PREDICTION OF COOPERATIVE LOAN FEASIBILITY USING THE K-NEAREST NEIGHBOR ALGORITHM

Roviani Roviani; Deddy Supriadi; Iqbal Dzulfiqar Iskandar

doi:10.33480/pilar.v17i1.2183

Roviani Roviani Universitas Bina Sarana Informatika
Deddy Supriadi Universitas Bina Sarana Informatika
Iqbal Dzulfiqar Iskandar Universitas Bina Sarana Informatika Kampus Tasikmalaya

DOI: https://doi.org/10.33480/pilar.v17i1.2183

Keywords: Accuracy, Creditworthiness, Data mining classification, K-Nearest Neighbor, ROC Curve Split Validation

Abstract

Approval of credit lending to cooperative members without proper feasibility analysis can cause credit problems, cooperatives such as late payment of installments, and an increase in bad credit which can threaten the survival of the cooperative as a provider of lending services. As a solution to minimize the creditworthiness assessment errors for loan funds, research is carried out to analyze the feasibility of loan funds from the data of cooperative members using the data mining method approach and the algorithm used using the K-Nearest Neighbor. The purpose of this research is to predict the feasibility of granting credit with the right decision and to find out the level of evaluation, accuracy, and validation of the effectiveness of the k-NN algorithm on processing creditworthiness application data classifications. After the prediction research was carried out, the data on the eligibility of credit lending applications were conducted at the Bakti Berkah Sukaraja Savings and Loan Cooperative, The data obtained from the accuracy value of the k-nearest neighbor algorithm before being validated has an accuracy of 87.78% with AUC 0.95, after validation with split validation the accuracy decreased slightly by 2% to be 85.71%, while the AUC value in the ROC Curve was 0.836%. Even though there was a decline, it can still be categorized as a good classification. The impact of this research is that besides the accuracy of the k-NN algorithm being validated, the Bakti Berkah Sukaraja Savings and Loan cooperative can predict the feasibility of applying for credit funds, as an effort to reduce the threat of bad credit risk

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Author Biographies

Deddy Supriadi, Universitas Bina Sarana Informatika

Program Studi Sistem Informasi
Fakultas Teknik dan Informatika, Universitas Bina Sarana Informatika Kampus Tasikmalaya, Indonesia

Iqbal Dzulfiqar Iskandar, Universitas Bina Sarana Informatika Kampus Tasikmalaya

Iqbal Dzulfiqar Iskandar, lahir di tasikmalaya 13 oktober 1991, s1 universitas siliwangi, dan melanjutkan jenjang pendidikan S2 di STMIK Magister ilmu Komputer Nusa Mandiri jakarta, bidang ke ahlian data mining. sekarang sebagai Dosen pengajar di BSI Tasikmalaya - Bidang keahlian Sistem Teknologi Informasi, Jaringan, Data mining - Jaringan Komputer - Familiar dengan Sistem Operasi clear OS, Ms. Windows 98, Windows 2000, Ms. Windows XP, Windows7 dan Windows 8. - Mampu mengoperasikan komputer, merakit/memperbaiki PC dan menginstall system operasi komputer. - Mampu installasi jaringan computer local area. - Mampu merancang kebutuhan system dan membuat perangkat lunak. - Mampu menggunakan aplikasi Microsoft Office, Software Pembangun Sistem : Turbo Pascal, C++, Visual Basic 6.0, Macromedia Dreamweaver, Adobe Dreamweaver, Java NetBeans, Flash, Packet tracer. - Software Desain Grafis : Macromedia Fireworks MX, Adobe Photoshop, Corel Draw. - Konfigurasi jaringan computer. - Manajemen Database : Microsoft Access dan MySQL - Mampu menggunakan Internet, Browsing, Email dll.

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