Comparison of running time between C4.5 and k-nearest neighbor (k-NN) algorithm on deciding mainstay area clustering
Developing a sustainable activity needs a good plan, so the programs can be effective and have a clear objective. Therefore, a model to help the analysis is significantly needed in determining the priority area to conduct better development in the future. This research applies the concept of Klassen Typology to analyze PDRB data in Papua Province. Based on the result of using Klassen typology analysis method, there are 4 (four) quadrants of area classification in Papua Province. Twenty nine regencies were analyzed based on PDRB data to investigate which area can be used as the development of priority area in the future. The method used in this study is C4.5 and K-Nearest Neighbor. Time complexity becomes test standard of a particular algorithm to get efficient execution time when it is implemented into programming language. The approach of asymptotic analysis using the concept of Big-O is one of the techniques that is usually used to test time complexity of an algorithm. Based on the test result of both methods, it shows that the result of running time of KNN is more stable than of C4.5 although the analysis of Big-O gives the same complexity.