CLASSIFICATION OF THE POOR IN SUMATERA AND JAVA ISLAND USING NAIVE BAYES ALGORITHM AND NAIVE BAYES ALGORITHM BASED ON PSO
DOI:
https://doi.org/10.34288/jri.v4i3.164Keywords:
classification, naïve bayes, particle swarm optimizationAbstract
The poverty rate in Indonesia is still quite high, due to the large population and uneven development and economic center. With a large population and an archipelagic country that stretches from west to east, it is not easy for the government to level the economy in order to reduce poverty in Indonesia. This study was conducted to classify the poverty rate in districts on the island of Sumatra and Java using Nave Bayes and Nave Bayes based on Particle Swarm Optimization. Thus, it is hoped that the central government and local governments can monitor the implementation of programs in order to reduce poverty rates, especially in districts with high poverty rates. Based on research conducted on the classification of the poor in districts on the island of Sumatra and Java with confusion matrix testing and validation validation techniques using the Naïve Bayes algorithm, the accuracy rate is 59.75% and AUC 0.768 is included in a good classification. While the results of the classification using the Naïve Bayes algorithm based on Particle Swarm Optimization produces an accuracy rate of 82.93% and AUC of 0.849 is included in a good classification. From the results of this study, it can be said that Al-Qur'an Naïve Bayes is a good technique for classification in data mining, and for maximum results using Particle Swarm Optimization.
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