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Journal of Computer Science and Research
Published by Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer Provinsi Sumatera Utara
ISSN : -     EISSN : 29862337     DOI : -
Core Subject : Economy, Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Library & Information Science
Journal of Computer Science and Research (JoCoSiR) is aimed to publish research articles on theoretical foundations of information and computation, and of practical techniques for their implementation and application in computer systems. Journal of Computer Science and Research (JoCoSiR) published quarterly and is a peer reviewed journal covers the latest and most compelling research of the time. Journal of Computer Science and Research (JoCoSiR) is managed and published by APTIKOM Wilayah 1 Sumatera Utara.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
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Search results for , issue "Vol. 3 No. 1 (2025): Jan: Computer Science" : 5 Documents clear
Model Predictive Analysis of Performance in Training and Course Institutions Using Naive Bayes and K-Means Clustering Eko Budianto; Muhammad Iqbal
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 1 (2025): Jan: Computer Science
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v3i1.68

Abstract

The performance of course and training institutions (LKP) is a crucial factor in determining the quality of non-formal education in Indonesia. Performance assessments are currently conducted manually using the National Accreditation Board for Non-Formal Education (BAN-PNF) assessment instrument, which is time-consuming and prone to subjectivity. This research aims to develop a predictive analysis model for the performance of course and training institutions using a combination of the Naive Bayes and K-Means Clustering methods. The K-Means Clustering method is used to group institutions based on similar characteristics across key variables such as trainers, infrastructure, curriculum, management, and graduate outcomes. These clustering results are then used as additional features for the Naive Bayes classification model to predict performance categories (high, medium, or low). Testing of 150 institutions' data showed a predictive accuracy of 89.2%, with three main clusters representing high-, medium-, and low-performing institutions. This model has the potential to become a data-driven tool for governments and institutions to conduct performance evaluations quickly, objectively, and adaptively to changes in training data.
Classification of Student Activity Status Using Machine Learning Algorithms at Royal University Hermawan, Rudi; Muhammad Iqbal
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 1 (2025): Jan: Computer Science
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v3i1.69

Abstract

Inactivity is a significant challenge that impacts academic performance, retention rates, and the operational effectiveness of higher education institutions. Royal University faces an urgent need to identify students at risk of becoming inactive early, so that academic interventions can be carried out appropriately and effectively. This study aims to develop a classification model for student inactivity status (Active or Passive) using a machine learning approach, by testing three main algorithms: Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The dataset used consists of 642 student entries, including academic information such as Grade Point Average (GPA), total credits taken, attendance percentage, number of courses per semester, and semester level. The methodology steps include data cleaning and transformation, splitting the dataset into 80% training data and 20% testing data using a random sampling method ( train_test_split with random_state = 42), model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The experimental results show that DT and SVM achieve the highest accuracy of 98.44%, with maximum precision in predicting active students, while RF excels in recall (0.96), making it more effective in detecting active students at risk of being missed. Feature importance analysis reveals that GPA and attendance are the most determining factors in predicting student active status, while the number of courses, credits taken, and semester level have a lower additional influence. The primary contribution of this research is the provision of an accurate and practically applicable classification model, enabling universities to conduct automated student monitoring, proactive academic interventions, and data-driven decision-making. Implementing this model in academic information systems can improve the effectiveness of advising programs, reduce the risk of student inactivity , and support efforts to improve retention and graduate quality. This research also emphasizes the importance of contextual features in improving prediction accuracy and provides insights that can be leveraged for the development of data-driven academic strategies
The Best Caregiver at SOS Children’s Villages Using a Decision Support System Muhammad Iqbal; Syahputri, Maulisa
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 1 (2025): Jan: Computer Science
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v3i1.67

