IMPLEMENTASI METODE ENSEMBLE UNTUK MENGKLASIFIKASI KUALITAS UDARA PADA MODEL NEURAL NETWORK, K-NEAREST NEIGHBOR DAN FUZZY DECISION TREE DENGAN ALGORITMA ID3

HAITOMI, HAITOMI and Zayanti, Des Alwine and Amran, Ali (2023) IMPLEMENTASI METODE ENSEMBLE UNTUK MENGKLASIFIKASI KUALITAS UDARA PADA MODEL NEURAL NETWORK, K-NEAREST NEIGHBOR DAN FUZZY DECISION TREE DENGAN ALGORITMA ID3. Undergraduate thesis, Sriwijaya University.

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Abstract

Air quality is a form of suitability or level of good or bad levels or certain levels based on the quality in the air itself whose use is done wisely for the sake of future life. Efforts to maintain and improve air quality include pollution control, the use of cleaner and environmentally friendly energy, and the reduction of greenhouse gas emissions. Therefore this study is predicting air quality using the Ensemble method with three classification models. The use of the Ensemble method aims to increase the level of accuracy of better classification. This classification uses the Ensemble Majority Vote method with three algorithm models, namely Neural Network, K-Nearest Neighbor and Fuzzy Decision Tree. The results of this study showed that the level of accuracy using the ensemble method with Majority Vote obtained an accuracy value of 97.46%, precision of 75.85%, recall of 73.18% and f1-score of 74.49%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	kualitas udara, Ensemble, Neural Network, K-Nearest Neighbour, Fuzzy Decision Tree
Subjects:	Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Haitomi Haitomi
Date Deposited:	04 Aug 2023 08:30
Last Modified:	04 Aug 2023 08:30
URI:	http://repository.unsri.ac.id/id/eprint/125446

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