KLASIFIKASI KUALITAS UDARA MENGGUNAKAN RANDOM FOREST DENGAN PENERAPAN REGRESI K-NN DAN SMOTE UNTUK MENGATASI DATA HILANG DAN KELAS TIDAK SEIMBANG

LESTARI, INDAH and Resti, Yulia and Kresnawati, Endang Sri (2025) KLASIFIKASI KUALITAS UDARA MENGGUNAKAN RANDOM FOREST DENGAN PENERAPAN REGRESI K-NN DAN SMOTE UNTUK MENGATASI DATA HILANG DAN KELAS TIDAK SEIMBANG. Undergraduate thesis, Sriwijaya University.

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Abstract

The lives of living things are highly dependent on air quality, especially humans. Air quality prediction that classifies air quality into categories can involve many factors including factors that affect pollution levels. This research classifies air quality based on PM2.5, PM10, NO2, CO, SO2, and O3 which are the main factors that affect pollution. The secondary data used in the study was obtained from Kaggle totaling 108.035. In this data there are more than 10,000 missing values and there is a class imbalance, to fill in the missing values the K-Nearest Neighbor method is used and for balancing class the Synthetic Minority Oversampling Technique (SMOTE) method is used. As for classifying the level of accuracy of air quality, the Random Forest method is used. The results of this study obtained the highest level of accuracy, precision, recall, and F1-score in the Random Forest classification method, respectively 76,192%, 72%, 76%, and 73%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Kualitas Udara, K-Nearest Neighbor, SMOTE, Random Forest
Subjects:	Q Science > QA Mathematics > QA299.6-433 Analysis > Q337.3 Swarm intelligence. Big data -- Social aspects. Information technology -- Economic aspects.
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Indah Lestari
Date Deposited:	25 May 2025 07:48
Last Modified:	25 May 2025 07:48
URI:	http://repository.unsri.ac.id/id/eprint/174064

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