complex datasets where realistic data augmentation is needed. Each of these techniques is analyzed in terms of their capability of dealing with imbalanced data through conventional metrics such as accuracy and specific metrics for imbalanced datasets such as F1-score, G-mean, and others. Recent advancements, such as hybrid approaches and learning from deep learning models are also discussed as viable solutions given the complexities associated with big data (high dimensional and large) and their corresponding models. Such comparative analysis should facilitate the construction of more robust models that handle complex data in modern applications.
Multi-Level Stress Classification Using the Electroencephalogram Based on Mental Load Tasks Alajali, Walaa
The Indonesian Journal of Computer Science Vol. 14 No. 5 (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.v14i5.5022

Abstract

Stress is considered one of the major global health issues contributing to cardiovascular disease and depression among other disorders. This study examines the amounts of stress and performance on tasks using electroencephalogram (EEG) data and machine learning. Raw EEG data is preprocessed to remove noise and segment epochs. Empirical Mode Decomposition (EMD) is followed by Butterfly Optimization Algorithm (BOA) for feature extraction and dimensionality reduction. Five machine learning classifiers (SVM, Naive Bayes, Random Forest, KNN, Decision Tree) classify four levels of stress (neutral, low, medium, and high) based on cognitive load during two tasks: the Stroop color-word task and an arithmetic task. Results indicate the Naive Bayes classifiers for the Stroop and arithmetic tasks had accuracies of 98.82% and 98.87% respectively, while the SVM classifier achieved 99.02% accuracy for both tasks combined. Such results attest to the growing interest and application of machine learning on EEG data for mental health monitoring and the possible enhancement of task performance. .
A Facial Expression Prediction Based on Pre-Trained ResNet50 and SVM Ihsan, Rasheed; Abdulazeez , Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 14 No. 5 (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.v14i5.5005

Abstract

Facial expression prediction has become a vital area in computer vision, with applications spanning healthcare, security, and human-computer interaction. This study proposes a robust system for binary facial expression prediction using a combination of classical computer vision techniques and deep learning. The system employs the Haar Cascade algorithm for face detection and ResNet50, a 50-layer deep residual network, for feature extraction. Support Vector Machines (SVM) with a radial basis function kernel are used for classification. Using the 4,000 tagged images from the GENKI dataset, preprocessing and data augmentation improved the model's capacity for generalization. Experimental results demonstrate the system’s effectiveness, achieving a test accuracy of 94.65%. The robust integration of classical and modern techniques ensures computational efficiency while maintaining high performance. For real-world applications, this method provides a scalable solution that tackles issues including lighting fluctuation, position, and expression variation.
Developing a Campus Blended Learning Framework to Improve Adoption in Higher Educational Institutions in Southwestern Uganda Kabarungi, Moreen; Ntwari Richard; Annabella Habinka Ejiri; Kawuma Simon
The Indonesian Journal of Computer Science Vol. 14 No. 5 (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.v14i5.5026

Abstract

Despite the potential benefits of blended learning in higher education, adoption rates in Ugandan universities remain critically low at 29.1%, with significant barriers including inadequate infrastructure, limited institutional support, and mental health challenges affecting both educators and students. This study aimed to develop a Campus Blended Learning Framework (CBLF) to improve blended learning adoption in higher educational institutions in southwestern Uganda. A Design Science Research methodology was employed, incorporating both quantitative and qualitative approaches. Data were collected from three universities: Mbarara University of Science and Technology (MUST), Bishop Stuart University (BSU), and Kabale University (KAB). A total of 1,495 participants (1,051 students and 444 staff members) were surveyed using structured questionnaires based on the Complex Adaptive Blended Learning Framework (CABLF). Ten experts participated in qualitative interviews to evaluate the framework's usability and acceptability. The study identified six critical components for effective blended learning adoption: pedagogy, infrastructure, content, assessment, support, and mental health. Mental health emerged as a significant factor influencing all other components. The proposed CBLF integrates these elements while addressing the unique contextual challenges of developing countries. The Campus Blended Learning Framework provides a comprehensive approach to addressing low adoption rates of blended learning in southwestern Uganda's higher educational institutions. The framework's emphasis on mental health support and contextual adaptation makes it particularly suitable for developing country contexts.
Analisis Faktor Keberlanjutan Pemanfaatan Chatbot Akademik pada Perguruan Tinggi Nabarian, Tifanny; Ali Akbar
The Indonesian Journal of Computer Science Vol. 14 No. 5 (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.v14i5.5033

Abstract

This study examines the factors influencing the continued use of chatbot use as an academic information platform and identifies key factors contributing to its utilization. Data were collected through interviews and questionnaires, then analyse using the PLS-SEM method with Adanco software. The results show that System Quality has the strongest influence on Perceived Ease of Use (0.5326), which subsequently has a positive impact on Continued Use (0.4816). E-Service Quality also significantly affects User Satisfaction (0.3516). However, Information Quality and Perceived Usefulness show low influence on *User Satisfaction (0.2234) and Continued Use (0.1354), respectively. In terms of reliability, the constructs Continued Use (0.9147), E-Service Quality (0.9203), and Perceived Usefulness (0.8866) demonstrate strong measurement consistency, while System Quality (0.6363) requires improvement. The Q² analysis indicates that the model has good predictive relevance, with Continued Use (0.4349) emerging as the dominant variable
Multiclass Regression for Facial Beauty Prediction Based on Deep Learning Using SCUT-B 5500 Haji, Vaman; Abdulazeez, Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 14 No. 6 (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.v14i6.5008

