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All Journal Jurnal Teknik Industri Jurnal Nasional Komputasi dan Teknologi Informasi SULUH ABDI : Jurnal Ilmiah Pengabdian Kepada Masyarakat Jurnal Informatika dan Teknik Elektro
Husenti, Nadya
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Neuroscience Transportation Other

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Journal : Jurnal Informatika dan Teknik Elektro

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Klasifikasi Penyakit Demam Tifoid Menggunakan Algoritme K-Nearest Neighbor Hanifah, Hidayatul Nur; Bhakti, Henny Dwi; Husenti, Nadya
INTRO : Journal Informatika dan Teknik Elektro Vol 3 No 2 (2024): INTRO : Jurnal Informatika dan Teknik Elektro Edisi Desember 2024
Publisher : Fakultas Teknik dan Informatika Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/intro.v3i2.2258

Abstract

Typhoid fever is an infectious disease caused by Salmonella typhi bacteria. Transmission of typhoid fever can occur through fecal and oral consumption of contaminated food and beverages, irregular eating patterns and lack of cleanliness in food processing sites . Symptoms in patients are analyzed by a doctor with an examination to get a diagnosis. Diagnosis of typhoid fever in patients in order to be handled quickly one of the actions that can be done by developing a system of symptoms of typhoid fever. The system was formed with a classification model from data mining techniques to diagnose typhoid fever in patients. Classification process in solving the problem of diagnosis of patients with symptoms of typhoid fever using the K-Nearest Neighbor (KNN) method. Based on the implementation and testing of the system with K-Nearest Neighbor algorithm to obtain conclusions in the analysis of each patient's symptoms in the test with the system, can provide results of diagnosis of typhoid fever in patients based on testing with training data. Analysis of the highest accuracy of K-Nearest Neighbor algorithm evaluation of 20 testing data and 10 training data contained in K=9 at 75%, recall at 66.67%, and precission 75%.
Co-Authors Dinita Rahmalia, Dinita Hanifah, Hidayatul Nur Harunur Rosyid Henny Dwi Bhakti Listari, Lailatul Makhfudah Rifki, Achmad