Evaluation of Deep Learning in Textile Products Subjects at SMK Ibu Kartini Semarang

Abstract

This study aims to evaluate the quality of learning in the Fashion expertise program using the CIPP (Context, Input, Process, and Product) model as a comprehensive approach to evaluate deep learning comprehensively on discharge materials in textile product subjects. This study uses an evaluative method with instruments in the form of questionnaires, observation sheets, and document analysis. The research instrument was declared valid based on the content validity test using the Aiken's V coefficient with a value above the minimum limit of 0.77 and reliability based on the Alpha Cronbach test with a reliability coefficient in the range of 0.70–1.00. The results of the evaluation of discharge technique learning  using the weighted CIPP model showed that the context dimension obtained an achievement of 22.75%, the input dimension of 19.49%, the process dimension of 22.38%, and the product dimension of 21.54%. These findings show that discharge technique learning has been carried out effectively and relatively evenly in all evaluation dimensions, although there is still a need to strengthen the aspects of conscious learning, the completeness of supporting facilities, and the use of real media to improve the quality of vocational learning in a sustainable manner.