Perbandingan Kinerja Random forest dan SVM Pada Klasifikasi Tingkat Kekumuhan Permukiman Menggunakan SMOTE - Nurika Dwi Wahyuni; Fadhilah Syafria; Novi Yanti; Surya Agustian (Forum Kerjasama Pendidikan Tinggi, 2026)
Classifying slum levels is essential for a structured, data-driven analysis of settlement conditions. This study compares the performance of Random forest and Support vector machine (SVM) in classifying slum levels in Pekanbaru City across two scenarios with and without SMOTE using slum indicator scoring data. Its contributions include analyzing SMOTE's impact on model performance and evaluating the top 10 features against the full feature set. The dataset comprises 992 RT-level records from Disperkim Pekanbaru City (2020, 2021, and 2023) featuring 16 slum indicator scores based on PUPR Ministerial Regulation No. 14/2018, categorized into three classes: Non-Slum, Low Slum, and Moderate Slum. Following the KDD process (selection, preprocessing, transformation, data mining, evaluation, and analysis), the data was split 80:20 using stratified sampling and evaluated based on accuracy, precision, recall, F1-score, and confusion matrix. Results show that the Linear SVM without SMOTE achieved perfect evaluation metrics (1.0000); however, this is interpreted cautiously as the class labels derive from strict regulatory scoring rules, making class boundaries inherently linear. Random forest saw its F1-score rise from 0.9660 to 0.9700 after SMOTE, while the most significant improvement occurred in SVM RBF, jumping from 0.9214 to 0.9779. Testing the top 10 features led to a decreased F1-score across models, indicating that utilizing all 16 features remains optimal for this dataset.