Skripsi

OPTIMASI RANDOM FOREST MENGGUNAKAN ALGORITMA GENETIKA PADA KLASIFIKASI KUALITAS UDARA

---

Penilaian

---

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.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2407003655	T146017	T1460172024	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1460172024

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xiv, 70 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

006.310 7

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

-

Subject(s)

Algoritma genetika
Prodi Teknik Informatika

Specific Detail Info

-

Statement of Responsibility

KA

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment