Bayes Theorem and Forward Chaining Method On Expert System for Determine Hypercholesterolemia Drugs

Anna Adi Perbawawati(1), Endang Sugiharti(2), Much Aziz Muslim(3),

DOI: https://doi.org/10.15294/sji.v6i1.14149


(1) Semarang State University
(2) Semarang state university
(3) Semarang State University

Abstract

The development of technology capable to imitating the process of human thinking  and led to a new branch of computer science named the expert system. One of the problem that can be solved by an expert system is selecting hypercholesterolemia drugs.  Drug selection starts from find the symptoms and then determine the best drug for the patient. This is consist with the mechanism of forward chaining which starts from searching for information about the symptoms, and then try to illustrate the conclusions. To accommodate the missing fact, expert systems can be complemented with the Bayes theorem that provides a simple rule for calculating the conditional probability so the accuracy of the method approaches the accuracy of the experts. This reseacrh uses 30 training data and 76 testing data of medical record that use hypercholesterolemia drugs from Tugurejo Hospital of Semarang. The variable are common symptoms and some hypercholesterolemia drugs. This research obtained a selection of hypercholesterolemia drugs system with 96.05% accuracy

Keywords

Expert System, Cholesterol, Hypercholesterolemia, Forward Chaining, Bayes.

Full Text:

PDF

References

Muslim, M. A., Kurniawati, I. I. N., & Sugiharti, E. 2015. Expert system diagnosis chronic kidney disease based on mamdani fuzzy inference system. Journal of Theoretical and Applied Information Technology, 78(1), 70.

Holel, K.R and Gulhane, V.S. 2014. Rule-Based Expert System for the Diagnosis of Memory Loss Diseases. IJISET - International Journal of Innovative Science, Engineering & Technology, 1(3).

Dath, A., & Balakrishnan, M. 2013. Development of an expert system for agricultural commodities. International Journal of Computer Science & Applications (TIJCSA), 2(07).

Setiabudi, W. U., Sugiharti, E., & Arini, F. Y. 2017. Expert System Diagnosis Dental Disease Using Certainty Factor Method. Scientific Journal of Informatics, 4(1), 43-50.

Ford, E. S., Li, C., Pearson, W.S., Zhao, G., Mokdad, A. H. 2008. Trends in Hypercholesterolemia, Treatment and Control Among United States Adults. International Juornal of Cardiology, 226-235.

Muslim, M.A. 2012. Pengembangan Sistem Informasi Jurusan Berbasis Web untuk Meningkatkan Pelayanan dan Akses Informasi. Jurnal MIPA 35(1).

Nusai, C., Cheechang, S., Chaiphech, S., & Thanimkan, G. 2015. Swine-Vet: a Web-based Expert System of Swine Disease Diagnosis. Procedia computer science, 63, 366-375.

Bernardo, J. M., & Smith, A. F. 2001. Bayesian theory. Measurement Science and Technology, 12 (2), 221-230

Rohmana, I, & Arifudin, R. 2014. Perbandingan Jaringan Syaraf Tiruan dan Naive Bayes dalam Deteksi Seseorang Terkena Penyakit Stroke. Jurnal MIPA, 37 (2), 178-191.

Rish, I. 2001. An empirical study of The Naive Bayes Classifier. International Joint Conference on Artificial Intelligence-California. 3(22): 41-46.

Wahudi, M. J. 2013. Sistem Pakar untuk Mengidentifikasi Penyakit Udang Galah dengan Metode Theorema Bayes. Jurnal Sarjana Teknik Informatika, 1(1).

Fiarnia, C., & Gunawan, A.S. 2015. Automated Scheduling System for Thesis and Project Presentation Using Forward Chaining Method With Dynamic Allocation Resources. Scient Direct, Procedia Computer Science ,72(1),209 – 216.

Pramesti, A. A., Arifudin, R., & Sugiharti, E. 2016. Expert System for Determination of Type Lenses Glasses Using Forward Chaining Method. Scientific Journal of Informatics, 3(2), 177-188.

Putra, Y.S., Muslim. M.A., & Naba, A. 2013. Game Chicken Roll dengan Menggunakan Metode Forward Chaining. Jurnal EECCIS, 7(1), 41-46.

Triyanto, S., & Fadlil, A. 2013. Sistem Pakar untuk Mendiagnosa Penyakit Kelinci Berbasis Web. JSTIE (Jurnal Sarjana Teknik Informatika)(E-Journal), 2(1), 22-32.

Trihartati, A & Adi, C.K. 2016. An Identification of Tuberculosis (TB) Disease in Humans using Naïve Bayesian Method. Scientific Journal of Informatics, 3(2), 99-108.

Sugiharti, E., Firmansyah, S., & Devi, F. R. 2017. Predictive Evaluation of Performance of Computer Science Students of Unnes Using Data Mining Based on Naïve Bayes Classifier (NBC) Algorithm. Journal of Theoretical and Applied Information Technology 28th February 2017. 95(4), 902.

Pal, S. K. & Shiu, S.C.K. 2004. Foundations of Soft Case Based Reasoning. Canada: Willey & Sons, Inc.

Purwinarko, A. 2014. Model Expertise Management System in Semarang State University, Scientific Journal of Informatics 1(2), 177-184.

Alamsyah, A., & Muna, I. H. 2016. Fuzzy Inference System untuk Penilaian Kinerja Pegawai Perpustakaan dan Pustakawan. Scientific Journal of Informatics, 3(1), 88-98.

Adenowo, A.A.A,. Adenowo. B. A. 2013. Software Engineering Methodologies: A Review of the Waterfall Model and Object-Oriented Approach. International Journal of Scientific & Engineering Research, (4)7, 427:434

Alamsyah, A., & Arus, A. A. 2015. Analisis Sistem Pendaftaran pada Web Forum Ilmiah Matematika Unnes 2014. Scientific Journal of Informatics, 1(1), 107-117.

Handarko, J. L & Alamsyah. 2015. Implementasi Fuzzy Decision Tree untuk Mendiagnosa Penyakit Hepatitis. UNNES Journal of Mathematics, 4(2), 157-164.

Patil, T.R., & Sherekar, S.S. 2013. Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification. International Journal Of Computer Science And Applications,6(2)

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Scientific Journal of Informatics (SJI)
p-ISSN 2407-7658 | e-ISSN 2460-0040
Published By Department of Computer Science Universitas Negeri Semarang
Website: https://journal.unnes.ac.id/nju/index.php/sji
Email: [email protected]

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.