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All Journal Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan (JUSTIKPEN)
Rudianta Sitepu
STMIK Triguna Dharma
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mechanical Engineering Social Sciences Other

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Implementasi Algoritma K-Nearest Neigbor Untuk Klasifikasi Pengajuan Kredit Rudianta Sitepu; Manohar Manohar
Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan Vol. 1 No. 2 (2022): Sistem Informasi, Teknik Informatikan dan Teknologi Pendidikan
Publisher : CV. Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/justikpen.v1i2.6

Abstract

Unsecured loans are the community's choice for lending to banks that provide these services. PT. BPR Diori Ganda is a regional private banking company that serves savings and loans and loans without collateral for the community. Submission of unsecured loans must go through an assessor team to process the analysis of the attributes that affect the customer's classification so that credit can be approved, which is then submitted to the commissioner for credit approval. But what if those who apply for credit on the same day in large amounts, of course this will make the process of credit analysis and approval will take a long time. If it is seen from the many needs of the community to apply for loans without collateral, a classification application is needed, in order to facilitate the work of the assessor team in the process of analyzing the attributes that affect customer classification. To find out the classification of customers who apply for unsecured loans using data mining with the K-Nearest Neighbor algorithm. The result of this research is the classification of problematic or non-performing customers for credit applications without collateral
Co-Authors Manohar Manohar