PENERAPAN ALGORITMA K-MEANS CLUSTERING MENGGUNAKAN METODE ELBOW UNTUK ANALISA POLUSI UDARA DI KOTA YOGYAKARTA BERDASARKAN PARAMETER INDEKS STANDAR PENCEMAR UDARA (ISPU) PERIODE 2021

EKAPUTRA, MUHAMMAD RIZALDY (2023) PENERAPAN ALGORITMA K-MEANS CLUSTERING MENGGUNAKAN METODE ELBOW UNTUK ANALISA POLUSI UDARA DI KOTA YOGYAKARTA BERDASARKAN PARAMETER INDEKS STANDAR PENCEMAR UDARA (ISPU) PERIODE 2021. S1 thesis, Universitas Mercu Buana - Menteng.

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Abstract

Lingkungan yang sehat memiliki pengaruh terhadap kesehatan fisik makhluk hidup. Faktor krusial penunjang lingkungan yang sehat ialah kualitas udara yang memenuhi baku kesehatan. Indeks Standar Pencemar Udara yang selanjutnya disingkat ISPU adalah angka yang tidak mempunyai satuan yang menggambarkan kondisi mutu udara ambien di lokasi tertentu, yang didasarkan kepada dampak terhadap kesehatan manusia dan makhluk hidup lainnya. ISPU meliputi parameter seperti partikulat (PM10), partikulat (PM2.5), karbon monoksida (CO), nitrogen dioksida (NO2), sulfur dioksida (SO2) dan ozon (O3). Berdasarkan parameter ISPU tersebut, akan dilakukan pengelompokkan (clustering) untuk menggali atau menemukan informasi yang belum diketahui sebelumnya. Algoritma K-means banyak digunakan dalam proses pengelompokan data karena memungkinkan menemukan pola dan korelasi dalam data dengan cara yang tidak diawasi. Pengelompokkan yang dilakukan terbagi menjadi 6 kelompok (cluster). Cluster 2 merupakan cluster dengan polusi tertinggi yang memiliki kadar PM2.5 dan karbon monoksida (CO) dengan nilai rata-rata tertinggi. Disusul oleh cluster 4 yang berkategorikan sedang sebesar 10,68% dengan kadar PM2.5 yang cukup tinggi, namun cluster 4 juga memiliki persentase kategori baik terbesar sebesar 32,87%. Cluster 0, 3 dan 5 sama-sama memiliki kategori sedang di bawah 5% sedangkan cluster 1 tidak memiliki kategori sedang. Kata Kunci : Kualitas Udara, Indeks Standar Pencemar Udara, Clustering, K- Means, Metode Elbow A healthy environment has an influence on the physical health of living things. The crucial factor supporting a healthy environment is air quality that meets health standards. The Air Pollution Standard Index, hereinafter abbreviated as ISPU, is a number that does not have a unit that describes the condition of ambient air quality in a certain location, which is based on the impact on human health and other living things. ISPU includes parameters such as particulates (PM10), particulates (PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2) and ozone (O3). Based on the ISPU parameters, clustering will be carried out to explore or find information that was not previously known. The K-means algorithm is widely used in the process of grouping data because it allows finding patterns and correlations in data in an unsupervised way. The grouping is divided into 6 groups (clusters). Cluster 2 is the cluster with the highest pollution having PM2.5 and carbon monoxide (CO) levels with the highest average value. Followed by cluster 4 which is in the moderate category at 10.68% with quite high levels of PM2.5, but cluster 4 also has the largest percentage of the good category at 32.87%. Clusters 0, 3 and 5 both have a moderate category below 5% while cluster 1 does not have a moderate category. Keywords : Air Quality, Pollutant Standards Index, Clustering, K-Means, Elbow Method

Item Type: Thesis (S1)
NIM/NIDN Creators: 41518120119
Uncontrolled Keywords: Kualitas Udara, Indeks Standar Pencemar Udara, Clustering, K- Means, Metode Elbow,Air Quality, Pollutant Standards Index, Clustering, K-Means, Elbow Method
Subjects: 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika
Divisions: Fakultas Ilmu Komputer > Informatika
Depositing User: MELATI CAHYA FITRIANI
Date Deposited: 09 Aug 2023 05:00
Last Modified: 09 Aug 2023 05:00
URI: http://repository.mercubuana.ac.id/id/eprint/80056

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