STRENGTHENING MACHINE LEARNING COMPETENCE THROUGH LEAF IMAGE ANALYSIS IN VOCATIONAL EDUCATION
Keywords:
Machine Learning, Computer Vision, leaf Image Classification, Competency Strengthening, Vocational EducationAbstract
The rapid advancement of artificial intelligence demands strong Machine Learning and Computer Vision competencies in vocational education, particularly within Software Engineering study programs. Although students at SMK Negeri 2 Cimahi possess adequate programming foundations, they lack hands-on experience in building a complete end-to-end ML pipeline, creating a gap between conceptual knowledge and applied technical skill. This community service activity addresses that gap by strengthening participants' conceptual understanding while transferring it into practical competence for 38 eleventh-grade Software Engineering students, through a fully facilitated learning approach delivered via Google Colab. The activity encompassed leaf image dataset exploration, data preprocessing, ResNet9 model training, model evaluation, and a Streamlit-based prediction application demonstration deployed with Ngrok. Learning outcomes were measured using a 20-item multiple choice pre-test and post-test, complemented by facilitator observation of participant engagement.
Results indicate that the already-high conceptual understanding (mean scores of 96.84 and 97.37, N =38) was successfully transferred into technical competence, with per-stage achievement ranging between 93.4% and 99.1%, peaking in the Training and Evaluation stages (99.1%) and lowest in Deployment. These findings suggest that time allocation in similar short-duration ML/CV training should be proportional to each pipeline stage's integration complexity.
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