Implementasi Jaringan Syaraf Tiruan untuk Menentukan Penutupan Kompetensi Keahlian SMK berdasarkan Minat Siswa

Abstract

The development of vocational education requires Vocational High Schools (SMK) to align their competencies with student interests and industry needs. However, a mismatch between student interests and the competencies offered can result in low enrollment, requiring schools to consider closing certain programs. This study proposes the application of Artificial Neural Networks (ANNs) as a predictive method to determine the potential closure of vocational competencies based on an analysis of student interest patterns. The data used includes interest history, academic grades, and other preference indicators, which are then subjected to a preprocessing stage to ensure the quality of the model’s input. The ANN is trained to accurately recognize interest patterns, thus generating objective and adaptive decision-making recommendations. The results show that the ANN implementation provides high accuracy in predicting student interest trends and provides more precise The development of vocational education in Vocational High Schools (SMK) requires the ability to align skill competencies with students' interests and industry needs. A mismatch between students' interests and the competencies offered can lead to low interest in certain programs, which in turn may result in the decision to close those programs. This study proposes the application of Artificial Neural Networks (ANN) as a predictive method to determine the potential closure of skill competencies based on the analysis of students' interest patterns. The data used includes interest history, academic grades, and other preference indicators. This data is processed through a preprocessing stage to ensure the quality of input for the model. The ANN is trained to accurately recognize students' interest patterns, allowing it to generate more objective and adaptive decision recommendations. The results of the study show that the application of ANN has high accuracy in predicting students' interest trends and provides more precise recommendations compared to traditional methods. Therefore, this system can be an effective tool for schools to plan curriculum policies more strategically and sustainably, as well as support decisions regarding skill programs that align with students' interests and industry needs.