CD Skripsi

Implementasi Algoritma K-Means++ Untuk Klasterisasi Polutan Udara Kota Pekanbaru

---

Increased industrial, transportation, and other anthropogenic activities have led to high emissions of air pollutants that have a significant impact on public health and the environment, especially in big cities such as Pekanbaru. This study implements the K-Means++ algorithm in clustering air pollutants in Pekanbaru City to obtain information on important patterns of the clusters formed. The data used includes concentrations of CO, NO2, O3, SO2, PM2.5 and PM10 pollutants for the period January 1 to December 31, 2024 totaling 8.641 data. The results showed that a cluster of two produced the best Davies-Bouldin Index value, which was 0.99. The interpretation and visualization results show that the cluster categorized as "Tends to Have Poor Air Quality" has a higher average Z-score compared to the cluster categorized as "Tends to Have Good Air Quality" for almost all pollutants, except for the O3 pollutant.

Keywords: Air Pollutants Clustering, Data Mining, Davies-Bouldin Index,
K-Means++, Machine Learning.
 

---

Ketersediaan

Informasi Detail

Judul Seri

-

No. Panggil

2103112023

Penerbit

Pekanbaru : Universitas Riau FMIPA Sistem Informasi., 2025

Deskripsi Fisik

-

Bahasa

Indonesia

ISBN/ISSN

-

Klasifikasi

2103112023

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

SISTEM INFORMASI

Info Detail Spesifik

-

Pernyataan Tanggungjawab

Mutia

Versi lain/terkait

Lampiran Berkas

Komentar

---

Anda harus masuk sebelum memberikan komentar