CLUSTERING PENDUDUK WILAYAH TANGERANG KOTA YANG SUDAH TERVAKSINASI TAHUN 2022 DENGAN ALGORITMA K-MEANS DAN VISUALISASI WEBGIS

SUBROTO, BAMBANG (2022) CLUSTERING PENDUDUK WILAYAH TANGERANG KOTA YANG SUDAH TERVAKSINASI TAHUN 2022 DENGAN ALGORITMA K-MEANS DAN VISUALISASI WEBGIS. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

COVID-19 has been declared a global pandemic. In Indonesia, President Joko Widodo said that the first case of Covid-19 entered Indonesia, namely on March 2, 2020. Starting from this case, the number of cases of Indonesian people infected with the Covid-19 virus continues to grow every day. Seeing that there are too many and too dangerous Covid 19, various efforts have been made by the Indonesian government, one of which is the Covid 19 vaccination. Based on the tangerangkota.go.id page, the vaccination rate that has been carried out in Tangerang city has reached 94%, not yet reaching 100%. This problem allows for the need for a vaccination clustering for the Tangerang City area. therefore the authors conducted an experiment using the K-Means algorithm for clustering residents of Tangerang City who had been vaccinated. This clustering will be divided into 3 clusters. The division into 3 clusters is determined using the elbow method by looking at the smallest Sum of Square Error (SSE) value. the results of the clustering are cluster 0 for neighborhoods which has the highest vaccinated population which has a total of 25 neighborhoods, cluster 1 for neighborhoods which has a moderately vaccinated population which has a total of 58 neighborhoods, cluster 2 for neighborhoods which has a low vaccinated population which has a total of 21 neighborhoods. From these results visualization was carried out with WebGIS in order to make it easier to read the clustering results. Kata Kunci : COVID-19, vaccination, K-Means, neighborhoods COVID-19 telah dinyatakan sebagai pandemi global. Di Indonesia Presiden Joko Widodo mengatakan bahwa kasus pertama Covid-19 masuk ke Indonesia yaitu pada tanggal 2 Maret 2020 lalu. Berawal dari kasus tersebut, jumlah kasus masyarakat Indonesia yang terinfeksi virus Covid-19 terus bertambah setiap harinya. Melihat terlalu banyaknya dan bahanya Covid 19, berbagai upaya dilakukan oleh pemerintah indonesia salah satunya vaksinasi Covid 19. Berdasarkan laman tangerangkota.go.id vaksinasi yang telah dilakukan di tangerang kota mencapai 94 %, belum mencapai 100 %. Masalah tersebut memungkinkan perlunya sebuah pengelompokan/Clustering vaksinasi daerah Tangerang Kota. maka dari itu penulis melakukan eksperimen dengan menggunakan algoritma K-Means untuk clustering penduduk Tangerang Kota yang sudah tervaksinasi. clustering ini akan dibagi menjadi 3 cluster. Pembagian menjadi 3 cluster tersebut ditentukan dengan menggunakan metode elbow dengan melihat nilai (SSE) Sum of Square Error terkecil. hasil dari clustering tersebut yaitu cluster 0 untuk kelurahan yang memiliki penduduk tervaksinasi tertinggi yang mmemiliki total 25 kelurahan, cluster 1 untuk kelurahan yang memiliki penduduk tervaksinasi sedang yang memiliki total 58 kelurahan, cluster 2 untuk kelurahan yang memiliki penduduk tervaksinasi rendah yang memiliki total 21 kelurahan. dari hasil tersebut dilakukan visualisasi dengan WebGIS agar untuk mempermudah membaca dari hasil clusterisasi. Kata Kunci : COVID-19, Vaksinasi, K-Means, Kelurahan

Item Type: Thesis (S1)
Call Number CD: FIK/INFO. 22 195
Call Number: SIK/15/23/013
NIM/NIDN Creators: 41519010067
Uncontrolled Keywords: COVID-19, Vaksinasi, K-Means, Kelurahan
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
000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 020 Library and Information Sciences/Perpustakaan dan Ilmu Informasi > 025 Operations, Archives, Information Centers/Operasional Perpustakaan, Arsip dan Pusat Informasi, Pelayanan dan Pengelolaan Perpustakaan > 025.3 Bibliographic Analysis and Control/Bibliografi Analisis dan Kontrol Perpustakaan > 025.35 Cooperative Cataloging, Classification, Indexing/Pengatalogan Khusus, Klasifikasi, Pengindeksan
500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik
500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
Divisions: Fakultas Ilmu Komputer > Informatika
Depositing User: ADELINA HASNA SETIAWATI
Date Deposited: 24 Mar 2023 02:47
Last Modified: 24 Mar 2023 03:10
URI: http://repository.mercubuana.ac.id/id/eprint/75380

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