Diagnosa Penyakit Tuberkulosis Paru Menggunakan Metode Forward Chaining dan Certainty Factor

Wira Wirdawati; Rini  Sovia; Billy  Hendrik

doi:10.55382/jurnalpustakaai.v5i2.1217

Penulis

Wira Wirdawati Universitas Putra Indonesia YPTK Padang http://orcid.org/0009-0006-3927-3796
Rini Sovia
Billy Hendrik

DOI:

https://doi.org/10.55382/jurnalpustakaai.v5i2.1217

Kata Kunci:

Pulmonary Tuberculosis, Expert System, Forward Chaining, Certainty Factor, Disease Diagnosis, Artificial Intelligence

Abstrak

Tuberculosis (TB) is an infectious disease that can affect people of all ages, including children, adolescents, and the elderly, and can cause illness and death in over one million people. The disease is spread through coughs or sneezes by people with pulmonary TB, through contaminated saliva, and inhalation by healthy people with weakened immune systems. Therefore, this study aims to develop an expert system to assist in the diagnosis of pulmonary tuberculosis using the Forward Chaining and Certainty Factor methods. This process begins by identifying symptoms reported by the user and then searching for rules in the knowledge base that match those symptoms. This method allows the system to follow a logical flow of reasoning similar to the way a doctor diagnoses a disease. This study used data from 100 patients from 2023 at the Pariaman Community Health Center. Using the Forward Chaining and Certainty Factor methods, three patient data sets with three types of tuberculosis were tested. The percentage results for each type of disease were 100% positive for pulmonary tuberculosis, 0.91% negative for pulmonary tuberculosis, and 0.92% latent for pulmonary tuberculosis, with a confidence level of Very Confident. This research contributes to increasing knowledge and understanding in the field of expert systems, particularly in the application of the Forward Chaining and Certainty Factor methods for diagnosing tuberculosis.

Unduhan

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