Application of the Case Based Learning (CBR) Method to Diagnose Conjunctivitis

Authors

  • Marco Ramadhani Universitas Labuhan Batu
  • Volvo Sihombing Universitas Labuhan Batu
  • Gomal Juni Yanris Universitas Labuhan Batu

DOI:

10.33395/sinkron.v6i1.10908

Keywords:

Application, Diagnosis, Conjunctivitis, Expert System, Case Based Reasoning, Diagnosis

Abstract

Conjunctivitis is an inflammation of the conjunctiva or the outer layer of the eye and the inner lining of the eyelids caused by micro-organisms or viruses, bacteria, fungi, chlamydia, allergies, irritation of chemicals. The problems that arise start from common symptoms that are often shown or are shown by eye diseases such as redness of the eye area and then cause pain and soreness. Diagnosis can be made by an ophthalmologist. In addition, to diagnose eye diseases, an expert system can also be used to pour expert knowledge into an expert system, thus helping diagnose eye diseases. The research objective is to analyze a desktop-based expert system program that contains the knowledge of a trusted expert/doctor who has the ability to be able to diagnose the disease from the eye disease symptoms felt by the patient quickly and precisely. The stages of research carried out in this study include literature study, data collection, system design, system creation, system testing. In addition, the authors developed using the Case-Based Reasoning (CBR) method, which is one method of building an expert system by making decisions from cases with the solution of previous cases to determine the type of conjunctivitis.

GS Cited Analysis

Downloads

Download data is not yet available.

References

Aji, AH, Furqon, MT, & Widodo, AW (2018). Expert System for Diagnosing Pregnant Women Diseases Using the Certainty Factor (CF) Method. Journal of Information Technology and Computer Science Development, 2 (5), 2127–2134. http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/1556

Ananta Dama Putra, P., Adi Purnawan, IK, & Purnami Singgih Putri, D. (2018). Expert System for Diagnosing Eye Diseases with Fuzzy Logic and Naïve Bayes. Merpati Scientific Journal (Information Technology Academic Research Tower). https://doi.org/10.24843/jim.2018.v06.i01.p04

Eclipse, YES, Irfan, M., & Slamet, C. (2017). TECHNOLOGY ACCEPTANCE MODEL IMPLEMENTATION TO MEASURE TEACHER ACCEPTANCE TO LEARNING INNOVATION (Case Study CBR Learning Model in SMK). Journal of Informatics Engineering.

Gunawan, EP, & Wardoyo, R. (2018). An Expert System Using Certainty Factor for Determining Insomnia Acupoint. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 12 (2), 119. https://doi.org/10.22146/ijccs.26328

Ilyas, S., & Yulianti, SR (2017). Eye Disease Science. In Publishing Agency, Faculty of Medicine, University of Indonesia.

Minarni., Indra, W., & Wenda, H ,. (2017). Case-Based Reasoning (CBR) in the Expert System for Identifying Cassava Pests and Diseases in an Effort to Increase Food Crop Productivity. TEKNOIF Journal.

Murni, S., & Riandari, F. (2018). Application of the Bayes Theorem Method to the Expert System to Diagnose Gastric Disease. Prima Journal of Technology and Computer Science (JUTIKOMP). https://doi.org/10.34012/jutikomp.v1i2.226

Heroes, arno reza, & Wibisono, S. (2017). IMPLEMENTATION OF CASE BASED REASONING FOR DIAGNOSIS SYSTEM OF RED CABE PLANT PEST AND DISEASE USING NEYMAN SIMILARITY ALGORITHM. SINTAK.

Putri, TE, Andreswari, D., & Efendi, R. (2016). Implementation of the CBR (Case Based Reasoning) Method in the Selection of Pesticides against Paddy Rice Pests Using the K-Nearest Neighbor (KNN) Algorithm (Case Study of Seluma District). Recursive Journal.

Ramadan, PS, & Pane, UFS (2018). Comparative Analysis of Methods (Certainty Factor, Dempster Shafer and Bayes Theorem) to Diagnose Inflammatory Dermatitis in Children. SAINTIKOM Journal (Journal of Information Management and Computer Science).

Salamun, S. (2018). Application of Nearest Neighbor and CBR Algorithms to the Sexual Behavior Deviation Expert System. Online Journal of Informatics. https://doi.org/10.15575/join.v2i2.97

Shinta, NLP, Kushartomo, W., & Varian, M. (2017). THE EFFECT OF SOIL CBR VALUE AND CONCRETE QUALITY ON THE THICKNESS OF RIGID HOLDING PLATE METHODS. Journal of Muara Sains, Technology, Medicine and Health Sciences. https://doi.org/10.24912/jmstkik.v1i1.436

Sinaga, B., Hasugian, PM, & Manurung, AM (2018). Expert System to Diagnose Smartphone Damage. 3 (1), 333–339.

Zulfikar, WB, & Lukman, N. (2016). COMPARISON OF NAIVE BAYES CLASSIFIER WITH NEAREST NEIGHBOR FOR EYE DISEASES IDENTIFICATION. Online Journal of Informatics. https://doi.org/10.15575/join.v1i2.33

Downloads


Crossmark Updates

How to Cite

Ramadhani, M., Volvo Sihombing, & Gomal Juni Yanris. (2021). Application of the Case Based Learning (CBR) Method to Diagnose Conjunctivitis. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 5(2B), 176-182. https://doi.org/10.33395/sinkron.v6i1.10908