ANALISIS PENGARUH SELEKSI FITUR TERHADAP KINERJA RANDOM FOREST PADA KLASIFIKASI KUALITAS UDARA

onesimus Harefa; Septian Trio Sitohang; Glenn Desmon Sirait; Rado Rama Jaya Manurung; Jaya Tata Hardinata

doi:10.64626/jukomtek.v5i2.717

Penulis

onesimus Harefa universitas hkbp nommensen pematangsiantar
Septian Trio Sitohang Universitas HKBP nommensen Pematangsiantar
Glenn Desmon Sirait Universitas HKBP nommensen Pematangsiantar
Rado Rama Jaya Manurung Universitas HKBP nommensen Pematangsiantar
Jaya Tata Hardinata Universitas HKBP nommensen Pematangsiantar

DOI:

https://doi.org/10.64626/jukomtek.v5i2.717

Kata Kunci:

air quality, machine learning, random forest, feature selection, classification

Abstrak

To reduce the impact of pollution on human health and the environment, air quality is one of the important indicators that must be monitored. The purpose of this study was to evaluate the effect of feature selection on the performance of the Random Forest algorithm on air quality classification. The data is processed through the stages of preprocessing, feature selection, model formation, and evaluation using 10-Fold Cross Validation. This data is used as an experimental method with a quantitative approach using Orange Data Mining. The results of the feature selection resulted in five main attributes: PM10, CO, NO₂, SO₂, and O₃. The Random Forest model yielded an accuracy of 76.6%, an accuracy of 0.764, a recall of 0.766, an F1 score of 0.764, a Matthews correlation coefficient of 0.689, and an area under the curve of 0.948. The results show that although feature selection succeeded in simplifying the model by reducing the number of attributes, the use of all attributes has not been able to improve the performance of Random Forest.

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