Komparasi Algoritma K-Nearest Neighbor dan Naive Bayes pada Klasifikasi Tingkat Kualitas Udara Kota Tangerang Selatan

Avira Budianita (Universitas Muhammadiyah Kudus)
Nurul Iman (Universitas Muhammadiyah Kudus)
Fida Maisa Hana (Universitas Muhammadiyah Kudus)
Cikita Berlian Hakim (Universitas Wahid Hasyim)

Jurnal Informatika dan Rekayasa Perangkat Lunak
Vol 6, No 1 (2024): Maret

The growth of technology and the impact of industrial activities on the earth have an influence on environmental changes, including changes that are felt are a decrease in air quality or air pollution which has an impact on the health of the human body. Based on this, this research aims to produce a model for solving air quality classification problems based on parameter indicators. A comparative evaluation was also carried out on the classification of the K-Nearest Neighbor and Naive Bayes algorithm methods on the air quality dataset in South Tangerang in 2022. At the same ratio in the classification process, the K-Nearest Neighbor algorithm got an accuracy value of 94.44% and the Naive Bayes algorithm got an accuracy value of 94.44%. Accuracy value 86.11%. From the results of testing the data, it can be concluded that the K-Nearest Neighbor algorithm has high accuracy compared to the Naive Bayes algorithm in air level classification.