Jurnal SINTA 2
Perbandingan Performa Metode Klasifikasi SVM, Neural Network, dan Naive Bayes untuk Mendeteksi Kualitas Pengajuan Kredit di Koperasi Simpan Pinjam
Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 6 No. 4 (2019) · pp. 444–449
- ISSN
- 2528-6579, 2355-7699
- Published
- 2019-07-15
- Publisher
- Fakultas Ilmu Komputer, Universitas Brawijaya
- Language
- id
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.
Collaboration network of the authors
40 authors · 75 co-authorship links · 10 across different organisations · showing the 40 most connected
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
- University of Brawijaya
- Indonesian Institute of Sciences
- Institut Teknologi Sepuluh Nopember
- Affiliation unknown
- Different organisations
- Same organisation
- Affiliation unknown
- Thicker line = more joint articles · bigger circle = more articles
Show as table
| Co-author | Organisation | Joint articles |
|---|---|---|
| Fajar Pradana | University of Brawijaya | 10 |
| Retno Indah Rokhmawati | University of Brawijaya | 3 |
| Fitri Utaminingrum | University of Brawijaya | 3 |
| Ahmad Afif Supianto | University of Brawijaya | 2 |
| Bayu Priyambadha | University of Brawijaya | 2 |
| Satrio Agung Wicaksono | University of Brawijaya | 2 |
| Achmad Basuki | University of Brawijaya | 2 |
| Imam Cholissodin | University of Brawijaya | 2 |
| Muhammad Wafi | University of Brawijaya | 2 |
| Novanto Yudistira | University of Brawijaya | 2 |
| Herman Tolle | University of Brawijaya | 2 |
| Aisyah Awalina | University of Brawijaya | 2 |
| Welly Purnomo | University of Brawijaya | 2 |
| Dian Eka Ratnawati | University of Brawijaya | 2 |
| Nur Laita Rizki Amalia | University of Brawijaya | 1 |
| Vicky Zilvan | Indonesian Institute of Sciences | 1 |
| Asri Rizki Yuliani | Indonesian Institute of Sciences | 1 |
| Ade Ramdan | Indonesian Institute of Sciences | 1 |
| Joseph Ananda Sugihdharma | University of Brawijaya | 1 |
| Rizal Setya Perdana | University of Brawijaya | 1 |
| Nathanael Victor Darenoh | University of Brawijaya | 1 |
| Reza Andria S | Affiliation unknown | 1 |
| Sarwosri Sarwosri | Institut Teknologi Sepuluh Nopember | 1 |
| Aditya Rachmadi | University of Brawijaya | 1 |
| Aulia Akhrian Syahidi | University of Brawijaya | 1 |
| Fajerin Biabdillah | University of Brawijaya | 1 |
| Indra Syahputra | University of Brawijaya | 1 |
| Bayu Sutawijaya | University of Brawijaya | 1 |
| Mochammad Dearifaldi Al Ikhsan | University of Brawijaya | 1 |
| Sulandri Sulandri | University of Brawijaya | 1 |
| Rona Salsabila | University of Brawijaya | 1 |
| Muhammad Zulfikarrahman | University of Brawijaya | 1 |
| Diva Fardiana Risa | University of Brawijaya | 1 |
| Aisyatul Maulidah | University of Brawijaya | 1 |
| Intan Yusuf Habibie | University of Brawijaya | 1 |
| Budi Darma Setiawan | University of Brawijaya | 1 |
| Muhammad Nugraha Delta Revanza | Affiliation unknown | 1 |
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.