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Edu Komputika Journal
Published by Universitas Negeri Semarang
ISSN : -     EISSN : 2599297X     DOI : https://doi.org/10.15294/edukom
Core Subject : Education,
Education
Edu Komputika Journal uses Open Journal Systems (OJS) for online journal management in submission, review, copyediting, and publication. Submitted manuscripts are written in English and should follow the style of the Edu Komputika Journal. Manuscripts are original research results, or theoretical/literature study results that have never been published in other journals or are not considered for publication elsewhere. The author should follow all the provisions and processes. Accepted papers will be available online and will be charged a publication fee.
Arjuna Subject : Ilmu Sosial - Pendidikan
Articles 45 Documents
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Artificial Intelligence Based Educational Game Design for Literacy and Numeracy Skills in Elementary School Mathematics Learning Fitria Nur Hasanah; Fitria Eka Wulandari; Mahardika Darmawan Kusuma Wardana; Clarisa Seliya Tamara
Edu Komputika Journal Vol. 13 No. 1 (2026): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v13i1.49228

Abstract

Low literacy and numeracy achievement among elementary school students, particularly in mathematics learning, indicates the need for innovative learning media capable of providing adaptive and engaging learning experiences. Therefore, this study aims to design and evaluate an Artificial Intelligence (AI) based educational game for plane geometry learning to improve elementary school students' literacy and numeracy skills. The study employed the Research and Development (R&D) method using the ADDIE model, consisting of analysis, design, development, implementation, and evaluation. The developed product is an AI based Monopoly educational game that integrates adaptive question generation, automatic feedback, and learning analytics to adjust the difficulty level according to students' performance. The game was validated by two media experts and two mathematics education experts before being implemented in learning. The trial involved 25 fifth-grade students at Muhammadiyah 1 Labschool Umsida Elementary School. Data were collected through expert validation, classroom observation, and literacy and numeracy pretests and posttests. The validation results indicated that the developed game was highly feasible for classroom use. During implementation, students demonstrated high enthusiasm and active engagement in learning activities. The average pretest score increased from 65.64 to 80.16 in the posttest. The data met the normality assumption (pretest = 0.253; posttest = 0.567; p > 0.05). Furthermore, the Paired Sample t-test revealed a significant improvement in students' literacy and numeracy skills (p = 0.000), while the average N-Gain score of 40.07% indicated a moderate level of effectiveness. These findings demonstrate that the proposed AI based educational game is a feasible and effective learning medium for enhancing elementary students' literacy and numeracy skills while supporting Sustainable Development Goal 4 on Quality Education.
Performance Analysis of Convolutional Neural Network Methods Using VGG16 and YOLOv8n for Bottle Waste Sorting in Computer Vision Applications Akbar Sujiwa; Nailul Hasan; Fajar Timur; Reffany Choiru Rizkiarna
Edu Komputika Journal Vol. 13 No. 1 (2026): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v13i1.42314

Abstract

This study examines the performance of two artificial intelligence models—VGG16 and Yolov8n—in sorting plastic bottles using computer vision.  The objective is to individually assess the classification and detection performance of these models with constrained computational resources and training data.  The dataset consists of 300 original images for two classes (bottle and other), and is split into 210 training, 45 validation and 45 test images.  The images were taken in different lighting conditions and orientations to simulate the real waste sorting situation . Both models were trained and evaluated on CPU based hardware to simulate a constrained computing environment.  The VGG16 was evaluated using classification metrics like accuracy, precision, recall, and F1-score, while the YOLOv8n was evaluated using object detection metrics like precision, recall, F1-score, mAP@0.5, and frame processing speed (FPS). The accuracy of the VGG16 model was 91% on the test set. The mAP@0.5 of YOLOv8n was 0.56 with an average processing speed of 47.12 FPS, while the average processing speed of VGG16 was 5.17 FPS. These results indicate that VGG16 had a good performance on image-level classification, while YOLOv8n had a higher processing efficiency and better object-localization performance in the studied conditions. Further evaluation on embedded hardware is required to establish the suitability of YOLOv8n for practical real-time waste-sorting applications.
Multimodal Learning Interaction in Multimedia Laboratory Environments: An Empirical Framework for Infrastructure Development Jarir; Akbar Juliansyah; Fitri Ramdani; Wirawan Putrayadi; Edy Haryanto
Edu Komputika Journal Vol. 13 No. 1 (2026): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v13i1.50379