Abstract

This study focuses on the development and implementation of a Decision Support System (DSS) designed to determine the best caregiver at SOS Children’s Villages. The main objective is to enhance efficiency and objectivity in the decision-making process related to caregiver performance evaluation. The research methodology includes collecting caregiver performance data, analyzing organizational needs, and applying an appropriate decision-making model. The DSS developed in this study utilizes Artificial Intelligence (AI) techniques to process and analyze performance data, generate performance scores, and identify the best caregiver based on predetermined criteria. The results show that the implementation of the DSS improves the objectivity of performance evaluations and provides significant support in the decision-making process. With this system, the organization is expected to better identify and optimize the potential of each caregiver, thereby increasing productivity and strengthening the competitiveness of SOS Children’s Villages in Medan. The collected data is processed and evaluated using the Simple Additive Weighting (SAW) method. The results are presented in the form of rankings and scores for each caregiver, facilitating a more accurate and transparent decision-making process. This study is expected to contribute positively to improving the efficiency and effectiveness of human resource management at SOS Children’s Villages.
Smarter School Labs: Fast and Accurate Anomaly Detection Using Lightweight CNN Technology Marbun, Ramlan; Iqbal, Muhammad
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 1 (2025): Jan: Computer Science
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v3i1.70

Abstract

This study proposes a lightweight convolutional neural network (CNN) model for anomaly detection in school computer laboratories, aiming to enhance operational reliability and cybersecurity awareness. Real-time event logs were collected from 20 computers (PC01–PC20) at Santo Nicholas School with slight variations in CPU, RAM, and network behavior to simulate real-world heterogeneity. After preprocessing and normalization, the merged dataset contained over 10,000 log entries labeled as normal or anomalous. The proposed lightweight CNN achieved 92.23% F1-score, 91.80% accuracy, and a false positive rate (FPR) of 18.47%, demonstrating a balance between detection precision and computational efficiency. Comparative evaluation shows that this architecture performs competitively while requiring fewer parameters and lower inference latency than conventional CNNs. The results highlight the suitability of the proposed model for deployment in low-resource educational environments, supporting early anomaly detection and preventive maintenance. Future research will explore cross-domain generalization and lightweight deployment through edge-AI integration.
Multivariate Analysis and Neural Network-Based Prediction of Compression Molding Behavior in Plantain–Bamboo Fiber Reinforced HDPE Composites Obiora Jeremiah Obiafudo; Joseph Achebo; Kessington Obahiagbon; Frank. O. Uwoghiren; Callistus Nkemjika Chukwu
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 1 (2025): Jan: Computer Science
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The compression molding behavior of plantain–bamboo fiber reinforced high-density polyethylene (HDPE) composites was studied through an integrated multivariate analysis and neural network modelling framework. The study utilized materials for fiber extraction and composite production, including water, alkali (NaOH), acetic acid, acetic anhydride, maleic anhydride grafted PE, hydrogen peroxide, hypochlorite, and caustic soda. The composite matrix was high-density polyethylene with density (0.96 g/cm³), reinforced with activated plantain and bamboo fibers. Methods involved mechanical extraction, chemical treatment using alkali solutions, neutralization, bleaching, and stabilization. Fibers were oven-dried, milled, and sieved to (75 μm) before composite formation. Process variables such as fiber fraction (10–50%) and temperature (150–190°C) informed the experimental design. A feed-forward neural network (5-5-5) was used for modelling system performance. The multivariate analysis used predictive neural network models to study combined process-variable effects during compression molding. Interaction plots were generated by varying fiber volume fraction (VF) against other variables. Results showed that high yield stress near (90 MPa) occurred at low VF (10–20%) when bamboo fiber ratio (BFR) was maintained at (40–60%). Pure plantain fiber outperformed pure bamboo at (0) and (1.0 BFR). Optimal molding temperature ranged (166–174°C), producing high yield stress even at VF (10%). At low temperatures (150°C) and VF (30%), yield stress exceeded (80 MPa). Maximum strength required holding times (>17 min) and low clamping force (<1900 N). Neural network predictions aligned closely with experimental data, demonstrating strong predictive reliability. This integrated statistical–computational approach provides valuable insights for optimizing natural fiber composite manufacturing and reducing experimental cost.

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