The Comparison between Bayes and Certainty Factor Method of Expert System in Early Diagnosis of Dengue Infection
(1) Universtas Negeri Semarang
(2) Universitas Negeri Semarang
(3) Universitas Negeri Semarang
Abstract
The development of existing artificial intelligence technology has been widely applied in detecting diseases using expert systems. Dengue Infection is one of the diseases that is commonly suffered by the community and may cause in death. In this study, an expert diagnosis system for dengue infection is made by comparing between both Bayes method and Certainty Factor. The aims are to build an expert system using Bayes and Certainty Factor for early diagnosis of dengue infection and also to determine their level of accuracy. There are 80 data used in this study which are obtained from the medical records of Sekaran Health Center in Semarang City. The test results show that the level of accuracy obtained from 80 medical record data for Bayes method is 90% and the Certainty Factor method is 93,75%.
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Naser, S. S. A., & Ola, A. Z. A. 2008. An Expert System For Diagnosing Eye Diseases Using Clip. Journal of Theoritical and Applied Information Technology, 923-930.
Olanloye., & Odunayo, D. 2014. An Expert System For Diagnosing Faults In Motorcycle. International Journal of Engineering and Applied Sciences, 5(6): 1-8.
Naik, M. V., & Lokhanday, S. 2012. Building a Legal Expert System For Legal Reasoning In Spesific Domain-A Survey. International Journal of Computer Science & Information Technology (IJCSIT), 4(5): 175-184.
Listiyono, H. 2008. Merancang dan Membuat Sistem Pakar. Jurnal Teknologi Informasi DINAMIK, 8(2): 115-124.
Muslim, M.A., Kurniawati, I., & Sughiarti, E. 2015. Expert System Diagnosis Chronic Kidney Disease Based on Mamdani Fuzzy Inference System. Journal of Theoretical and Applied Information Technology, 78(1): 70-75.
Sari, N. A. 2013. Sistem Pakar Mendiagnosa Penyakit Demam Berdarah Menggunakan Metode Certainty Factor. Pelita Informatika Budi Darma, 4(3): 100-103.
Prihatini, P. M. 2011. Metode Ketidakpastian dan Kesamaran dalam Sistem Pakar. Lontar Komputer, 2(1): 29-42.
Syarief, M., Prastiti, N., & Setiawan, W. 2017. Comparison of Naive Bayes and Certainty Factor Method for Corn Disease Expert System: Case in Bangkalan, Indonesia. Int. Journal of Engineering Research and Application, 7(11): 30-34.
Rahayu, S. 2013. Sistem Pakar Untuk Mendiagnosa Penyakit Gagal Ginjal Dengan Menggunakan Meode Bayes. Pelita Informatika Budi Darma, 4(3): 129-134.
Arhami, M. 2005. Konsep Dasar Sistem Pakar. Yogyakarta: ANDI.
Munandar, Tb. Ai., Suherman., & Sumiati. 2012. The Use of Certainty Factor with Multiple Rules for Diagnosing Internal Disease. International Journal of Application or Innovation in Engineering & Management, 1(1): 58-64.
Sutojo. 2011. Kecerdasan Buatan. Yogyakarta: Penerbit Andi.
Patel, U. A., & Jain N. K. 2013. New Idea In Waterfall Model For Real Time Software Development. International Journal of Engineering Research & Technology (IJERT), 2(4): 115.
Pressman, R. S. 2001. Software Engineering: A Practitioner’s Approach (6th Ed). Singapore: McGraw-Hill, Inc.
Putra, A. T. 2014. Pengembangan E-Lecture menggunakan Web Service Sikadu untuk Mendukung Perkuliahan di Universitas Negeri Semarang. Scientific Journal of Informatics, 1(2): 170.
Purwinarko, A. 2014. Model Expertise Management System di Universitas Negeri Semarang. Scientific Journal of Informatics, 1(2): 178.
Nugroho, Z. A., & Arifudin, R. 2015. Sistem Informasi Tracer Study Alumni Universitas Negeri Semarang Dengan Aplikasi Digital Maps. Scientific Journal of Informatics, 1(2): 154.
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Scientific Journal of Informatics (SJI)
p-ISSN 2407-7658 | e-ISSN 2460-0040
Published By Department of Computer Science Universitas Negeri Semarang
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