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p-Index From 2021 - 2026
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This Author published in this journals
All Journal Journal of Information Systems and Informatics Journal of Applied Engineering and Technological Science (JAETS)
Ranty Deviana Siahaan
Del Institute of Technology
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Enhancing Student Motivation in Programming Education Through N-EGM-Based Gamification: A Mobile Application Approach Ranty Deviana Siahaan; Boy Martahan Sitorus; Emely Angelica Lestari; Enrico Hezkiel Sirait
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.8721

Abstract

Programming education continues to face persistent challenges, including high cognitive load, abstract syntax complexity, and declining intrinsic motivation among students. Although gamification has been widely adopted to address these issues, existing frameworks such as MDA and Octalysis lack structured personalization and socialization mechanisms tailored specifically for programming learning contexts. This study proposes a mobile-based programming learning application designed using the Newton Enhanced Gamification Model (N-EGM) and empirically evaluates its effectiveness through the Hedonic Motivation System Adoption Model (HMSAM). The study involved 116 undergraduate Informatics students selected using purposive sampling. Data were collected using validated questionnaire instruments and analyzed through descriptive statistics, reliability testing, multiple regression analysis, multicollinearity diagnostics, and common method bias detection using SPSS. The findings indicate that gamification elements mapped through the N-EGM framework explain 99.1% of the variance in student motivation and 98.9% of the variance in engagement (p < 0.001). Leaderboard and Objective elements were the strongest predictors of motivation, while Economy and Quest significantly influenced immersion. Multicollinearity diagnostics confirmed acceptable VIF values (< 5), and Harman’s single-factor test indicated no critical common method bias. Theoretically, this study contributes by integrating a structured multi-layer gamification framework with a hedonic adoption model in a programming education context. Practically, it provides a systematic design blueprint for implementing adaptive and socially integrated gamification strategies in mobile STEM learning environments.
Performance Trade-Off Analysis of Faster R-CNN with Grid-Based Histogram for Student Face Detection Arie Satia Dharma; Herimanto; Ranty Deviana Siahaan; Lamboy Albertson Sirait; Luna Sweeta Pangaribuan
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1681

Abstract

Face detection supports applications such as security, identity management, human–computer interaction, and academic information systems. Faster R-CNN is known for strong detection accuracy, but its Region Proposal Network produces many candidate boxes, including background proposals, which may increase processing cost. This study replaces the conventional anchor-generation process with a Grid-Based Histogram method to improve inference efficiency while retaining competitive detection performance. Experiments were conducted on 1132 annotated student profile images collected from the Campus Information System of Institut Teknologi Del. The standard and modified models were evaluated using mean Intersection over Union (IoU), mean Average Precision at IoU 0.50 (mAP@50), and average latency per image with an inference batch size of one. Standard Faster R-CNN achieved an IoU of 0.7595, an mAP@50 of 0.9818, and a latency of 0.170 s per image. The modified model obtained an IoU of 0.7519, an mAP@50 of 0.9719, and a latency of 0.147 s per image. Thus, latency decreased by about 13.53%, with small reductions in localization and detection accuracy. The novelty of this study lies in applying a Grid-Based Histogram as a lightweight replacement for conventional anchor generation in Faster R-CNN, resulting in a preliminary speed–accuracy trade-off rather than an overall performance improvement.
Co-Authors Arie Satia Dharma Boy Martahan Sitorus Emely Angelica Lestari Enrico Hezkiel Sirait Herimanto Lamboy Albertson Sirait Luna Sweeta Pangaribuan