Text

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

---

Penilaian

---

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%.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003098	T174064	T1740642025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1740642025

Publisher

: Prodi Ilmu Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Sriwijaya., 2025

Collation

xvii, 65 hlm.: Ilus., tab.; 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

510.07

Content Type

-

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Matematika

Specific Detail Info

-

Statement of Responsibility

EM

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment