Analisa Distance Metric Algoritma K-Nearest Neighbor Pada Klasifikasi Kredit Macet
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Khairul Fadhli Margolang
*
Universitas Potensi Utama, Medan,
Indonesia
- Muhammad Mizan Siregar Universitas Potensi Utama, Medan, Indonesia
- Sugeng Riyadi Universitas Potensi Utama, Medan, Indonesia
- Zakarias Situmorang Universitas Katolik Santo Thomas, Medan, Indonesia (*) Corresponding Author
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
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References
Christnatalis, Saragih, R. R., Tambunan, B. C. (2021) Data Mining Algorithm C4.5 Classification Determination Credit Eligibility For Jaya Bersama Cooperatives (KORJABE). Jurnal Teknologi dan Sistem Informasi. 8(1): 59-68.
Rizky, S. A., Yesputra, R., Santoso. (2021) Prediksi Kelancaran Pembayaran Cicilan Calon Debitur Dengan Metode K-Nearest Neighbor. JURTEKSI (Jurnal Teknologi dan Sistem Informasi). 7(2): 195-202.
Nofitri, R., Irawati, N. (2019) Integrasi Metode Neive Bayes Dan Software Rapidminer Dalam Analisis Hasil Usaha Perusahaan Dagang. JURTEKSI (Jurnal Teknologi dan Sistem Informasi). 6(1): 35-42.
Iqbal, M. (2019) Klasterisasi Data Jamaah Umroh Pada Auliya Tour & Travel Menggunakan Metode K-Means Clustering. JURTEKSI (Jurnal Teknologi dan Sistem Informasi). 5(2): 97-104.
Arnomo, R. A., Saptomo, W. L. Y., Harsadi, P. (2018) Implementasi Algoritma K-Nearest Neighbor Untuk Identifikasi Kualitas Air (Studi Kasus: PDAM Kota Surakarta). Jurnal TIKomSiN. 6(1): 1-5.
Alfeilat, H. A. A., Hassanat, A. B. A., Lasassmeh, O., Tarawneh, A. S, Alhasanat, M. B., Salman, H. S. E., Prasath, V. B. S. (2019) Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review. Big Data. 7(4): 1-28.
Iswanto, Tulus, Sihombing, P. (2021) Comparison of Distance Models on K-Nearest Neighbor Algorithm in Stroke Disease Detection. Applied Technology and Computing Science Journal. 4(1): 63-68.
Fan, H., Chen, Y., Huang, S., Zhang, X., Guan, H. (2018) Post-fault Transient Stability Assessment Based on k-Nearest Neighbor Algorithm with Mahalanobis Distance. International Conference on Power System Technology (POWERCON). 4417-4423.
Farokhah, L. (2020) Implementasi K-Nearest Neighbor Untuk Klasifikasi Bunga Dengan Ekstraksi Fitur Warna RGB. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK). 7(6): 1129-1135.
Markoulidakis, I., Rallis, I., Georgoulas, I., Kopsiaftis, G., Doulamis, A., Doulamis, N. (2021). Multiclass Confusion Matrix Reduction Method and Its Application on Net Promoter Score Classification Problem. Technologies. 9(81): 58-73.
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Copyright (c) 2022 Khairul Fadhli Margolang, Muhammad Mizan Siregar, Sugeng Riyadi, Zakarias Situmorang
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