%0 Journal Article %1 IJACSA.2013.041105 %A Ahmad Ashari Iman Paryudi, A Min Tjoa %D 2013 %J International Journal of Advanced Computer Science and Applications(IJACSA) %K bayes; classification decision energy k-nearest method; na\&\#239;ve neighbor simulation tool; tree; %N 11 %T Performance Comparison between Na&\#239;ve Bayes, Decision Tree and k-Nearest Neighbor in Searching Alternative Design in an Energy Simulation Tool %U http://ijacsa.thesai.org/ %V 4 %X Energy simulation tool is a tool to simulate energy use by a building prior to the erection of the building. Commonly it has a feature providing alternative designs that are better than the user’s design. In this paper, we propose a novel method in searching alternative design that is by using classification method. The classifiers we use are Na&\#239;ve Bayes, Decision Tree, and k-Nearest Neighbor. Our experiment shows that Decision Tree has the fastest classification time followed by Na&\#239;ve Bayes and k-Nearest Neighbor. The differences between classification time of Decision Tree and Na&\#239;ve Bayes also between Na&\#239;ve Bayes and k-NN are about an order of magnitude. Based on Percision, Recall, F-measure, Accuracy, and AUC, the performance of Na&\#239;ve Bayes is the best. It outperforms Decision Tree and k-Nearest Neighbor on all parameters but precision.

@article{IJACSA.2013.041105, abstract = {Energy simulation tool is a tool to simulate energy use by a building prior to the erection of the building. Commonly it has a feature providing alternative designs that are better than the user’s design. In this paper, we propose a novel method in searching alternative design that is by using classification method. The classifiers we use are Na\&\#239;ve Bayes, Decision Tree, and k-Nearest Neighbor. Our experiment shows that Decision Tree has the fastest classification time followed by Na\&\#239;ve Bayes and k-Nearest Neighbor. The differences between classification time of Decision Tree and Na\&\#239;ve Bayes also between Na\&\#239;ve Bayes and k-NN are about an order of magnitude. Based on Percision, Recall, F-measure, Accuracy, and AUC, the performance of Na\&\#239;ve Bayes is the best. It outperforms Decision Tree and k-Nearest Neighbor on all parameters but precision. }, added-at = {2014-02-21T08:00:08.000+0100}, author = {{Ahmad Ashari Iman Paryudi}, A Min Tjoa}, biburl = {https://www.bibsonomy.org/bibtex/29df66c66fd7e41c87dfc8aa5a29a45a6/thesaiorg}, interhash = {eafba82c35a545a6cef3b872486c9008}, intrahash = {9df66c66fd7e41c87dfc8aa5a29a45a6}, journal = {International Journal of Advanced Computer Science and Applications(IJACSA)}, keywords = {bayes; classification decision energy k-nearest method; na\&\#239;ve neighbor simulation tool; tree;}, number = 11, timestamp = {2014-02-21T08:00:08.000+0100}, title = {{Performance Comparison between Na\&\#239;ve Bayes, Decision Tree and k-Nearest Neighbor in Searching Alternative Design in an Energy Simulation Tool}}, url = {http://ijacsa.thesai.org/}, volume = 4, year = 2013 }