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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Iqbal Taufiq Ahmad Nur
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Perbandingan Performa Metode Klasifikasi SVM, Neural Network, dan Naive Bayes untuk Mendeteksi Kualitas Pengajuan Kredit di Koperasi Simpan Pinjam Iqbal Taufiq Ahmad Nur; Nanang Yudi Setiawan; Fitra Abdurrachman Bachtiar
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 4: Agustus 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2650.471 KB) | DOI: 10.25126/jtiik.2019641352

Abstract

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 Naïve 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 Naïve 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. AbstractDetecting 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 Naïve 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 Naïve 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.
Prediksi Kredit Macet Berdasarkan Preferensi Nasabah Menggunakan Metode Klasifikasi C4.5 pada Koperasi Simpan Pinjam Mitra Raya Wates Iqbal Taufiq Ahmad Nur; Nanang Yudi Setiawan; Fitra Abdurrachman Bachtiar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (891.364 KB)

Abstract

Bad credit is the main problem that faced by financial institutions, especially cooperatives in Indonesia. This problem is also happened in KSP Mitra Raya Wates that does not use credit analyst and the decision making process is using an intuitive approach and based on existing experience that owned by KSP Leader. The survey process conducted at KSP Mitra Raya Wates also cannot guarantee that the loans made by customers are free from credit risk, considering there are customers who have bad credit from a total of all customers who have received loans. KSP Mitra Raya Wates needs a system that capable of supporting decision to detect credit quality early on. C4.5 method can be used to predict customers' credit quality by generating rule in form of decision tree. The results of confusion matrix have accuracy of 94.5946. While based on the ROC curve, it generated AUC value of 0.9689. The level of usability generated by utilizing SUS is 82.5. The output is dashboard visualization with several graphs containing the percentage, time-series and trend of total submissions that have been made and also forms that can be used by KSP Mitra Raya Wates to make predictions of customer credit application and also dataset entry into the system.
Co-Authors Fitra Abdurrachman Bachtiar Nanang Yudi Setiawan