Decision Support System for Determining Senior High School Students’ Majors Using K-Nearest Neighbors and Decision Tree Methods

Abstract

Determining Student Majors in Senior High School (SMA) is an important process that requires consideration based on academic data. This study aims to develop a web-based Decision Support System (DSS) for student major selection using the K-Nearest Neighbor (KNN) and Decision Tree Classifier algorithms. The system is developed using Python programming language with an interactive interface built on Streamlit. Five core subjects are used as input features, namely Mathematics, English, Biology, Physics, and Chemistry. The research method applied is software engineering with a prototyping approach, as this approach allows direct interaction between users and the system during the design and interface testing stages. The system is equipped with role-based login features, student grade input and management, criteria weight configuration, Excel data import/export, decision tree visualization, and KNN algorithm accuracy testing. The implementation results show that the system can predict student majors (Science or Social Studies) with an accuracy level above 90% on a limited test dataset. In addition, the Decision Tree visualization feature helps users understand the classification logic performed by the system.