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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic
Leidiana, Henny
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Computer Science & IT Decision Sciences, Operations Research & Management

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PENERAPAN ALGORITMA K-NEAREST NEIGHBOR UNTUK PENENTUAN RESIKO KREDIT KEPEMILIKAN KENDARAAN BEMOTOR Leidiana, Henny
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 1 No 1 (2013): Januari 2013
Publisher : LPPM Universitas Islam 45 Bekasi

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Abstract

ABSTRAK Sejalan dengan pertumbuhan bisnis, kredit merupakah masalah yang menarik untuk diteliti. Beberapa riset bidang komputer untuk mengurangi resiko kredit telah banyak dilakukan dalam rangka menghindarai kehancuran suatu perusahaan pembiayaan. Paper ini membahas algoritma k-Nearest Neighbor (kNN) yang diterapkan pada data konsumen yang menggunakan jasa keuangan kredit kendaraan bermotor. Hasil testing untuk mengukur performa algoritma ini menggunakan metode Cross Validation, Confusion Matrix dan kurva ROC dan menghasilkan akurasi dan nilai AUC berturut-turut 81,46 % dan 0,984. Karena nilai AUC berada dalam rentang 0,9 sampai 1,0 maka metode tersebut masuk dalam kategori sangat baik (excellent). Kata kunci : K-Nearest Neighbor, Cross Validation, Confusion matrix, ROC ABSTRACT In line with the growth and business development, credit issues remain to be studied and revealed interesting. Some of the research field of computers has done much to reduce the credit risk of causing harm to the company. In this study, k-Nearest Neighbor (kNN) algorithm is applied to the data of consumers who have good credit financing motorcycle that consumers are troubled or not. From the test results to measure the performance of the algorithms using the test method Cross Validation, Confusion Matrix and ROC curves, it is known that the accuracy value of 81.46% and AUC values of 0.984. This methodes is include excellent classification because the AUC value between 0.90-1.00. Keywords: K-Nearest Neighbor, Cross Validation, Confusion matrix, ROC
PENERAPAN ALGORITMA K-NEAREST NEIGHBOR UNTUK PENENTUAN RESIKO KREDIT KEPEMILIKAN KENDARAAN BEMOTOR Leidiana, Henny
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 1 No 1 (2013): Januari 2013
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

ABSTRAK Sejalan dengan pertumbuhan bisnis, kredit merupakah masalah yang menarik untuk diteliti. Beberapa riset bidang komputer untuk mengurangi resiko kredit telah banyak dilakukan dalam rangka menghindarai kehancuran suatu perusahaan pembiayaan. Paper ini membahas algoritma k-Nearest Neighbor (kNN) yang diterapkan pada data konsumen yang menggunakan jasa keuangan kredit kendaraan bermotor. Hasil testing untuk mengukur performa algoritma ini menggunakan metode Cross Validation, Confusion Matrix dan kurva ROC dan menghasilkan akurasi dan nilai AUC berturut-turut 81,46 % dan 0,984. Karena nilai AUC berada dalam rentang 0,9 sampai 1,0 maka metode tersebut masuk dalam kategori sangat baik (excellent). Kata kunci : K-Nearest Neighbor, Cross Validation, Confusion matrix, ROC ABSTRACT In line with the growth and business development, credit issues remain to be studied and revealed interesting. Some of the research field of computers has done much to reduce the credit risk of causing harm to the company. In this study, k-Nearest Neighbor (kNN) algorithm is applied to the data of consumers who have good credit financing motorcycle that consumers are troubled or not. From the test results to measure the performance of the algorithms using the test method Cross Validation, Confusion Matrix and ROC curves, it is known that the accuracy value of 81.46% and AUC values of 0.984. This methodes is include excellent classification because the AUC value between 0.90-1.00. Keywords: K-Nearest Neighbor, Cross Validation, Confusion matrix, ROC
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