Analisis Kesehatan Bank Menggunakan Local Mean K-Nearest Neighbor dan Multi Local Means K-Harmonic Nearest Neighbor

*Alwi Assegaf  -  Departemen Statistika FSM Universitas Diponegoro, Indonesia
Moch. Abdul Mukid  -  Departemen Statistika FSM Universitas Diponegoro, Indonesia
Abdul Hoyyi  -  Departemen Statistika FSM Universitas Diponegoro, Indonesia
Received: 13 Feb 2020; Published: 18 Feb 2020.
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Open Access Copyright 2020 Jurnal Gaussian
License URL: http://creativecommons.org/licenses/by-nc-sa/4.0

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Abstract

The classification method continues to develop in order to get more accurate classification results than before. The purpose of the research is comparing the two k-Nearest Neighbor (KNN) methods that have been developed, namely the Local Mean k-Nearest Neighbor (LMKNN) and Multi Local Means k-Harmonic Nearest Neighbor (MLM-KHNN) by taking a case study of listed bank financial statements and financial statements complete recorded at Bank Indonesia in 2017. LMKNN is a method that aims to improve classification performance and reduce the influence of outliers, and MLM-KHNN is a method that aims to reduce sensitivity to a single value. This study uses seven indicators to measure the soundness of a bank, including the Capital Adequacy Ratio, Non Performing Loans, Loan to Deposit Ratio, Return on Assets, Return on Equity, Net Interest Margin, and Operating Expenses on Operational Income with a classification of bank health status is very good (class 1), good (class 2), quite good (class 3) and poor (class 4). The measure of the accuracy of the classification results used is the Apparent Error Rate (APER). The best classification results of the LMKNN method are in the proportion of 80% training data and 20% test data with k=7 which produces the smallest APER 0,0556 and an accuracy of 94,44%, while the best classification results of the MLM-KHNN method are in the proportion of 80% training data and 20% test data with k=3 which produces the smallest APER 0,1667 and an accuracy of 83,33%. Based on APER calculation shows that the LMKNN method is better than MLM-KHNN in classifying the health status of banks in Indonesia.

Keywords: Classification, Local Mean k-Nearest Neighbor (LMKNN), Multi Local Means k-Harmonic Nearest Neighbor (MLM-KHNN), Measure of accuracy of classification

Keywords: Classification, Local Mean k-Nearest Neighbor (LMKNN), Multi Local Means k-Harmonic Nearest Neighbor (MLM-KHNN), Measure of accuracy of classification

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