Integrating IoT-Based Predictive Maintenance into Motorcycle Engineering Instruction: Effects on Vocational Students’ Diagnostic Competence

Abstract

This study investigates the application of Internet of Things (IoT) technology in predictive maintenance learning within Motorcycle Engineering courses at SMK Negeri 1 Kandis and its impact on improving student competence in the context of Industry 4.0. A quasi-experimental one-group pretest–posttest design was employed involving 64 students across Grades X, XI, and XII. Students participated in learning activities using IoT-based trainer media integrated with vehicle technical sensors. Data were collected through pretests, posttests, observations, and questionnaires, and analyzed using N-gain, paired t-tests, and effect size (Cohen’s d). Substantial improvements were observed across all grade levels. In Grade X, the mean score increased from 66 (pretest) to 95 (posttest) with Cohen’s d > 3.7. In Grade XI, scores improved from 65 to 95 (d > 27.7), and in Grade XII from 67 to 97 (d > 37.6). The overall N-gain of 0.90 indicated a high level of improvement. Paired t-tests revealed statistically significant differences between pretest and posttest scores (p < 0.05), demonstrating enhanced conceptual understanding, analytical skills, and readiness to meet automotive industry challenges. The findings confirm that integrating IoT technology into vocational learning effectively enhances student competence and aligns with Industry 4.0 demands. However, the absence of a control group and the short intervention duration limit generalizability. Future research should employ longitudinal designs and larger samples to validate these results and strengthen curricular integration strategies.