COMPARATIVE OF ID3 AND NAIVE BAYES IN PREDICTID INDICATORS OF HOUSE WORTHINESS
DOI:
https://doi.org/10.22216/jit.v14i3.99Keywords:
Decision Tree, Naive Bayes, Confusion Matrix, Binary ClassificationAbstract
Decision making is method of solving problems using certain way / techniques so that can be
accepted. After making some calculations and considerations through several stages, the decision
have taken that decision maker goes through. This stage will be selected until the best decision has
made. Decision-making aims to solve problems that solve problems so that decisions with final
goals can be implemented properly and effectively. This study uses a simulation of decision making
from seven attributes to the proportion of the feasibility of a house based on data from Central
Statistics Agency (BPS). There are several techniques for presenting decision making including: ID3
(decision tree) algorithm concept and Naïve Bayes algorithm. Both classification are learningsupervised
data grouping. ID3 algorithm depicts the relationship in the form of a tree diagram
whereas Naïve Bayes makes use of probability calculations and statistics. As a result, in data
training, decision trees are able to model decision making more accurately. The prediction results
using the decision tree model = 90.90%, while Naïve Bayes = 72.73%. Meanwhile, the speed of the
Naive Bayes algorithm is better
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