Personalizing Student Major Selection through Artificial Neural Network Prediction Models - Palupi, Dian Exsi; Liantoni, Febri; Efendi, Agus (Newinera Publisher, 2025)
This research focuses on a well-known problem in secondary education that a student may have a hard time in choosing their study major to fit their interest, gifts, and long-term goals. Concentrating on the SAINTEK (science and technology) and SOSHUM (social sciences and humanities) streams of Indonesian high schools, the study will incorporate an Artificial neural network (ANN) to provide an insight into the student preferences based on multidimensional data through modeling and predicting purposes. A 44-item questionnaire was distributed to 205 students of SMA Negeri 1 Karanganom to gather the inputs that involved personal interests, parental influence, career perspective, and psychosocial characteristics. It was trained and validated with Stratified K-Fold Cross Validation that delivered good performance scores of average accuracy at 87% and precision value at 89- 91 recall and an F1-score of 90. In addition to the algorithmic validation, qualitative interviews of some of the students indicated that the prediction of the model corresponds to what these students perceive about their academic leanings. The obtained results argue that ANN systems can be used, not only as an error-free classifier but also as an educational decision-support system that can be used to augment student guidance with personally-tailored, data-driven advice. The proposed study locates ANN in the larger pedagogical mission of responsiveness and student-centered planning as such, the study also advances the new discourse of ethical and effective uses of artificial intelligence in education.