Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal Pseudocode

Penilaian Pembayaran Kredit dengan Logistic Regression dan Random Forest pada Home Credit Yulianti, Titin; Cahyana, Amanda Hasna; Komarudin, Muhamad; Mulyani, Yessi; Septama, Hery Dian
Jurnal Pseudocode Vol 11 No 2 (2024): Volume 11 Nomor 2 September 2024
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.11.2.79-88

Abstract

Global economic development has led to the high complexity of society's needs. Financial institutions are here to provide facilities to meet the increasingly complex needs of society. However, the existence of problem loans can be a serious threat so classification techniques in data mining are used to overcome this problem. This research develops a model that can predict customers' ability to make credit payments so that financial institutions can avoid problematic credit. In this research, the SMOTE resampling technique is used to see the effect of sampling in dealing with class imbalance and conducting credit assessments. The research results show that the model built using SMOTE has better AUC than the model without SMOTE. From the two machine learning algorithms, logistic regression and random forest, the results show that the random forest model with SMOTE has the best performance with an accuracy value of 90%, precision of 92%, recall of 88%, F1-score of 90%, and AUC value of 0.97. Based on the best model, ten important features were obtained that influence the process of assessing credit repayment capabilities, namely the normalized score from external data sources, the period for changing customer numbers, the number of previous installment payments, the customer's age, registration time, the period for applying for credit at the credit bureau, the period for changing identity documents, the time for updating information at the credit bureau, and the length of time the customer has worked. In addition, this research produces visualizations via dashboards that can be used to improve the process of assessing credit repayment capabilities. Keywords: Prediction; Logistic Regression; Random Forest; Credit; Repayment Capabilities.
Co-Authors Adhi Nurhartanto, Adhi Ageng Sadnowo Repelianto Agus Haryanto Agustina, Indria Ardi Ragil Saputra Arifudin, M. Bagus br Ginting, Simparmin Budi Wintoro, Puput Cahyana, Amanda Hasna Deni Achmad Djausal, Gita Paramita Dzihan Septiangraini Efendi, Ujang Eliza Hara Fajriansyah, Gilang Filya, Kwinny Intan Gigih Forda Nama Gilang Fajriansyah Gita Paramitha Djausal Gunawan, Charles Gusti, Khalid Surya Halim Abdillah Sholeh Helmy Fitriawan Herti Utami Hery Dian Saptama Hery Dian Septama Hery Dian Septama Hilmi Hermawan Huda, Zulmiftah Ilim, Ilim Irza Sukmana Jaya, Winaldi Putra Kesuma, Yunita Khalid Surya Gusti Komarudin, M. Laksana, Muhammad Fajar lina marlina, lina M. Bagus Arifudin Mahendra Pratama Manzi, Satria Berliano MARDIANA Mardiana Mardiana Mareli Telaumbanua Martinus Martinus Martinus, Martinus Meizano Ardhi Muhammad Meizano Ardi Muhamad Mona Arif Muda Mugahed Al-Rahmi, Waleed Muhamad Komarudin Muhamad Komarudin Muhamad Komarudin Muhammad Amin Muhammad Komarudin Muhammad Komarudin Muhammad, Meizano Ardhi Nanda Sazqiah Nyoman Herman Ardike Panji Kurniawa Pratama, Rama Wahyu Ajie Puput Budi Wintoro Puput budi wintoro Puput Budi Wintoro, Puput Budi Putri, Renatha Amelia Manggala Rafi'syaiim, Muhammad Afif Ragil Saputra, Ardi Reza Dwi Permana Rhomadhona, Nazmah Wulan Rian Kurniawan Rian Kurniawan Satrio, Muhamad Septiangraini, Dzihan Shalihah , Atiqah Hanifah Sony Ferbangkara Sugeng Triyono Titin Yulianti Trisya Septiana Umi Murdika Wahyu Aji Pulungan Wahyu Eko Sulistiono Waleed Mugahed Al-Rahmi Wijaya , Aldo Wulan Rahma Izzati