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All Journal Tadris: Jurnal keguruan dan Ilmu Tarbiyah International Journal of Electronics and Communications Systems
Salam, Fitria Nur Dina
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Computer Science & IT Education Electrical & Electronics Engineering

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Outlier-Aware Clustering for Mapping Computer Science Students’ Career Readiness: A Hybrid DBSCAN–K-Means Approach Eriyani, Nindy Sri; Surianto, Dewi Fatmarani; Nurhidayat; Salam, Fitria Nur Dina; Muliadi
International Journal of Electronics and Communications Systems Vol. 6 No. 1 (2026): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v6i1.28865

Abstract

Career readiness assessment increasingly requires competency-based profiling beyond academic achievement, yet conventional clustering methods remain vulnerable to outliers that distort student classification. This study proposes an outlier-aware hybrid DBSCAN–K-Means framework to map the career readiness of computer science students using eight non-academic competency dimensions. Data were collected from 566 students across 21 Indonesian universities using a validated 35-item questionnaire covering leadership, collaboration, time management, self-directed learning, goal setting, adaptability, problem-solving, and technical skills. Cluster quality was evaluated using the Elbow Method, Silhouette Score, and Davies–Bouldin Index. DBSCAN identified 113 outliers (19.96%), and removing these observations improved clustering performance, increasing the Silhouette Score from 0.292 to 0.326 while reducing the Davies–Bouldin Index from 1.229 to 1.122. The hybrid approach identified four meaningful career readiness profiles, including highly prepared students, students requiring competency development, critically underprepared outliers, and exceptionally high-performing outliers overlooked by conventional clustering. These findings demonstrate that outlier-aware clustering produces more robust competency profiles and provides a replicable analytical framework for evidence-based career development strategies in higher education.
How ChatGPT Usage and AI Over-Reliance Influence Student Integrity: The Moderating Role of Academic Misconduct Salam, Fitria Nur Dina; Rahman, Edi Suhardi; Fakhri, M. Miftach; Soeharto, Soeharto
Tadris: Jurnal Keguruan dan Ilmu Tarbiyah Vol 11 No 2 (2026): Tadris: Jurnal Keguruan dan Ilmu Tarbiyah
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/tadris.v11i2.29449

Abstract

The rapid adoption of generative artificial intelligence in higher education has intensified debates about whether ChatGPT supports responsible learning or weakens academic integrity through over-reliance and misconduct. This study examined the direct effects of ChatGPT usage and AI over-reliance on student integrity and tested academic misconduct as a moderating variable. A quantitative cross-sectional survey was conducted with 571 Indonesian undergraduate students who had experience using ChatGPT or comparable AI tools for academic purposes. Data were collected using a validated 20-item questionnaire measured on a five-point Likert scale and analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM) with 5,000 bootstrap resamples. The results show that purposive ChatGPT usage positively predicts student integrity (β = 0.482, t = 10.419, p < .001), whereas AI over-reliance has a significant negative effect (β = -0.215, t = 3.853, p < .001). Academic misconduct also shows a significant direct path to integrity (β = 0.159, p = .005) and moderates the relationship between AI over-reliance and student integrity (β = 0.247, t = 5.143, p < .001), but it does not significantly moderate the ChatGPT usage-integrity relationship. These findings demonstrate that the ethical consequences of generative AI depend on how students engage with the technology. The study implies that universities should not merely prohibit AI use, but should develop clear AI governance, digital ethics instruction, and assessment designs that encourage critical, transparent, and accountable use of ChatGPT.
Co-Authors Dewi Fatmarani Surianto Edi Suhardi Rahman Eriyani, Nindy Sri M. Miftach Fakhri Muliadi Nurhidayat Soeharto Soeharto