OPTIMASI RANDOM FOREST MENGGUNAKAN ALGORITMA GENETIKA PADA KLASIFIKASI KUALITAS UDARA

SUNJABAR, ACHMAD MARIO and Rini, Dian Palupi (2024) OPTIMASI RANDOM FOREST MENGGUNAKAN ALGORITMA GENETIKA PADA KLASIFIKASI KUALITAS UDARA. Undergraduate thesis, Sriwijaya University.

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Abstract

The air quality in a region significantly impacts the health of its residents. The Indonesian government has established the Air Quality Index (AQI) as a parameter to assess air quality and its effects on the health of individuals exposed to the air for several hours to several days. Several studies have found that machine learning methods, particularly the Random Forest algorithm, can be used to classify air quality indices. However, Random Forest has a drawback in terms of processing time as it involves many decision trees. To address this issue, feature selection techniques, such as the Genetic Algorithm, can be employed to identify relevant attributes in the classification, thereby improving accuracy while reducing model complexity. Research results indicate that Random Forest achieves an accuracy ranging from 94% to 94.84%, depending on the number of estimators used. Through the optimization of the Genetic Algorithm, the classification performance of Random Forest can be enhanced, achieving an accuracy between 96.4% and 97.37% using 2 to 4 selected parameters. The significant difference in accuracy between standard Random Forest and Random Forest optimized with the Genetic Algorithm demonstrates the effectiveness of the Genetic Algorithm in improving classification accuracy.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Random Forest, Algoritma Genetika, Klasifikasi, Kualitas Udara
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76 Computer software
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Achmad Mario Sunjabar
Date Deposited:	29 May 2024 05:48
Last Modified:	29 May 2024 05:48
URI:	http://repository.unsri.ac.id/id/eprint/146017

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