KLASIFIKASI KUALITAS UDARA MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR (K-NN) DI KABUPATEN OGAN KOMERING ILIR SUMATERA SELATAN

JUWITA, MERI and Miraswan, Kanda Januar and Junia, Kurniati (2024) KLASIFIKASI KUALITAS UDARA MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR (K-NN) DI KABUPATEN OGAN KOMERING ILIR SUMATERA SELATAN. Undergraduate thesis, Sriwijaya University.

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Abstract

Ogan Komering Regency (OKI) is one of the regencies in South Sumatra Province which is prone to forest fire cases. The higher the level of forest fires that occur, the higher the level of air pollution that occurs. For this reason, it is necessary to accurately measure and classify air quality levels every day. This research aims to classify air quality in Ogan Komering Ilir Regency using the K-Nearest Neighbor (KNN) method. Using 641 ISPU data with 7 attributes. Daily air quality data is collected and classified based on the Air Pollution Standard Index (ISPU) categories, namely Good, Moderate, Unhealthy, Very Unhealthy and Hazardous. The classification process is carried out using the K-Nearest Neighbor algorithm and the results are evaluated using the Confusion Matrix to calculate accuracy, precision, recall and F1-Score. Based on test results, the K=1 value provides the highest accuracy of 98.97%. Meanwhile, the values K=3 to K=7 show accuracy between 98.86% to 98.52%, and the values K=9 to K=19 remain consistent with high accuracy in the range 98.41% to 98.18%. Overall, this research shows that the KNN method is able to classify air quality with an average accuracy of 98.52%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Kualitas Udara, K-Nearest Neighbor, Klasifikasi, Indeks Standar Pencemaran Udara, Kabupaten Ogan Komering Ilir.
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Meri Juwita
Date Deposited:	03 Sep 2024 07:32
Last Modified:	03 Sep 2024 07:32
URI:	http://repository.unsri.ac.id/id/eprint/156533

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