Expert System For Detection of Cataracts Disease Using The Certainty Factor Method

Authors

  • Vania Jusenda Prasetya Duta Bangsa University, Surakarta, Indonesia
  • Herliyani Hasanah Duta Bangsa University, Surakarta, Indonesia
  • Nurmalitasari Duta Bangsa University, Surakarta, Indonesia

DOI:

https://doi.org/10.61398/ijist-das.v1i1.8

Keywords:

expert system, certainty factor, eye diseases, cataracts

Abstract

A cataract is an eye disease that causes visual impairment in the eye, the most significant cause of blindness in Indonesia. The rate of blindness in Indonesia caused by cataracts has reached 35% among the elderly 50 years and over. With the development of technology and the shortage of ophthalmologists, an expert system is needed to assist eye health experts by incorporating expert intelligence into the system in the form of fact-based data from the interview results. So that with this expert system, it is hoped that it can help society find cataracts in the eye as a form of early prevention of the chance of suffering from cataracts. The certainty factor method is used in the system to determine the certainty value of the facts that have been entered into the system to obtain a percentage level with a value of 93% so that with the help of this method, system users can find out the type of disease from each symptom

References

N. A. Fetrizen, “Analisis Pengaruh Kualitas Produk, Harga, Promosi terhadap Keputusan Pembelian Air Minum dalam Kemasan (AMDK) Merek AICOS Produksi PT. Bumi Sarimas Indonesia,” OSF Prepr., vol. 1, pp. 1–9, 2019.

Mi. Pratama Putra, M. Ariandi, M. Bina Darma, and D. Bina Darma, “Penerapan Data Mining Untuk Memprediksi Tingkat Ketepatan Jumlah Penjualan Produk Air Mineral Pada Pt. Mars Lestari,” … Prod. Air Miner. Pada Pt. Mars …, pp. 20–33, 2022.

R. Alfian, Y. Krishna, F. M. Sari, and W. Heriyani, “Bisnis Menjanjikan Air Dalam Kemasan,” Ekonomi, 2022. .

C. Janiesch, P. Zschech, and K. Heinrich, “Machine Learning and Deep Learning,” Ingeniare, vol. 29, no. 2, pp. 182–183, 2021.

N. Ayuningtyas, R. Nining, and F. M. Basysyar, “Penerapan Data Mining pada Penjualan Produk MS Glow Menggunakan Metode Naive Bayes untuk Strategi Pemasaran,” J. Account. Inf. Syst. (AIMS, vol. 5, no. 2, pp. 156–166, 2022.

W. Lestari and S. Sumarlinda, "Implementation Of K-Nearest Neighbor (KNN) AND Support Vector Machine (SVM) For Clasification Cardiovascular Disease," Int. J. Multiscience, vol. 2, no. 10, pp. 30–36, 2022.

S. Saikin and K. Kusrini, “Model Data Mining Untuk Karekteristik Data Traveller Pada Perusahaan Tour and Travel,” J. Manaj. Inform. dan Sist. Inf., vol. 2, no. 2, p. 61, 2019.

A. Wenda, “Support Vector Machine Untuk Pengenalan Bentuk Manusia Menggunakan Kumpulan Fitur Yang Dioptimalkan,” JST (Jurnal Sains dan Teknol., vol. 11, no. 1, pp. 77–84, 2022.

M. I. Fikri, T. S. Sabrila, and Y. Azhar, “Perbandingan Metode Naïve Bayes dan Support Vector Machine pada Analisis Sentimen Twitter,” Smatika J., vol. 10, no. 02, pp. 71–76, 2020.

M. Azhari, S. Zakaria, and R. Rosnelly, “Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes,” J. Media Inform. Budidarma, vol. 5, no. 2, p. 640, 2021.

M. E. Lasulika, “Komparasi Naïve Bayes, Support Vector Machine Dan K-Nearest Neighbor Untuk Mengetahui Akurasi Tertinggi Pada Prediksi Kelancaran Pembayaran Tv Kabel,” Ilk. J. Ilm., vol. 11, no. 1, pp. 11–16, 2019.

D. A. Anggoro and N. D. Kurnia, "Comparison of accuracy level of support vector machine (SVM) and artificial neural network (ANN) algorithms in predicting diabetes mellitus disease," ICIC Express Lett., vol. 15, no. 1, pp. 9–18, 2021.

S. Sumarlinda, D. A. B. R. Rahmat, and A. P. Z. B. A. Long, "Comparative Analysis of Cardiovascular Diseases Prediction Model Using Decision Tree Learning and Backpropagation Artificial Neuro Network," pp. 0–4, 2023.

G. A. Sandag, “Prediksi Rating Aplikasi App Store Menggunakan Algoritma Random Forest,” CogITo Smart J., vol. 6, no. 2, pp. 167–178, 2020.

I. Arpaci, S. Huang, M. Al-Emran, M. N. Al-Kabi, and M. Peng, "Predicting the COVID-19 infection with fourteen clinical features using machine learning classification algorithms," Multimed. Tools Appl., vol. 80, no. 8, pp. 11943–11957, 2021.

R. Maulana and D. Kumalasari, “Analisis Komparasi Algoritma Klasifikasi Data Mining Untuk Prediksi Status Kelulusan Mahasiswa Akademi Bina Sarana Informatika,” J. Inform. Kaputama, vol. Juni, no. Semantik, pp. 241–249, 2019.

T. K. Kim and J. H. Park, "More about the basic assumptions of t-test: Normality and sample size," Korean J. Anesthesiol., vol. 72, no. 4, pp. 331–335, 2019.

M. N. Ab Wahab, A. Nazir, A. T. Z. Ren, M. H. M. Noor, M. F. Akbar, and A. S. A. Mohamed, "Efficientnet-Lite and Hybrid CNN-KNN Implementation for Facial Expression Recognition on Raspberry Pi," IEEE Access, vol. 9, pp. 134065–134080, 2021.

Mulyawan, A. Bahtiar, G. Dwilestari, F. M. Basysyar, and N. Suarna, "Data mining techniques with machine learning algorithm to predict patients of heart disease," IOP Conf. Ser. Mater. Sci. Eng., vol. 1088, no. 1, p. 012035, 2021.

I. Parlina et al., "Naive Bayes Algorithm Analysis to Determine the Percentage Level of visitors the Most Dominant Zoo Visit by Age Category," J. Phys. Conf. Ser., vol. 1255, no. 1, 2019.

Downloads

Published

2023-06-09

How to Cite

Vania Jusenda Prasetya, Hasanah, H., & Nurmalitasari, N. (2023). Expert System For Detection of Cataracts Disease Using The Certainty Factor Method. International Journal of Information System Technology and Data Science, 1(1), 9–14. https://doi.org/10.61398/ijist-das.v1i1.8

Issue

Section

Articles