Tupan Tri Muryono(1*), Irwansyah Irwansyah(2),

(1) STMIK Widuri
(2) STMIK Widuri
(*) Corresponding Author


The banking world in terms of lending to customers is routine activities that are at high risk. In its execution, the problematic credit or bad credit is often due to the lack of careful credit analysis in the process of granting credit, as well as from poor customers. The purpose of this study is to implement data mining to assist in conducting credit analysis process in order to produce the right information whether the customer who will apply for the credit is worthy or not to be able to see the potential payment by the customer. The attributes used in this study consist of 11 attributes i.e. marital status, number of liabilities, age, last education, occupation, monthly income, home ownership, warranties, loan amount, length of loan and description as a result attribute. The methods of data collection used are observation, interviews, and documentation. The method used in this study is K-Nearest Neighbor (K-NN). From the results of evaluation and validation using the K-5 fold that has been done using the RapidMiner tools obtained the highest accuracy results from the K-Nearest Neighbor (K-NN) method of 93.33% in the 5th test.


Kredit, Data Mining, K-Nearest Neighbor

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Agus Budiyantara, Irwansyah, Egi Prengki, P. A. P. (2020). Komparasi Algoritma Decision Tree, Naive Bayes Dan K-Nearest Neighbor Untuk Memprediksi Mahasiswa Lulus Tepat Waktu.

Bramer, M. (2007). Principles of Data Mining: Undergraduate Topics in Computer Science. Springer-Verlag.

Dewi, D., & Satria, F. (2017). Algoritma Naive Bayes Untuk Menentukan Kelayakan Pemberian Kredit Pada Adira. Jurnal Sistem Informasi STMIK Pringsewu Lampung, 8–13.

Han, J, Kamber, M, & Pei, J. (2002). Data Mining: Concept and Techniques (2nd ed.). Morgan Kaufmann.

Mudrajad Kuncoro dan Suhardjono. (2002). Manajemen Perbankan: Teori dan Aplikasi (1st ed.). BPFE.

Murti, T., Abdillah, L. A., & Sobri, M. (2015). Sistem penunjang keputusan kelayakan pemberian pinjaman dengna metode fuzzy tsukamoto. 252–256.

Oktaputra, Alif Wahyu, Noersasongko, E. (2014). Sistem Pendukung Keputusan Kelayakan Pemberian Kredit Motor Menggunakan Metode Simple Additive Weighting Pada Perusahaan Leasing Hd Finance. Ilmu Komputer, Jurnal SPK Kelayakan Pemberian Kredit Motor, 1–9.

Sani, A. (2018). Penerapan Metode K-Means Clustering Pada Perusahaan. Jurnal Ilmiah Teknologi Informasi, 353, 1–7.

Tupan Tri M dan Irwansyah. (2020). Implementasi Data Mining Untuk Menentukan Kelayakan Pemberian Kredit Dengan Menggunakan Algoritma K-Nearest Neighbors (K-Nn).



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