JARINGAN SYARAF TIRUAN DENGAN ALGORITMA HEBB RULE UNTUK DIAGNOSA PENYAKIT PARU-PARU

Abstract

Lung diseases are a serious health issue that requires prompt and accurate treatment. However, in practice, medical professionals often face challenges such as high patient volumes, limited examination time, and the similarity of symptoms across different types of lung diseases, which make it difficult to consistently establish an initial diagnosis. This study aims to design and develop a medical decision support system to diagnose lung diseases using an Artificial Neural Network (ANN) with the Hebb Rule algorithm. The types of diseases focused on in this study include Asthma, Bronchitis, COPD, and Pulmonary TB. The research methodology utilized 100 patient medical records from H. OK Arya Zulkarnain General Hospital as training data, consisting of 39 clinical symptom variables. The system was developed using the PHP programming language and a MySQL database. The Hebb Rule algorithm was applied to perform network weight learning so that the system could recognize patterns of relationships between symptoms and disease types based on historical data. The results of the study show that the Hebb Rule algorithm was successfully implemented into a web-based system capable of generating diagnostic decisions based on the highest activation values in the output neurons. This system can process patient symptom data quickly and provide prediction results consistent with the training data patterns. This study concludes that the use of ANNs with the Hebb Rule method is effective as a tool for early detection and decision support for medical personnel to improve the efficiency of healthcare services in hospitals.