Abstract

Multimedia-supported laboratory environments play an important role in enhancing learning engagement, conceptual understanding, and practical competence in higher education. However, limited empirical evidence exists regarding the relative contributions of the visual, auditory, and kinesthetic interaction dimensions to laboratory learning effectiveness. This study aimed to develop an empirical multimodal interaction framework for multimedia laboratory infrastructure development by examining the influence of these three dimensions on perceived learning effectiveness. A mixed-methods sequential explanatory design was employed. Quantitative data were collected from 101 university students enrolled in PTI Practicum, PTI Non-Practicum, and BK courses. Multiple regression analysis was conducted using responses from 58 students with direct laboratory practicum experience. Qualitative support was obtained through expert validation involving 14 lecturers. The data were analyzed using descriptive statistics, one-way ANOVA, Tukey’s post hoc test, multiple linear regression, and expert triangulation. The descriptive analysis revealed that the kinesthetic dimension had the highest mean score, indicating a stronger preference for hands-on and experiential learning activities. However, the regression analysis demonstrated that the auditory dimension made the strongest contribution to perceived laboratory learning effectiveness, followed by the visual and kinesthetic dimensions. The regression model explained 64.8% of the variance in perceived learning effectiveness, indicating substantial explanatory power. The standardized regression coefficients were subsequently normalized and translated into infrastructure development priorities, forming the basis of the proposed empirical framework. Lecturer validation further emphasized the importance of experiential learning and practical engagement in laboratory environments, while highlighting the complementary roles of instructional communication and multimedia support in enhancing learning effectiveness. These findings suggest that effective multimedia laboratory environments should integrate instructional communication, multimedia visualization, and experiential learning facilities in a balanced manner, recognizing their complementary roles within a multimodal learning ecosystem. This study contributes an empirical framework to support adaptive multimedia laboratory infrastructure development and instructional design in higher education. In practice, the proposed framework may assist laboratory managers and higher education institutions in prioritizing instructional communication, multimedia visualization, and experiential learning facilities according to empirically identified learning interaction needs.
Enhancing Adolescent Smartphone Addiction Screening Using a Weighted Radial Basis Function Neural Network Mochammad Anshori; Rosyidah Alfitri
Edu Komputika Journal Vol. 13 No. 1 (2026): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v13i1.36235

Abstract

Smartphone addiction among adolescents threatens mental health, sleep, academic performance, and social functioning, creating an urgent need for objective, scalable screening tools. This study evaluates whether a weighted RBFNN (Radial Basis Function Neural Network) with learnable per-feature weights can improve early identification of smartphone addiction in adolescents. Using a questionnaire-derived dataset of 394 participants, features were standardized, hidden-unit centers were initialized with k-means clustering, and both conventional and weighted RBFNN architectures were trained and compared under stratified ten-fold cross-validation while sweeping the number of hidden units. Models were assessed by accuracy, precision, recall, F-measure, area under the receiver operating characteristic curve, and computation time. The weighted RBFNN consistently outperformed the conventional variant and previously reported baselines, achieving a mean cross-validation accuracy 97.99% across all cluster settings, with best performance of 98.48% (at cluster 6) and an area under the curve near 0.989, with very low false positive and false negative rates and reduced computation time at larger cluster settings. Learnable feature weighting mitigated noisy predictors and improved generalization, while results underscored sensitivity to cluster-count selection and preprocessing choices. These findings indicate that a weighted RBFNN is a promising, efficient approach for automated adolescent smartphone addiction screening.
Designing Summary Tables to Optimize Performance in Relational Databases: An Empirical Approach Edi Widodo; Kartika Imam Santoso; Wawan Setiawan
Edu Komputika Journal Vol. 13 No. 1 (2026): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v13i1.41416

Abstract

The performance of transactional applications involving databases will decline over time due to the increasing amount of stored data and the continuously rising volume of transactions. This research proposes a summary table design method that integrates table partitioning techniques and data aggregation to improve query efficiency without sacrificing the consistency and accuracy of the stored data. We tested this method using 30,275 transaction records on Google Cloud infrastructure and MySQL Server 8.0, demonstrating a data processing speed increase of up to 92.36% compared to conventional table designs. The research results show that this method not only accelerates data processing but also simplifies data management in relational database management systems (RDBMS). This method is relevant for transactional database applications with high transaction loads, such as the financial and e-commerce sectors. By combining the physical table design architecture and efficient query processing, this research contributes to the development of more scalable, reliable, and suitable database designs for high workload scenarios.

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