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Selsa Amelia
Mulawarman University
Decision Sciences, Operations Research & Management Mathematics

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Penerapan Metode Modified K-Nearest Neighbor pada Pengklasifikasian Status Pembayaran Kredit Barang Elektronik dan Furniture Selsa Amelia; Memi Nor Hayati; Surya Prangga
Statistika Vol. 22 No. 1 (2022): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v22i1.345

Abstract

ABSTRAK Klasifikasi merupakan serangkaian proses pembentukan model dari suatu objek ke dalam kelompok untuk memprediksi kelas dari suatu objek yang belum diketahui sebelumnya. Modified K-Nearest Neighbor (MK-NN) merupakan salah satu metode klasifikasi pengembangan dari algoritma K-Nearest Neighbor (K-NN) yang menambahkan proses validitas serta weight voting (pembobotan) untuk mengatasi tingkat akurasi rendah dari algoritma K-NN. Penelitian ini bertujuan untuk mengetahui hasil pengklasifikasian status pembayaran kredit barang elektronik dan furniture serta tingkat akurasi klasifikasi pada metode MK-NN. Data yang digunakan adalah data debitur PT. KB Finansia Multi Finance Tahun 2020 dengan status pembayaran kredit lancar dan tidak lancar serta menggunakan 7 variabel bebas yaitu usia, jumlah tanggungan, lama tinggal, pendapatan, masa kerja, besar pembayaran kredit, dan lama peminjaman kredit. Berdasarkan penelitian yang telah dilakukan, diperoleh nilai akurasi sebesar 84,61% dengan K optimal yaitu K = 5 pada proporsi 90% : 10%. ABSTRACT Classification is a series of process of forming a model of an object into groups to predict the class of an object that has not been known before. Modified K-Nearest Neighbor (MK-NN) is one of the classification methods developed from the K-Nearest Neighbor (K-NN) algorithm which adds a process of validity and weight voting to overcome the low level of accuracy of the K-NN algorithm. This study aims to determine the results of classifying credit payment status for electronic goods and furniture as well as the accuracy of the classification using the MK-NN method. The data used is debtor data for the 2020 KB Finansia Multi Finance Company with current and non-current credit payment status and uses 7 independent variables, namely age, number of dependents, length of stay, income, years of service, amount of credit payments, and length of loan. Based on the research that has been done, an accuracy value of 84.61% is obtained with optimal K, namely K = 5 at a proportion of 90%: 10%.
Co-Authors Memi Nor Hayati Surya Prangga