A Perbandingan Algoritma Support Vector Machine dan Random Forest dalam Klasifikasi Kelayakan Pemberian Pinjaman pada BPR Nusumma Jawa Barat
DOI:
https://doi.org/10.30605/proximal.v9i1.7977Keywords:
Kelayakan kredit, Klasifikasi, Support Vector Machine, Random Forest, Machine Learning, Evaluasi ModelAbstract
Pesatnya peningkatan pengajuan kredit pada lembaga keuangan menuntut adanya sistem evaluasi kelayakan pinjaman yang mampu bekerja secara cepat, akurat, dan objektif guna meminimalkan risiko kredit bermasalah. Oleh karena itu, penelitian ini bertujuan untuk membandingkan kinerja dua algoritma machine learning, yaitu Support Vector Machine (SVM) dan Random Forest (RF), dalam mengklasifikasikan kelayakan pemberian pinjaman pada BPR Nusumma Jawa Barat. Penelitian ini menggunakan data primer sebanyak 753 observasi dengan enam variabel prediktor dan satu variabel target berupa status pinjaman. Metode penelitian yang digunakan adalah eksperimen kuantitatif melalui beberapa tahapan, meliputi pembersihan data, penyeimbangan kelas menggunakan metode ROSE, transformasi data, pembangunan model SVM dan Random Forest, serta evaluasi kinerja model menggunakan confusion matrix, accuracy, precision, recall, F1-score, dan Area Under Curve (AUC). Hasil penelitian menunjukkan bahwa algoritma SVM menghasilkan nilai AUC sebesar 0,9748, sedangkan algoritma Random Forest memperoleh nilai AUC sebesar 0,9953. Berdasarkan hasil tersebut, Random Forest menunjukkan performa klasifikasi yang lebih unggul dibandingkan SVM. Temuan ini mengindikasikan bahwa Random Forest berpotensi menjadi algoritma yang lebih optimal untuk diterapkan sebagai sistem pendukung keputusan dalam evaluasi kelayakan kredit, sehingga dapat membantu lembaga keuangan dalam meningkatkan efektivitas dan kualitas pengambilan keputusan kredit.
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