Komparasi Metode Data Mining K-Nearest Neighbor Dengan Naive Bayes Untuk Klasifikasi Kualitas Air Bersih (Studi Kasus PDAM Tirta Kencana Kabupaten Jombang)

Maulana Aditya Rahman (Fakultas Ilmu Komputer, Universitas Brawijaya)
Nurul Hidayat (Fakultas Ilmu Komputer, Universitas Brawijaya)
Ahmad Afif Supianto (Fakultas Ilmu Komputer, Universitas Brawijaya)

Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Vol 2 No 12 (2018): Desember 2018

Water is a chemical compound that is needed for the survival of living things on earth. The widest area on planet earth is water that covers almost 71% of the region on earth. Water is also a very important substance on earth that is needed by all living things from plants, animals and humans. It takes the supervision and processing of the environment around the water source so as to produce clean water quality in accordance with the standard of clean water quality and meet the standard of water that is suitable for human consumption. To determine the classification of clean water quality there are many methods that can be used. To choose the best classification method, it can be comparated between several methods. This study comparing the K-Nearest Neighbor and Naive Bayes methods. Based on several studies, the K-Nearest Neighbor and Naive Bayes methods are quite good and yield a high degree of accuracy. Based on the test result, the average accuracy value of K-Nearest Neighbor method is 82.42% and the average accuracy of Naive Bayes method is 70.32%. It can be concluded that the best method for water quality classification is K-Nearest Neighbor method.