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All Journal Journal of Applied Data Sciences Bulletin of Social Informatics Theory and Application
Kurubacak, Gulsun
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Social Sciences

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Internship and Entrepreneurship in computer science Alfan, Muhammad Bahauddin; Sangjaya, Sabri; Rusdiansyah, Muhammad Rizal; Arfabuma, Fandi M; Kurubacak, Gulsun
Bulletin of Social Informatics Theory and Application Vol. 7 No. 1 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v7i1.206

Abstract

Graduated students of State University of Malang majoring in Computer Science have required to do an internship in industry. Generally, the chosen private industry is in the form of a startup such as learning media applications and games. In this case, the authors would like to conduct research related to the impact of an internship program and another Computer Science subject related to entrepreneurial skills. It is expected that this research can improve the quality of student graduates in order to have an entrepreneurial spirit.
Clustering-Based Adaptive UX in E-Learning Systems: Aligning Microservices with the 4C Framework Belluano, Poetri Lestari Lokapitasari; Patmanthara, Syaad; Ashar, Muhammad; Kurniawan, Fachrul; Kurubacak, Gulsun
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.884

Abstract

This study introduces a clustering-driven adaptive User Experience (UX) architecture for e-learning systems, aligning machine learning segmentation with the 21st-century 4C educational framework (critical thinking, communication, collaboration, creativity). The objective is to dynamically personalize digital learning interactions through a microservices architecture responsive to users' UX profiles. A quantitative survey was conducted involving 50 active users of Shopee and Tokopedia, whose interaction feedback was mapped using the User Experience Questionnaire (UEQ). Three unsupervised clustering techniques—KMeans, Agglomerative, and DBSCAN—were compared. KMeans outperformed the others with a silhouette score of 0.157, compared to 0.146 for Agglomerative and −0.017 for DBSCAN, identifying three meaningful clusters representing high, medium, and low UX proficiency. A one-way ANOVA test confirmed statistically significant differences (p 0.01) among the clusters in dimensions such as error clarity, support responsiveness, and user confidence. These UX profiles were then mapped to individualized microservices: Cluster 0 received autonomous content with minimal support, Cluster 1 was offered guided prompts, and Cluster 2 was provided with simplified interfaces and proactive assistance. Each cluster was aligned with specific 4C competencies to ensure pedagogical relevance. The proposed architecture, built with gRPC-based microservices, enabled asynchronous, low-latency personalization based on user cluster membership. The novelty of this research lies in its dual alignment—technological (microservices + machine learning) and educational (4C competency mapping)—to construct a scalable and responsive e-learning environment. The system design, although validated through simulation, demonstrates a practical foundation for future deployment in platforms like Moodle or OpenEdX. By linking behavioral UX clustering to pedagogical intervention strategies, this study offers a model for adaptive, data-informed instructional systems that are both scalable and learner-centered.
Co-Authors Alfan, Muhammad Bahauddin Arfabuma, Fandi M Ashar, Muhammad Fachrul Kurniawan Lokapitasari Belluano, Poetri Lestari Rusdiansyah, Muhammad Rizal Sangjaya, Sabri Syaad Patmanthara