COMPARATIVE OF ID3 AND NAIVE BAYES IN PREDICTID INDICATORS OF HOUSE WORTHINESS - Ade Clinton Sitepu; Wanayumini -; Zakarias Situmorang (Jurnal Ipteks Terapan, 2020)
Decision making is method of solving problems using certain way / techniques so that can beaccepted. After making some calculations and considerations through several stages, the decisionhave taken that decision maker goes through. This stage will be selected until the best decision hasmade. Decision-making aims to solve problems that solve problems so that decisions with finalgoals can be implemented properly and effectively. This study uses a simulation of decision makingfrom seven attributes to the proportion of the feasibility of a house based on data from CentralStatistics Agency (BPS). There are several techniques for presenting decision making including: ID3(decision tree) algorithm concept and Naïve Bayes algorithm. Both classification are learningsuperviseddata grouping. ID3 algorithm depicts the relationship in the form of a tree diagramwhereas Naïve Bayes makes use of probability calculations and statistics. As a result, in datatraining, decision trees are able to model decision making more accurately. The prediction resultsusing the decision tree model = 90.90%, while Naïve Bayes = 72.73%. Meanwhile, the speed of theNaive Bayes algorithm is better