Predicting Network Engineering Graduate Employability: A Multi-Factor Analysis of Internships and Motivation

1 Program Studi Pendidikan Teknologi dan Kejuruan, Pascasarjana, Universitas Negeri Makassar, Indonesia
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Bridging the gap between vocational education and labor market demands remains a critical challenge under the Kurikulum Merdeka, particularly in technical specializations like Computer and Network Engineering (TKJ). This study examines the multi-factor influence of internship duration (X1), industrial supervisory quality (X2), and student motivation (X3) on graduate job readiness (Y) among TKJ students in Gowa Regency, Indonesia. Utilizing a quantitative ex-post facto design, a proportional random sample of 175 12th-grade students from three public vocational high schools was surveyed following their industrial placements during the 2025/2026 academic year. Data collected via validated four-point Likert questionnaires (Cronbach’s α=0.746–0.812) were analyzed using multiple linear regression in IBM SPSS 25. Results revealed that internship duration (β=0.359, p<0.001), supervisory quality (β=0.292, p<0.001), and internal motivation (β=0.149, p=0.020) significantly and simultaneously predicted job readiness (F=42.389, p<0.001), accounting for 42.6% of total variance (R^2=0.426). Partial regression identified internship duration as the primary driver (R^2=0.339), followed by mentorship quality (R^2=0.302) and motivation (R^2=0.147). Ultimately, technical employability heavily relies on prolonged real-world experiential learning and high-touch mentorship, urging stakeholders to standardize industrial mentorship protocols and optimize placement durations.

Nurhikmah, I., Rahmah, U., & Amiruddin, A. (2026). Predicting Network Engineering Graduate Employability: A Multi-Factor Analysis of Internships and Motivation. ETDC: Indonesian Journal of Research and Educational Review , 6(1), 384–394. https://doi.org/10.51574/ijrer.v6i1.5542

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