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All Journal Jurnal Ilmu Komputer Jurnal Sains dan Teknologi EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Jurnal Pilar Nusa Mandiri JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Tekno Insentif Jurnal Responsif : Riset Sains dan Informatika Jurnal Ilmiah Teknik Informatika dan Komunikasi
Toni Arifin
Uniersitas Adhirajasa Reswara Sanjaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Journal : Jurnal Ilmu Komputer

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Implementasi Algoritma Naïve Bayes Berbasis Particle Swarm Optimization Untuk Memprediksi Penyakit Hepatitis Hilda Farida Husniah; Toni Arifin
Jurnal Ilmu Komputer Vol 14 No 1 (2021): Jurnal Ilmu Komputer
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIK.2021.v14.i01.p05

Abstract

Hepatitis disease is an inflammatory disease of the liver cells, caused by infections (viruses, bacteria, parasites), medicines (including traditional medicines), consuming alcohol, excessive fats and autoimmune diseases. The cause of hepatitis is often caused by Hepatitis B and C Virus. The Hepatitis prevalence in Indonesia in 2013 amounted to 1.2% increased twice compared to the year 2007 Riskesdas of 0.6%. East Nusa Tenggara is the province with the highest prevalence of Hepatitis in 2013 of 4.3%. Researchers are trying to make a breakthrough by making research for the prediction classification of Hepatitis patients with data mining technique. Naïve Bayes is a method used to predict the probability of the future based on past experience and proved to have a high level of accuracy and high speed of calculation. Particle Swarm Optimization is used to improve the accuracy of the method. The research aims to determine if the Naïve Bayes-based Particle Swarm Optimization method can improve the accuracy of the good. The results of using Naïve Bayes-based Particle Swarm Optimization has a confusion matrix accuracy of 91.90% and an AUC of 0946 proved that has good results than Naïve Bayes has a confusion matrix accuracy of 88.52% and AUC 0896.
Co-Authors Ade Mubarok Adi Suparwo Al Huda Ramadhan Ali Akbar Rismayadi Ani Solihat Asri Dwi Andini Asti Herliana, Asti Daniel Ariesta Dwi Ariyandi Dwiza Riana Dwiza Riana Elin Sopiah Fahmi Permadi Achmad Fajar Nugraha Fakhri Habib Hawari Farizul Ma’arif Faslah Fadillah Fitriyani Fitriyani Fitriyani Fitriyani Fitriyani Fitriyani Fitriyani Fitriyani Gita Indah Hapsari Hendi Suhendi Hilda Farida Husniah Ihsan Mudzakir Imam Muhammad Mannar Shahih Insan Cahya Setia Maya Nurjanah Mayya Nurbayanti Shobary Muhamad Revin Reginal Harahap Muhamad Rifqi Alviandi Naufal Hidayah Surya Nur Huzumah Rahayu Febryani Rahmaniza Maulana Rahmawati Rangga Sanjaya Rina Resmiati Rizal Rachman, Rizal Rizki Tri Prasetio Siti Syalwah Syarif Hidayatulloh Taufik Hidayatulloh Tiara Nurwulandari Tiki Ramdhani Tyas Widyani Pratiwi Wildan Wiguna Wira Sanjaya Yudi Ramdhani