Implementasi Algoritma K-Nearest Neighbor (K-NN) dan Single Layer Perceptron (SLP) Dalam Prediksi Penyakit Sirosis Biliari Primer
Primary biliary cirrhosis is a chronic cholestatic liver disease that can lead to liver failure. The majority of individuals who suffer from this disease are women. Primary biliary cirrhosis is recorded as contributing to mortality worldwide with a percentage of 0.6% to 2.0%. However, so far, randomized trials have shown that some immunosuppressant or immunosuppressive drugs do not play a major role in patients with primary biliary cirrhosis. Therefore, early detection is important to start treatment and planning for appropriate medical needs. The results of the processing accuracy with the K-NN algorithm of 76.2% and the SLP algorithm of 63% using the Percentage Split method show that the K-NN algorithm is better for early detection of primary biliary cirrhosis. The K-Nearest Neighbor algorithm is able to perform early detection of primary biliary cirrhosis with a precision of 77% and recall of 75% with the hope that the percentage of mortality worldwide can decrease. However, the K-NN algorithm is not superior in retrieving information in patients with primary biliary cirrhosis. On the other hand, the SLP algorithm is superior in retrieving information in patients with primary biliary cirrhosis with a recall value of 65%.
V. I. Reshetnyak, “Primary biliary cirrhosis: Clinical and laboratory criteria for its diagnosis,” World J. Gastroenterol., vol. 21, no. 25, pp. 7683–7708, 2015, doi: 10.3748/wjg.v21.i25.7683.
J. Mattner, “Impact of microbes on the pathogenesis of primary biliary cirrhosis (PBC) and primary sclerosing cholangitis (PSC),” Int. J. Mol. Sci., vol. 17, no. 11, 2016, doi: 10.3390/ijms17111864.
E. Jenny Heathcote, “Management of primary biliary cirrhosis,” Hepatology, vol. 31, no. 4, pp. 1005–1013, 2000, doi: 10.1053/he.2000.5984.
G. M. Hirschfield et al., “The British Society of Gastroenterology/UK-PBC primary biliary cholangitis treatment and management guidelines,” Gut, vol. 67, no. 9, pp. 1568–1594, 2018, doi: 10.1136/gutjnl-2017-315259.
T. Kumagi and E. J. Heathcote, “Primary biliary cirrhosis,” Orphanet J. Rare Dis., vol. 3, no. 1, pp. 1–17, 2008, doi: 10.1186/1750-1172-3-1.
A. Jacoby et al., “Development, validation, and evaluation of the PBC-40, a disease specific health related quality of life measure for primary biliary cirrhosis,” Gut, vol. 54, no. 11, pp. 1622–1629, 2005, doi: 10.1136/gut.2005.065862.
D. R. W. Z. M. Jollyta, Konsep Data Mining dan Penerapan. Yogyakarta: Deepublish, 2020.
S. B. Imandoust and M. Bolandraftar, “Application of K-Nearest Neighbor ( KNN ) Approach for Predicting Economic Events : Theoretical Background,” Int. J. Eng. Res. Appl., vol. 3, no. 5, pp. 605–610, 2013.
G. A. Rosso, “Milton,” William Blake Context, no. September, pp. 184–191, 2019, doi: 10.1017/9781316534946.021.
M. Yanto, “Penerapan Jaringan Syaraf Tiruan Dengan Algoritma Perceptron Pada Pola Penentuan Nilai Status Kelulusan Sidang Skripsi,” J. Teknoif, vol. 5, no. 2, pp. 79–87, 2017, doi: 10.21063/jtif.2017.v5.2.79-87.
T. Mada Abdillah, “Rancangan Bangun Sistem Pengklasifikasi Kecepatan Maksimum Kereta Api pada Jalur Klakah-Pasirian Menggunkan Metode Single Layer Perceptron,” Digit. Repos. Univ. Jember, 2017.
M. Najwa, B. Warsito, and D. Ispriyanti, “Pemodelan Jaringan Syaraf Tiruan Dengan Algoritma One Step Secant Backpropagation dalam Return Kurs Rupiah Terhadap Dolar Amerika Serikat,” J. Gaussian, vol. 6, no. 1, pp. 61–70, 2017.
A. J. T, D. Yanosma, and K. Anggriani, “Implementasi Metode K-Nearest Neighbor (KNN) dan Simple Additive Weighting (SAW) Dalam Pengambilan Keputusan Seleksi Penerimaan Anggota Paskibraka,” vol. III, no. 0065, pp. 98–112, 2016.
M. D. Wuryandari and I. Afrianto, “Perbandingan Metode Jaringan Syaraf Tiruan Backpropagation Dan Learning Vector Quantization Pada Pengenalan Wajah,” Komputa, vol. 1, no. 1, pp. 45–51, 2012.
Karsito and S. Susanti, “Klasifikasi Kelayakan Peserta Pengajuan Kredit Rumah Dengan Algoritma Naïve Bayes Di Perumahan Azzura Residencia,” J. Teknol. Pelita Bangsa, vol. 9, pp. 43–48, 2019.
Copyright (c) 2022 Jurnal Ilmiah Informatika
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.