Prediksi Pengajuan Kredit Usaha Pada Koperasi Menggunakan Algoritma K-Nearest Neighbor


  • Bartolomius Harpad * Mail STMIK Widya Cipta Dharma, Samarinda, Indonesia
  • Tommy Bustomi Politeknik Negeri Samarinda, Samarinda, Indonesia
  • (*) Corresponding Author
Keywords: Prediction; Prospective Customers; Nave Bayes

Abstract

Cooperative activities have become the activities most needed by many people because they are related to money, cooperatives are places that provide loans to housewives and also workers in a certain area or environment, the lack of interest offered by this cooperative is considered very easy. and very helpful for many parties in facilitating financial affairs, especially in financial matters, because the convenience offered by the cooperative makes many interested people ask for the same thing resulting in vulnerability to fraud, the importance of making predictions on prospective new business loan applications can help reduce the worst risks from various risks that occur in the future, in this study the k-nearest neighbor algorithm will be used as a prediction algorithm for prospective business credit applications at cooperatives, the value obtained is the value of training data or data records of several previous customers so as to easier to know new data as test data in a study. The results found in this study for prospective business credit applications are "Not Eligible" seen to the closest value based on the smallest value with (closest distance) between one another as many as 3 distances, namely numbers 1, 2 and 3 where number 1 states "Not feasible ”, at the second closest distance stating “Eligible” and number 3 stating “Not Eligible”, the most results stated Not Eligible so that the decision value on new customers had to be rejected “Not eligible” to be accepted

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Article History
Submitted: 2022-06-02
Published: 2022-06-30
Abstract View: 43 times
PDF Download: 5 times
How to Cite
Harpad, B., & Bustomi, T. (2022). Prediksi Pengajuan Kredit Usaha Pada Koperasi Menggunakan Algoritma K-Nearest Neighbor. Building of Informatics, Technology and Science (BITS), 4(1), 156−161. https://doi.org/10.47065/bits.v4i1.1626
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