Penerapan Data Mining Untuk Klasifikasi Penduduk Miskin Di Kabupaten Labuhanbatu Menggunakan Random Forest Dan K-Nearest Neighbors - Andi Ernawati; Khairul; Zulham Sitorus; Muhammad Iqbal; Darmeli Nasution (Forum Kerjasama Pendidikan Tinggi (FKPT), 2025)
This study aims to apply and compare the performance of two data mining algorithms—Random Forest (RF) and K-Nearest Neighbors (KNN)—in classifying poverty status among residents of Labuhanbatu Regency. The dataset includes information on occupation, income, housing, and education from 21,137 individuals. After undergoing preprocessing, model training, hyperparameter optimization, and evaluation, both models were assessed using five key metrics: accuracy, precision, recall, F1-score, and AUC. The results show that Random Forest performed slightly better than KNN, achieving an accuracy of 0.6023, precision of 0.4827, recall of 0.4177, F1-score of 0.4479, and an AUC of 0.5681. In comparison, KNN obtained an accuracy of 0.5990, precision of 0.4771, recall of 0.4006, F1-score of 0.4355, and an AUC of 0.5622. Based on these findings, it can be concluded that Random Forest is more effective for poverty classification on this dataset, although the performance difference is relatively small.