Comparison of Data Mining Methods in Classifying Village Status of Purwakarta and West Bandung Regencies (Podes 2021)
DOI:
https://doi.org/10.29303/emj.v6i1.156Keywords:
Villages Status, Decision Tree, Support Vector Machine, Naïve Bayes, Random ForestAbstract
Each village has different characteristics and is constantly changing along with the level of development in a village. These changes in conditions are used as indicators to classify villages into urban or rural village status. In this study, researchers will compare or evaluate of several data mining methods, namely decision trees, support vector machines, naïve bayes, and random forests to find the best algorithm in classifying urban villages and rural villages in Purwakarta and West Bandung Regencies. The data used in this study were 357 records and 8 attributes sourced from village potential data (Podes 2021). Furthermore, it was obtained that the best method in classifying urban villages and rural villages is to use random forests with accuracy value and F- score of 0,9.References
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