This study aimed to analyze the influence of fieldwork, hard skills, and self-efficacy on the work readiness of vocational high school students in the Computer and Network Engineering Program in Gowa Regency, while also addressing the research gap regarding the integration of these three variables into a comprehensive model. This ex post facto study used a quantitative approach. This study was conducted at vocational high schools in Gowa Regency, namely, SMK Negeri 1 Gowa, SMK Negeri 2 Gowa, SMK Negeri 4 Gowa, and SMK Negeri 5 Gowa. The subjects of this study were 389 twelfth-grade TKJ students at vocational high schools in Gowa Regency, with a sample of 197 respondents determined using the Slovin formula with a 5% precision level and proportional random sampling. Data were collected through questionnaires and documentation and then analyzed using descriptive statistics, classical assumptions, simple regression, and multiple regression with the assistance of SPSS 24. The research findings indicate that, in part, the field work practice variable (X1) has a significant effect on work readiness (Y) with a significance value of 0.000 < 0.05 and an effect size of 23.2%; the hard skills variable (X2) has a significant effect on work readiness (Y) with a significance value of 0.000 < 0.05 and an effect size of 34.5%; and the self-efficacy variable (X3) has a significant effect on work readiness (Y) with a significance value of 0.000 < 0.05 and an effect size of 62.5%. Simultaneously, the three variables—field work practice, hard skills, and self-efficacy—significantly influenced work readiness, with a significance value of 0.000 < 0.05 and an effect size of 70.8%, while the remaining 29.2% was influenced by other factors outside this research model. The implications of this study highlight the importance of optimizing internship implementation, strengthening hard skills, and developing self-efficacy in an integrated manner to enhance students’ employability. The originality of this study lies in the integration of these three variables into a single model within the specific context of Computer and Network Engineering, an area that has not yet been extensively studied in this way.
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