Abstract

FBP, that is, facial beauty prediction, is a fundamental procedure of how beautiful a face person perceives, just like human beings. The challenge focuses on systems that can assess facial features and provide ratings that align with human perceptions of attractiveness. In this paper, we investigate the usage of deep learning techniques using ResNet18 models for predicting beauty of a face using SCUT-B 5500 dataset and share our findings. In the last ten years machine recognition and scoring of attractiveness has developed into a new field through the use of artificial intelligence. We present our exploratory research on constructing a robust model based on a dataset containing 5500 annotated frontal images ranked according to perceived beauty. Multi-task transfer learning was employed to improve the model performance and address the issue of limited data. Our ResNet18 model had an impressive accuracy of over 91% on predicting beauty ratings. Furthermore, this study not only contributes to the field of facial beauty prediction, but it also has the potential to be implemented in multiple fields such as social networks, dating applications, personalized ads.
Hybrid Transfer Learning Model for Facial Attractiveness Prediction Hawar Bahzad Ahmad; Abdulazeez , Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 14 No. 6 (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.v14i6.5018

Abstract

Prediction of facial attractiveness greatly depends on the subjective terminology applied according to the diverse cultural, social and psychological considerations. This task is important for applications in many fields, such as aesthetics, entertainment, wardrobe recommendations, etc., and requires accurate and robust models. Current methods predominantly adopt a single model, which is unable to learn the diverse attributes that can influence the quality of facial beauty. In order to overcome these challenges, this study proposes a hybrid transfer learning framework for feature extraction and prediction that combines ResNet50 and InceptionV3. In this methodology, Multi-task Cascaded Convolutional Networks (MTCNN) is used for accurate face detection and preprocessing, then features extraction is done using pretrained ResNet50 and InceptionV3 architectures. The features extracted are then normalized and fused together and passed through a dense classification layer with application of dropouts and regularization in order to make the model robust. The CelebA dataset was used to train the model, utilizing class weights to account for imbalanced data and callbacks to optimize performance. Test accuracy and F1 Score of the proposed model is found to be 83.58% and 0.8384 respectively, which shows good generalization on unseen data. The validation frames the performance of the hybrid framework which leverages the complementary strengths of multiple CNNs, and thus provides robust performance.
An Industry 5.0 Compliant Human-Robot Collaboration Digital Twin Framework for African Medium Scale Enterprises Fasina, Ebun-Oluwa Phillip; Sawyerr, Babatunde Alade; Akinola-Taiwo, Kayodele; Murainah, Abdul-Azeez; Ojiako, Chika Perpetua
The Indonesian Journal of Computer Science Vol. 14 No. 6 (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.v14i6.5023

Abstract

Industry 5.0 emphasizes human-robot collaboration, where Digital Twins (DTs) connect physical and digital operations for efficient, flexible work. Existing DT frameworks often focus on full-system autonomy or prediction, overlooking structured, task-level coordination between Human and Robot Digital Twins (HDT and RDT). This paper introduces a minimal, modular framework that enables shared task-based collaboration between HDT and RDT agents. Built on the Cross Domain Digital Twin (CDDT) design pattern, it supports real-time, role-specific interaction. The framework provides a scalable foundation for collaborative DT systems aligned with Industry 5.0, offering a practical base for future human–robot coordination research.
Development of an LSTM-Based Power Monitoring and Prediction System for Campus Electrical Facilities Using ESP32 and PM2120 Sholikhah, Evi Nafiatus; Oktavia Rizqi Kurniawan; Dimas Pristovani Riananda; Mustika Kurnia Mayangsari; Rohmad Hadi Handayani
The Indonesian Journal of Computer Science Vol. 14 No. 6 (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.v14i6.5030

Abstract

This study develops a data acquisition system for monitoring, detecting, and forecasting electrical energy consumption to support efficient energy management. Electrical parameters such as voltage, current, and power are measured using a PM2120 power meter via Modbus RTU RS485 and processed by an ESP32 microcontroller. The data are displayed in real-time through a Nextion Human-Machine Interface (HMI) and utilized as input for a Long Short-Term Memory (LSTM) model trained on historical consumption data. Safety features include LED indicators that activate when current reaches 80% of maximum capacity and a buzzer that signals threshold violations. Experimental results demonstrate high prediction accuracy, with RMSE values of 0.38 kW (5.32%) for phase R, 0.47 kW (7.55%) for phase S, and 0.28 kW (5.39%) for phase T. Transmission latency averages two to three seconds, while prediction computation is under 10 seconds. The system effectively reflects consumption trends, making it a reliable decision-support tool for enhancing energy efficiency in small- to medium-scale installations.

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