Classification Agorithm Analysis For Predicting The Type Of Senior High School On Alumni Smp 2 Balong Ponorogo

Abstract

This study aims to analyze the performance of various classification algorithms in predicting the type of Senior High School (SLTA) that students choose based on academic scores and achievements. The study was conducted at SMPN 2 Balong Ponorogo using the SEMMA (Sample, Explore, Modify, Model, Assess) approach. Secondary data from 1,113 students were used and processed through the stages of data exploration, normalization, feature selection (using Pearson Correlation, Mutual Information, Random Forest, and Lasso Logistic Regression), and dimension reduction using Principal Component Analysis (PCA). Eight classification algorithms were tested, namely Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, XGBoost, LightGBM, CatBoost, and Naïve Bayes. Model evaluation is done using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results show that the Random Forest and KNN models with the Hybrid Feature Selection approach provide the best performance, with the F1-score value reaching 84%. This research contributes to data-based decision making for student guidance in choosing the right further education pathway.