Perbandingan dan Certainty factor pada Sistem Pakar Untuk Mendiagnosa Dini Penyakit Glaukoma
DOI:
https://doi.org/10.35870/jtik.v5i3.183Keywords:
Certainty factor, Expert System, Glaucoma, MySQL, Naïve bayes, PHPAbstract
Glaucoma is an eye disease that causes the second largest blindness after cataracts, this disease can cause decreased vision and can even be fatal, namely permanent blindness if it is not realized and treated immediately. Lack of information and education to the public to always maintain eye health is the basis for the purpose of making this expert system which aims to provide early diagnosis to people who are indicated to have glaucoma based on the symptoms or characteristics previously felt. The Naïve bayes method is a method that uses statistics and probability in predicting a person's chance of suffering from glaucoma based on the symptoms previously felt. It is made based on a website with PHP as the programming language and uses MySQL for the database. As for the comparison method used is the Certainty factor, which is a method that functions to determine a certainty value based on the calculation of the predetermined CF value by applying manual calculations. In the Naïve bayes method, the application can group symptom data and types of disease and can diagnose based on previous training data, while for the Certainty factor method based on the calculation of the value of the expert and the CF value that has been inputted by the user, it can produce a percentage of the diagnosis of the disease glaucoma in 96%.
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References
Pusat Data dan Informasi Kementerian Kesehatan RI. 2019. infodatin_glaukoma_2019.pdf (pp. 1-9).
Putra, P.A.D., Purnawan, I.K.A. and Putri, D.P.S., 2018. Sistem Pakar Diagnosa Penyakit Mata dengan Fuzzy Logic dan Naïve Bayes. MERPATI (Menara Penelitian Akademika Teknologi Informasi) Vol, 6.
Darussalam, U. and Benrahman, B., 2020. Web-Based Expert System for Diagnosing Human Eye Disease Using the Naïve Bayes Method. Jurnal Teknik Informatika CIT, 12(1, March), pp.16-25..
Qamaruzzaman, M.H., 2016. Sistem Pakar Untuk Mendiagnosa Penyakit Mata Pada Manusia Menggunakan Teorema Bayes. IJNS-Indonesian Journal on Networking and Security, 5(4).
Surbakti, F.P., 2020. Sistem Pakar Deteksi Penyakit Glaukoma Menggunakan Metode Certainty Factor.
Loppies, S.H.D., Zubaedah, R. and Sega, P.L., 2020. Sistem Pakar Diagnosis Penyakit Mata Berbasis Android. Musamus Journal of Technology & Information, 2(02), pp.50-54.
Hutama, R.S., Hidayat, N. and Santoso, E., 2018. Sistem Pakar Deteksi Dini Penyakit Stroke Menggunakan Metode Naïve Bayes-Certainty Factor. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN, 2548, p.964X.
Istyawati, F., 2019. Klasifikasi Penyakit Glaukoma Sudut Terbuka Menggunakan Metode Naïve Bayes Classifier (Doctoral dissertation, University of Technology Yogyakarta).
Vembandasamy, K., Sasipriya, R. and Deepa, E., 2015. Heart diseases detection using Naive Bayes algorithm. International Journal of Innovative Science, Engineering & Technology, 2(9), pp.441-444.
Wijaya, B.A. and Tanjung, J.P., 2020. An Expert System Diagnosis Human Eye Diseases Using Certainty Factor Method Web-Based. Sinkron: Jurnal dan Penelitian Teknik Informatika, 5(1), pp.78-83.
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