Classification of Students Major Preferences at SMKIT Ibnul Qayyim Using a Machine Learning Model Based on Students Knowledge, Skills, and Interests - Rajie Al Qadri Anwar; Widyastuti Andriyani (LPPM Universitas Islam 45 Bekasi, 2026)
This study aims to uncover the dynamics and meanings underlying the major preference process at SMK IT Ibnul Qayyim through the integration of a qualitative approach and the use of machine learning models based on student knowledge, skills, and interests. Major selection is a critical issue in vocational education, as mismatches often occur between student interests and the chosen majors, which can affect learning motivation and job readiness. This study adopts a qualitative case study approach involving ten participants, consisting of guidance and counseling teachers, homeroom teachers, and Grade IX–X students. Data were collected through semi-structured interviews, participatory observation, and document analysis. The data were analyzed using the interactive model of Miles and Huberman, including data reduction, data display, and conclusion drawing. The results reveal three main themes: (1) major determination is still largely influenced by academic achievement rather than skill potential and intrinsic interests; (2) students perceive machine learning-based prediction systems as objective decision-support tools, while emphasizing the importance of teacher involvement in interpreting the results; and (3) the integration of predictive technology with a humanistic approach is more effective in assisting students in determining majors that align with their personal profiles. This analysis aims to evaluate and predict major preferences of vocational high school students in the Software Engineering (Rekayasa Perangkat Lunak/RPL) program based on their academic achievement at the junior secondary school level. The data include scores from core subjects such as Computer Studies, Mathematics, English, Indonesian Language, Arts and Culture, Civic Education, and Social Studies. Two main analytical approaches are employed: Logistic Regression and Random Forest. These methods are selected because each offers distinct strengths in addressing the research objectives, not only in predicting major preferences but also in providing interpretability regarding the factors that influence student decision-making.