ANALISIS CLUSTERING ANGKA HARAPAN HIDUP BERDASARKAN JENIS KELAMIN MENGGUNAKAN ALGORITMA K-MEANS DI WILAYAH INDONESIA

IBRAHIM, BAGAS MAULANA (2024) ANALISIS CLUSTERING ANGKA HARAPAN HIDUP BERDASARKAN JENIS KELAMIN MENGGUNAKAN ALGORITMA K-MEANS DI WILAYAH INDONESIA. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Life expectancy is a key indicator used to assess the quality of health and welfare in a region. This study aims to analyze life expectancy in Indonesia based on gender using the K-Means algorithm. The data used includes life expectancy figures from various regencies/cities in Indonesia for the period 2017 to 2023, obtained from the official website of the Central Statistics Agency (BPS). The K-Means clustering method was employed to group regions based on similarities in life expectancy characteristics. This study also used three evaluation methods to determine the optimal number of clusters, namely the Elbow Method, Silhouette Score, and Davies-Bouldin Index. The results of this study indicate that there are three main clusters for male and female life expectancy. The first cluster consists of regions with high life expectancy, the second cluster with medium life expectancy, and the third cluster with low life expectancy. Keywords: Life Expectancy, Clustering, K-Means, Elbow Method, Silhouette Score, Davies-Bouldin Index Angka harapan hidup merupakan salah satu indikator utama yang digunakan untuk menilai kualitas kesehatan dan kesejahteraan suatu wilayah. Penelitian ini bertujuan untuk menganalisis angka harapan hidup di Indonesia berdasarkan jenis kelamin menggunakan algoritma K-Means. Data yang digunakan mencakup angka harapan hidup dari berbagai kabupaten/kota di Indonesia untuk periode 2017 hingga 2023, yang diperoleh dari website Badan Pusat Statistik (BPS). Metode clustering KMeans digunakan untuk mengelompokkan wilayah berdasarkan kesamaan karakteristik angka harapan hidup. Penelitian ini juga menggunakan tiga metode evaluasi untuk menentukan jumlah klaster optimal, yaitu Elbow Method, Silhouette Score, dan Davies-Bouldin Index. Hasil dari penelitian ini menunjukkan bahwa terdapat tiga klaster utama untuk angka harapan hidup laki-laki dan perempuan. Klaster pertama terdiri dari wilayah-wilayah dengan angka harapan hidup yang tinggi, klaster kedua dengan angka harapan hidup sedang, dan klaster ketiga dengan angka harapan hidup yang rendah. Kata Kunci: Angka Harapan Hidup, Clustering, K-Means, Elbow Method, Silhouette Score, Davies-Bouldin Index

Item Type: Thesis (S1)
Call Number CD: FIK/INFO. 24 186
Call Number: SIK/15/24/134
NIM/NIDN Creators: 41520010177
Uncontrolled Keywords: Angka Harapan Hidup, Clustering, K-Means, Elbow Method, Silhouette Score, Davies-Bouldin Index
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 > 070 Documentary Media, Educational Media, News Media, Journalism, Publishing/Media Dokumenter, Media Pendidikan, Media Berita, Jurnalisme, Penerbitan > 070.1-070.9 Standard Subdivisions of Documentary Media, Educational Media, News Media, Journalism, Publishing/Subdivisi Standar Dari Media Dokumenter, Media Pendidikan, Media Berita, Jurnalisme, Penerbitan > 070.4 Journalism/Jurnalisme, Jurnalistik, Pers > 070.48 Journalism Directed to Special Groups/Jurnalistik Disutradarai oleh Kelompok Khusus > 070.483 Groups by Age and Sex/Kelompok Menurut Usia dan Jenis Kelamin
500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 512 Algebra/Aljabar > 512.7 Number Theory/Teori Angka, Teori Bilangan > 512.75 Geometry of Numbers/Geometri Angka
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: khalimah
Date Deposited: 30 Aug 2024 09:37
Last Modified: 30 Aug 2024 09:37
URI: http://repository.mercubuana.ac.id/id/eprint/90931

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