Analysis of Student Interest Recommendations Using K-Means and Perceptron Algorithm at MTSN 3 Solok

Abstract

Choosing an academic field before entering Vocational High School (SMK) should be supported by information reflecting students' abilities. This study aims to provide an initial field recommendation using K-Means Clustering and Single Layer Perceptron (SLP). K-Means forms three levels of ability, namely High, Medium, and Low, based on nine subject grades. SLP identifies tendencies toward Science, Social, or Creative fields using report card grades and the difference between the two highest field averages. The dataset contains 1,000 records from the 2021/2022 to 2024/2025 academic years. K-Means produced a Silhouette Score of 0.6734 and converged at the seventh iteration. On 150 testing records, SLP achieved 97.33% accuracy, 97.36% precision, 97.33% recall, and a 97.34% F1-score, with 146 correct predictions. Both outputs were combined into an initial recommendation containing ability level and field tendency for students and schools, rather than a final decision. Final selection should consider interests, talents, conditions, and teacher guidance