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All Journal Journal of Technology and Informatics (JoTI)
Putra Aditya, Wigananda Firdaus
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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K-Means Algorithm Application for Clustering Recent University Graduates According to Work Readiness Indicators Putra Aditya, Wigananda Firdaus; Agussalim; Rizky Parlika
Journal of Technology and Informatics (JoTI) Vol. 8 No. 1 (2026): Vol. 8 N. 1 (2026)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v8i1.1281

Abstract

Graduate work-readiness segmentation is essential for data-driven career services in universities. This study applies K-Means clustering to tracer-study data using four input indicators: GPA (IPK), TOEFL, soft-skill points (SSKM), and study duration, while employment status and waiting time are treated as external outcomes. Records from 669 graduates (2020–2023) were preprocessed via deduplication, range checks, and z-score standardization. The number of clusters was determined data-driven over K=2–10 using the Elbow Method (SSE) and Davies–Bouldin Index; the optimal K=9 was selected at the DBI minimum. PCA visualization indicated a distinguishable cluster structure. Clusters C0, C3, C5, and C7 exhibited faster transitions (median waiting time 2 months) with high employment proportions (up to ~90%), whereas C2 and C8 showed longer waiting times (≥4 months). Cluster C4 was characterized by the longest study duration and a comparatively lower employment proportion. These results demonstrate that unsupervised learning can reveal actionable readiness segments, supporting targeted interventions (e.g., CV/portfolio clinics, interview practice, structured internships) and providing a foundation for subsequent predictive modeling of graduate outcomes.
Co-Authors Agussalim Rizky Parlika