Classification of Lung Disease Syndromes in Traditional Chinese Medicine Based on Learning Vector Quantization

In Indonesia, the development of treatment methods of Traditional Chinese Medicine (TCM) has been growing rapidly. This is indicated that TCM as an alternative treatment other than conventional medicine to cure the disease. The problem arises when the students or people who want to learn TCM experiencing difficulty in determining the classification of syndrome. While conventionally, the determination of the classification of syndrome requires sufficient experience for the students or people that begin to learn TCM in order to be able to determine the classification of syndrome. The purpose of this research is to create software application that can help make a decision in determining the classification of lung disease syndrome in TCM. This software application can be used as a learning medium for students and people who learn about the classification of lung disease syndrome in TCM. This research uses Learning Vector Quantization (LVQ) method. As it is known, LVQ has superiority in classifying a data set into several clusters according to weight training on LVQ. The classification of lung syndrome-based LVQ is built by using input as many as 46 symptoms of lung syndrome, and the output as many as 5 types of lung syndrome. The simulation results show that LVQ can be used to classify the type of lung syndrome very well.