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Mendeteksi kualitas kredit sejak dini merupakan satu tahapan penting yang wajib dilakukan oleh koperasi simpan pinjam guna meminimalisir adanya risiko kredit. Dalam penelitian ini, kami menggunakan tiga metode klasifikasi yaitu SVM, Neural Network , dan Naive Bayes untuk menemukan metode dengan performa yang paling baik dan optimal pada kasus pendeteksian kualitas kredit di koperasi simpan pinjam. Proses yang dilakukan adalah dengan mengimplementasikan data hasil pre processing menggunakan algoritme SVM, Neural Network , dan Naive Bayes dengan proses evaluasi menggunakan 5-fold cross validation . Hasil yang didapatkan adalah metode Neural Network menjadi metode dengan performa paling baik. Rerata tingkat akurasi yang dihasilkan sebesar 86,81%, rerata precision sebesar 0,8194, rerata recall sebesar 0,8236, dan rerata nilai AUC sebesar 0,9158. Namun, waktu eksekusi yang dihasilkan algoritme Neural Network menjadikan algoritme ini sebagai algoritme paling lambat dibandingkan dengan dua metode lain. Nilai rerata waktu eksekusi dari metode Neural Network sebesar 3,058 detik, jauh lebih lama dibandingkan dua algoritme lain yang hanya berkisar pada nilai 0 – 1 detik. Abstract Detecting credit quality at the early stage is an important step that must be done by koperasi simpan pinjam in order to minimize the credit risk. In this research, we use three classification methods i.e. SVM, Neural Network, and Naive Bayes to find the best performance and optimal method to be used in credit quality detection for koperasi simpan pinjam. The process conducted by implementing pre-processing data using an SVM, Neural Network, and Naive Bayes algorithm with the evaluation process using 5-fold cross validation. As the result, The Neural Network method was the best performing method. The average level of accuracy produced was 86.81%, mean precision was 0.8194, average recall was 0.8236, and the average AUC value was 0.9158. However, the execution time generated by the Neural Network algorithm made this algorithm the slowest algorithm compared to the other two methods. The average execution time of the Neural Network method was 3.058 seconds, longer than the other two algorithms which only range from 0 - 1 second.

Perbandingan Performa Metode Klasifikasi SVM, Neural Network, dan Naive Bayes untuk Mendeteksi Kualitas Pengajuan Kredit di Koperasi Simpan Pinjam