KLASIFIKASI DATA KUALITAS UDARA MENGGUNAKAN METODE FUZZY RANDOM FOREST DENGAN BOOSTRAP SAMPLING

SARTIKA, ELISA and Eliyati, Ning and Zayanti, Des Alwine (2023) KLASIFIKASI DATA KUALITAS UDARA MENGGUNAKAN METODE FUZZY RANDOM FOREST DENGAN BOOSTRAP SAMPLING. Undergraduate thesis, Sriwijaya University.

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Abstract

Air quality has an important role for living things on the surface of the earth, especially for humans, where human health depends on air quality. Therefore, air quality must be kept clean because unclean air will have a negative impact on living things, especially humans. Therefore the purpose of this research is to classify air quality using the fuzzy random forest method with boostrap sampling. The use of the de fuzzy method aims to minimize misclassification and achieve better accuracy. The dataset used refers to air quality in the city of Shanghai, China. The data consisted of 2502 obsrvations and 21 variables where 19 predictor variablesand 2 response variables. The classification uses the fuzzy random forest method based on 3 fuzzy membership functions, namely the S – curve of depreciation, the S – curve of growth, and the triangle curve. The result of this study indicate that the level of accuracy of air quality classification using the fuzzy random forest method with boostrap sampling obtained an accuracy value of 94,66%, precision for macro data is 82,75%, recall for macro data is 90,06%, fscore for macro data is 86,25%, and for values for precision, recall, and fscore on micro data have the same value of 86,65%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Kualitas Udara, Fuzzy Random Forest, Boostrap Sampling
Subjects:	Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics > QA279.M67 Experimental design. Response surfaces (Statistics)
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Elisa Sartika
Date Deposited:	10 Apr 2023 01:52
Last Modified:	10 Apr 2023 01:52
URI:	http://repository.unsri.ac.id/id/eprint/93578

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