PERBANDINGAN METODE K-NEAREST NEIGHBOR DAN DECISION TREE UNTUK KLASIFIKASI KUALITAS UDARA BERDASARKAN BOOTSTRAP SAMPLING

FAHIRA, ANINDITHA ARIANI and Zayanti, Des Alwine and Eliyati, Ning (2023) PERBANDINGAN METODE K-NEAREST NEIGHBOR DAN DECISION TREE UNTUK KLASIFIKASI KUALITAS UDARA BERDASARKAN BOOTSTRAP SAMPLING. Undergraduate thesis, Sriwijaya University.

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Abstract

Air can change based on factors that affect it and have an impact on air quality. Air quality plays an important role for organisms on the surface of the earth, especially for humans. Air quality must be clean so as not to have harmful impacts. Classification is a way to determine the level of air quality. Classification testing in this study uses two different methods, namely, K-Nearest Neighbor (KNN) and Decision Tree based on air quality data that has gone through a Bootstrap Sampling process. Therefore, the author tried to conduct research by comparing KNN and Decision Tree methods. The final accuracy of the study was measured by accuracy, precision, recall, and Fscore. The accuracy produced by the KNN method is 97%, 97.03%, 84.25%, and 89.60% respectively The accuracy produced by the Decision Tree method is 95.18%, 89.39%, 80.72%, and 84.45% respectively so that the KNN method shows better accuracy results than the Decision Tree method.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Kualitas Udara, KNN, Decision Tree, Bootstrap Sampling
Subjects:	Q Science > Q Science (General) > Q1-295 General
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Aninditha Ariani Fahira
Date Deposited:	06 Jun 2023 03:46
Last Modified:	06 Jun 2023 03:46
URI:	http://repository.unsri.ac.id/id/eprint/107128

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