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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF SCIENCE AND SOCIAL RESEARCH KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal Varian KAKIFIKOM : Kumpulan Artikel Karya Ilmiah Fakultas Ilmu Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JUKI : Jurnal Komputer dan Informatika MEANS (Media Informasi Analisa dan Sistem) Jurnal Ipteks Terapan : research of applied science and education Data Sciences Indonesia (DSI) International Journal of Economic, Technology and Social Sciences (Injects)
Zakarias Situmorang
Universitas Katolik Santo Thomas
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Journal : JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)

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Analisa Distance Metric Algoritma K-Nearest Neighbor Pada Klasifikasi Kredit Macet Khairul Fadhli Margolang; Muhammad Mizan Siregar; Sugeng Riyadi; Zakarias Situmorang
Journal of Information System Research (JOSH) Vol 3 No 2 (2022): Januari 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (468.088 KB) | DOI: 10.47065/josh.v3i2.1262

Abstract

Data mining is a method that can classify data into different classes based on the features in the data. With data mining, non-performance loan categories can be classified based on data on lending from cooperatives to their members. This study uses K-Nearest Neighbor to classify non-performance loan categories with various distance metric variations such as Chebyshev, Euclidean, Mahalanobis, and Manhattan. The evaluation results using 10-fold cross-validation show that the Euclidean distance has the highest accuracy, precision, F1, and sensitivity values ​​compared to other distance metrics. Chebyshev distance has the lowest accuracy, precision, sensitivity, while Mahalanobis distance has the lowest F1 value. Euclidean and Manhattan distances have the highest reliability values ​​for true-positive and true-negative class classifications. Mahalanobis distance has the lowest reliability value for false-positive class classification, while Chebyshev distance has the lowest value for false-negative class classification
Co-Authors Abwabul Jinan Ade Clinton Sitepu Ade Clinton Sitepu Aditia Rangga Agus Fahmi Limas Putra Asyahri Hadi Nasyuha B Herawan Hayadi Daim Azhari Parinduri Edunal Yoppi Erna Budhiarti Nababan Herman Mawengkang Ita Juwita Saragih Jaka Kusuma Junaidi Junaidi Jupri Manungkalit Karina Andriyani Khairul Fadhli Margolang Khoirunsyah Dalimunthe Masri Wahyuni Mawaddah Harahap Muhadi M. Ilyas Gultom Muhammad Mizan Siregar Novendra Adisaputra Sinaga P.P.P.A.N.W. Fikrul Ilmi R.H. Zer Puji Sari Ramadhan Raden Aris Sugianto Ridha Maya Faza Lubis Ridha Maya Faza Lubis Rika Rosnelly Rika Rosnelly Rimbun Siringoringo, Rimbun Roslina, Roslina Sartika Mandasari Sugeng Riyadi Valencya Lestari Wanayumini Yuni Franciska Yusniar Lubis