ANALYSIS OF CUSTOMER PROFILE CHARASTERISTIC WITH CREDIT QUALITY USING THE CLUSTERING METHOD FOR RISK MITIGATION AND SMALL MEDIUM ENTERPRISE CREDIT PORTOFOLIO EXPANSION PLANNING
DOI:
https://doi.org/10.33086/bfj.v8i2.5225Keywords:
Clustering, Customer Profile, Credit Quality.Abstract
in managing the Small Medium Enterprise credit portfolio. The strategy taken by a bank must be adapted to the general characteristics of the target debtors for business expansion and risk mitigation so that business expansion and risk mitigation efforts can be carried out effectively for certain groups. Based on these problems, the purpose of this research is to identify groups of debtors with similar characteristics based on debtor profile data and their credit quality, and to understand the differences in credit risk and opportunities for business expansion among these groups. The data used is the profile of the distribution of debtors from Bank ABC of 5088 debtors. The analysis technique used is K-Means and KMedoids with the evaluation criteria used are silhouette score, davies-bouldin index and computation time. Completion of the optimal number of groups is done by analyzing the WSS graph using the elbow method. Analysis of business expansion and risk reduction is carried out separately where business expansion analysis is carried out for debtors with a collectibility value of 1 and risk reduction analysis is carried out for debtors who have a collectibility value of 2 – 5. The results show that there are 5 groups in the business expansion analysis and 3 groups in risk mitigation analysis. The high value of the silhouette index and davies-bouldin index makes the grouping results have a strong structure. The KMedoids method is used in this analysis because it has better evaluation criteria than K-Means. Priority is also determined for each group formed so that it can be determined which group has the greatest opportunity to become target expansion and which group has the greatest risk that needs to be mitigated. In general, business sectors such as agriculture and plantations are experiencing a decline in economic activity in 2022, so it needs attention from the bank so that it does not disrupt the credit portfolio. To complement the results of this study, an analysis of external and internal business health needs to be carried out in more depth so that the big picture of credit problems at Bank ABC can be identified.
Downloads
References
Adiana, B. E., Soesanti, I., & Permanasari, A. E. (2018). Analisis segmentasi pelanggan menggunakan kombinasi RFM model dan teknik clustering. Jurnal Terapan Teknologi Informasi, 2(1), 23–32.
Afifah, D. R. (2016). Penerapan Algoritma Chaid dalam Pengklasifikasian Pada Status Kredit Macet (Studi Kasus: Nasabah PT. Bank Mega Tbk. Cabang Kantor Pusat DKI Jakarta pada Bulan Desember 2015).
Akbar, Z. K., Midu, S., & Rumimpunu, D. (2022). PENERAPAN PRINSIP KEHATI-HATIAN BANK GUNA MENCEGAH KREDIT MACET. LEX PRIVATUM, 10(4).
Alfina, T., Santosa, B., & Barakbah, A. R. (2012). Analisa perbandingan metode hierarchical clustering, k-means dan gabungan keduanya dalam cluster data (studi kasus: Problem kerja praktek teknik industri its). Jurnal Teknik Its, 1(1), A521–A525.
Bank Indonesia. (2023). Laporan Perekonomian Provinsi Bali Februari 2023. https://www.bi.go.id/id/publikasi/laporan/lpp/Pages/Laporan-Perekonomian-Provinsi-Bali- Februari-2023.aspx
Cahyani, A. N., Hasanah, N., & Irfany, M. I. (2020). Strategi Pencegahan Pembiayaan Bermasalah pada BPRS di Kabupaten Bogor. AL-MUZARA’AH, 8(1), 73–93. https://doi.org/10.29244/jam.8.1.73-93
Data Mining : Algoritma dan Implementasi. (n.d.).
Dias Ramadhani, R., & Januarita, D. A. (2017). Evaluasi K-Means dan K-Medoids pada Dataset Kecil. https://archive.ics.uci.edu/ml/datasets.html.
Fanggidae, A., Sina, D. R., & Mauko, A. Y. (2019). Implementasi Metode Analisis Gap Dan Profile Matching Untuk Kelayakan Calon Debitur Di Koperasi Simpan Pinjam (Ksp) Kopdit Solidaritas Santa Maria Assumpta. J-ICON: Jurnal Komputer Dan Informatika, 7(2), 129– 139.
Gernaria Sihombing, E. (2017). KLASIFIKASI DATA MINING PADA RUMAH TANGGA MENURUT PROVINSI DAN STATUS KEPEMILIKAN RUMAH KONTRAK/SEWA MENGGUNAKAN K-MEANS CLUSTERING METHOD (Vol. 2, Issue 2). https://www.bps.go.id/.
Hulu, I. I. P. (2018). PENGARUH PENGENDALIAN MANAJEMEN TERHADAP KOLEKTIBILITAS PIUTANG PADA KOPERASI SIMPAN PINJAM PENGEMBANGAN PEDESAAN (KSP3) NIAS CABANG GUNUNGSITOLI.
Husni, M., Apriliani, W. A., & Idayu, R. (2022). ANALISIS PENERAPAN PSAK 71 TERKAIT CADANGAN KERUGIAN PENURUNAN NILAI: PADA PERUSAHAAN SEKTOR PERBANKAN BUMN YANG TERDAFTAR DI BEI. Jurnal Valuasi: Jurnal Ilmiah Ilmu Manajemen Dan Kewirausahaan, 2(1), 62–81.
Indonesia, I. B. (2014). Mengelola Kredit Secara Sehat. Gramedia Pustaka Utama.
Irwansyah, E., & Faisal, M. (2015). Advanced Clustering: Teori dan Aplikasi. Deepublish. Kamila, I., Khairunnisa, U., & Mustakim, M. (2019). Perbandingan Algoritma K-Means dan K-
Medoids untuk Pengelompokan Data Transaksi Bongkar Muat di Provinsi Riau. Jurnal
Ilmiah Rekayasa Dan Manajemen Sistem Informasi, 5(1), 119–125.
Kansal, T., Bahuguna, S., Singh, V., & Choudhury, T. (2018). Customer segmentation using K-means clustering. 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), 135–139.
Katuuk, P. M., Kumaat, R. J., & Niode, A. O. (2018). Pengaruh Dana Pihak Ketiga, Loan to Deposit Ratio, Biaya Operasional Pendapatan Operasional terhadap Return on Asset Bank Umum di Indonesia periode 2010.1-2017.4. Jurnal Berkala Ilmiah Efisiensi, 18(2).
Khormarudin, A. N. (2016). Teknik Data Mining: Algoritma K-Means Clustering. J. Ilmu Komput, 1–12.
Madelene, M. L., & Sidauruk, J. (2022). Implementasi undang-undang cipta kerja dalam perizinan usaha bagi UMKM. Nommensen Journal of Business Law, 1(1), 32–46.
Muchtar, F. R., Muhyidin, Y., & Muttaqin, M. R. (2022). PENENTUAN STRATEGI MARKETING MENGGUNAKAN ALGORITMA K MEANS (studi kasus: PT. BPR Pondasi Niaga Perdana cabang Bekasi). Jurnal Teknologi Sistem Informasi, 3(2), 266–276.
Mukhopadhyay, A., Maulik, U., & Member, S. (2007). Multiobjective Approach to Categorical Data Clustering.
Mulyati, E. (2018). Penerapan Manajemen Risiko Sebagai Prinsip Kehati-Hatian Dalam Pemberian Kredit Perbankan. SUPREMASI: Jurnal Hukum, 1(1), 34–48.
Pradana, I. H. (2015). Klasifikasi Citra Sidik Jari Berdasarkan Enam Tipe Pattern Menggunakan Metode Euclidean Distance. Inform. Jur. Tek. Komputer, Fak. Ilmu Nuswantoro, Univ. Dian, 1–5.
Puspita, A. A., Al Zanah, N. M., & Sarikuswati, S. (2020). Analisa Perpanjanga Restrukturisasi Kredit Terhadap Likuiditas Perbankan Sebelum dan Sesudah Pandemi Covid 19. Jurnal Aktiva: Riset Akuntansi Dan Keuangan, 2(3), 140–148.
Savitri, O. A., Zahroh, Z. A., & Nuzula, N. F. (2014). Analisis Manajemen Risiko Kredit Dalam Meminimalisir Kredit Bermasalah Pada Kredit Usaha Rakyat (Studi pada Bank Jatim Cabang Mojokerto). Jurnal Administrasi Bisnis (JAB) Vol, 12.
Sihombing, E. G. (2017). Klasifikasi Data Mining Pada Rumah Tangga Menurut Provinsi Dan Status Kepemilikan Rumah Kontrak/Sewa Menggunakan K-Means Clustering Method. CESS (Journal of Computer Engineering, System and Science), 2(2), 74–82.
Siregar, A. M., Kom, S., Puspabhuana, M. K. D. A. N. A., Kom, S., & Kom, M. (2017). Data Mining: Pengolahan Data Menjadi Informasi dengan RapidMiner. CV Kekata Group.
Suksmonohadi, M., & Indira, D. (2020). Kebijakan Penanganan Pandemi Covid-19. Perkembangan Ekonomi Keuangan Dan Kerja Sama Internasional.
Sutrisno, S., Panggalo, L., Asir, M., Yusuf, M., & Cakranegara, P. A. (2023). Literature Review: Mitigasi Resiko dan Prosedur Penyelamatan pada Sistem Perkreditan Rakyat. COSTING: Journal of Economic, Business and Accounting, 6(2), 1154–1167.
Tengor, R. C., Murni, S., & Moniharapon, S. (2015). PENERAPAN MANAJEMEN RISIKO UNTUK MEMINIMALISIR RISIKO KREDIT MACET PADA PT. BANK SULUTGO. In Penerapan Manajemen Risiko... Jurnal EMBA (Vol. 345).
Utomo, W. (2021). The comparison of k-means and k-medoids algorithms for clustering the spread of the covid-19 outbreak in Indonesia. ILKOM Jurnal Ilmiah, 13(1), 31–35. https://doi.org/10.33096/ilkom.v13i1.763.31-35
Wahyuddin, S., Sudipa, I. G. I., Putra, T. A. E., Wahidin, A. J., Syukrilla, W. A., Wardhani, A. K., Heryana, N., Indriyani, T., & Santoso, L. W. (2023). Data Mining. Global Eksekutif Teknologi.
Wahyuni, N. (2017). Penerapan prinsip 5c dalam pemberian kredit sebagai perlindungan bank. Lex Journal: Kajian Hukum & Keadilan, 1(1).
Watrianthos, R., Kusmanto, K., Simanjorang, E. F. S., Syaifullah, M., & Munthe, I. R. (2019). Penerapan Metode Promethee Sebagai Sistem Pendukung Keputusan Pemeringkatan Siswa. Jurnal Media Informatika Budidarma, 3(4), 381–386.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Ilham Achmadi Yorinda, Agus Budi Raharjo
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
