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All Journal Bulletin of Social Informatics Theory and Application
Alfan, Muhammad Bahauddin
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Computer Science & IT 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.
Analyzing interaction and player experience of game based learning using feature importance based clustering Alfan, Muhammad Bahauddin; Yuhana, Umi Laili; Herumurti, Darlis
Bulletin of Social Informatics Theory and Application Vol. 8 No. 2 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i2.772

Abstract

This study explores the dynamics of the gaming experience and its impact on learning efficiency through digital game-based learning (DGBL). Leveraging the Fingerstroke Level Model-GOMS (FLM-GOMS) for interaction analysis and the In-Game Experience Questionnaire (iGEQ) for player experience assessment, we examine the relationship between game-play mechanics and educational outcomes. Our research incorporates a comprehensive dataset, focusing on 40 features encompassing motivation and efficiency outcomes. Through clustering, we identify distinct player groups exhibiting signif-icant variations in efficiency outcomes and game experiences. We utilized the feature selection technique to identify the crucial features that differentiate groups of students who excel in implementing DGBL from those who do not. Through the Random Forest feature importance method, we have found that FLM-GOMS features and positive player in-game feedback play a pivotal role in determining the effectiveness of DGBL.
Co-Authors Arfabuma, Fandi M Darlis Herumurti Kurubacak, Gulsun Rusdiansyah, Muhammad Rizal Sangjaya, Sabri Yuhana, Umi Laili