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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Pseudocode Jurnal Pengabdian Kepada Masyarakat Sakai Sambayan Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan Proceeding of the Electrical Engineering Computer Science and Informatics Masyarakat Telematika Dan Informasi : Jurnal Penelitian Teknologi Informasi dan Komunikasi INSIST (International Series on Interdisciplinary Research) Barometer Journal of Appropriate Technology for Community Services Jurnal Profesi Insinyur Universitas Lampung Journal of Applied Science, Engineering and Technology (J. ASET) Electrician : Jurnal Rekayasa dan Teknologi Elektro Journal of Hypermedia & Technology-Enhanced Learning Ulil Albab Edu Komputika Journal Jurnal Teknologi Riset Terapan SNTE Jurnal PPS-TP: Jurnal Pendidikan Analisis, Aplikasi Teori dan Hasil Penelitian
Hery Dian Septama
Department Of Electrical Engineering Faculty Of Engineering, Universitas Lampung Jalan Sumantri Brojonegoro 1, Bandar Lampung, 35145
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Journal : Jurnal Pseudocode

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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 Adam Hussein Afri Yudamson Ahmad Abdullah Ahmad Arbain Anam, M. Chairul Arbain, Ahmad Ardian Ulvan Ardian Ulvan Ardian Ulvan Ardian Ulvan Bestak, Robert Cahyana, Amanda Hasna Dwi Indria Anggraini Erimson Siregar Febrianti, Sintya Feren Ade Verilia Fitriyani Garin Revanol Geri Romadhoni Tanjung Gigih Forda Nama Gusti, Khalid Surya Hakim, Lukmanul Harahap, Mochammad Mogi Ibrahim Hlavacek, Jiri Irvika Romana Jiri Hlavacek Jiri Hlavacek Khalid Surya Gusti Lania, Siska M Komarudin M Komarudin M Komarudin, M M. Chairul Anam M. Komarudin Mahendra Pratama MARDIANA Mardiana Mardiana Mardiana Mardiana Mardiana Mardiana Maria Ulfa Muthmainah Martinus, Martinus Maulana, Sadam Meizano Ardhi Muhammad Meizano Ardhi Muhammad Meizano Ardhi Muhammad Melvi, Melvi Michel, Michel Mochammad Mogi Ibrahim Harahap Mona Arif Muda Batubara Muda, Mona Arif Muhamad Komarudin Muhamad Komarudin Muhamad Komarudin Muhammad Irsyad Muhammad Komaruddin Muhammad, Meizano Ardhi Mulyani, Yessi Nurhayati Nurhayati Nyoman Herman Ardike oktadiani, Isna P, Rio Ariestia Pamungkas, Ayu Dian Pangestu, Rizki Pramudya, Rivan Herdian Puput Budi Wintoro, Puput Budi R Arum Setiapriadi Raden Arum Setia Priadi Raden Arum Setia Priadi, Raden Arum Rahmayani, Fidha Rangga Firdaus Rangga Firdaus Rani Himayani Ratna Sulistiyanti, Sri Reksa Suhud Tri Atmojo Revanol, Garin Rinaldi, Daniel Rio Ariestia P Rizky Hadi Robert Bestak Robert Bestak Robert Bestak Setyawan, FX Arinto Sianipar, Yos Marison Sintya Febrianti Sophian, Ali Sri Purwiyanti Suharso Suharso Sulistyono, Wahyu Eko Surya Saputra, Surya Saputra Titin Yulianti Trisya Septiana Udini, Syariffah Alvitara Ulvan, Ardian Utami, Elika Dwi Wahyu Eko Sulistiono Wahyu Eko Sulistyono Wicaksono, Muhamad Aby Yuliant, Titin