Diagnose Skin Face Problems by Comparing Classification Algorithms

Data mining is a process of analyzing a set of existing data in the database so that information is obtained that is used in the next stage. Classification is a technique by forming models from unclassified data, to be used to classify new data. Classification is included in the type of supervised learning, meaning that training data is needed to build a classification model. Each classification has a choice of algorithms, which are often used are the naive Bayes algorithm, k-nearest neighbor, decision tree and support vector machine. In this study, a comparison was made to a case study of decision making in skin cases. The research method used in this study is a training method using data training and testing mode using data testing with a comparison of training and testing 70%: 30%, 80%: 20%, 90%: 10% by naive bayes algorthm, K-nearest neighbor, decision tree. Evaluate the training and testing carried out on the naive Bayes classification algorithm, nearest neighbor, decision tree in this study, namely how much accuracy the algorithm produces. The results of the comparison of the nearest neighbor, naive Bayes and decision tree classification algorithms used in the case study of clothing pattern selection decision making state that the decision tree classification algorithm is the classification algorithm that has the highest level of accuracy, reaching 97.5% in testing.