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All Journal Teknika Jurnal Pengabdian Masyarakat AbdiMas MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer IJISTECH (International Journal Of Information System & Technology) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) IJISTECH Journal Automation Computer Information System (JACIS) The Indonesian Journal of Computer Science Research Intechno Journal : Information Technology Journal JURNAL MULTIDISIPLIN BHATARA
Seniwati, Erni
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Social Sciences

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Journal : IJISTECH (International Journal Of Information System

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Implementation of The Naïve Bayes Algorithm For Diagnosing Lung Disesase In An Expert System Concept Hafiyyan, Azka; Seniwati, Erni; Haryoko, H
IJISTECH (International Journal of Information System and Technology) Vol 7, No 1 (2023): The June edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i1.300

Abstract

Web-based lung disease diagnosis expert system is an application designedto help diagnose lung disease quickly and accurately. This system uses the Naïve Bayes classification method which is one of the techniques in artificial intelligence. The Naïve Bayes method utilizes probability to classify data based on existing attributes. In the context of a lung disease expert system, these attributes can include symptoms experienced by patients such as cough, shortness of breath, chest pain, and so on. Research results have shown that in the test of accuracy comparing the expert with the lung disease diagnostic system using the naive Bayes method, the accuracy rate reached 86.66%. With the achieved accuracy rate, it can be stated that the system is capable of performing diagnoses effectively and accurately
Co-Authors Achmad Lukman, Achmad Acihmah Sidauruk Ali Mustopa, Ali Arief Setyanto Eko Riswanto, Eko Gibran, Ibrahim El Hafiyyan, Azka Haryoko, H Haryoko, Haryoko Imran, Muhammad Khamjah Bin Isnaini , Septi Fajar JOKO DWI SANTOSO Mulyatun, Sri Nila Feby Puspitasari, Nila Feby NINIK TRI HARTANTI Noviana , Zakia Rahmadhani Pangemanan, Robertus Aldrin Rahman, Bahtiar Rina Pramitasari Sari , Yulviani Puteri Puspita Sari, Rizka Amelia Setiawanto, Irwan Shahruri, Rifandi Annas TONNY HIDAYAT Wahju Tjahjo Saputro Wulandari, Evita Sekar Yuli Astuti