Classification Of Population Data On Status In The Family Based On Last Education And Work Using The Clustering Method (Case Study: Sei Prison Village Office)
Keywords:data mining, k-means algorithm, population data
Population data is structured individual or individual data through population registration, civil registration and population census activities. It is important to know population data because in making policies and planning regional or state development, population data is needed to describe the condition of an area. Population data include births, deaths, transfers or migration, population composition, population density and so on. This grouping is done so that population data that is already in the archives will be input into an application that will be designed to make it easier for parties who need data without having to look at the data that is still manual. The problems that exist are such as the increase in the number of residents in a city, village or even a district which is increasing while the population that has been recorded still does not have a job, such as status in the family, namely the head of the family is still there who does not work in terms of recent education can still be considered to get a job that matches the last type of education. From the research process conducted on 20 data, 3 groups were obtained, Cluster 1 contained 16 data, Cluster 2 contained 1 data, and Cluster 3 contained 3 data. And the most group obtained is cluster 1, there is education last high school, has a type of work that has not worked and status in the family of the head of the family.
Arhami, M., & Nasir, M. (2020). Data Mining (R. Indah utami, Ed.; 1st ed.). cv andi offset.
Buaton, R., Saragih, R., & SistemInformasi STMIK Kaputama Binjai Sumatera Utara, P. (2022). Data Mining Pengelompokan Akta Nikah Berdasarkan Usia Nikah atau Domisili Menggunakan Metode Clustering: Studi Kasus Kemenag Langkat. In Sci-Tech Journal (Vol. 2, Issue 1).
Dr. Eng. Ags Naba, Pengertian GUIDE atau GUI Yogyakarta, 2008
Eko Prasetyo, Data Mining Konsep Dan Aplikasi Menggunakan Matlab, PT.Andi, Yogyakarta, 2012
Irmanita Nasution, Agus Perdana Windarto, M.Fauzan. 2020. “Penerapan Algoritma K-Means Dalam Pengelompokan Data Penduduk Miskin Menurut Provinsi.” Vol. 2, No.2, Hal: 76-83 Desember 2020
Kusrini, Emha Taufiq Luthfi, Algoritma Data Mining, Penerbit C.V Andi, Yogyakarta 2009
Lina Listiani, Yoga Handoko Agustin, Mochammad Zaenal Ramdhani. 2019. “Implementasi Algoritma K-Means Clustering Untuk Rekomendasi Pekerjaan Berdasarkan Pengelompokan Data Penduduk.” Seminar Nasional Sistem Informasi dan Teknik Informatika.
Yatini B, Definisi Flowchart, Jakarta PT. Gramedia Widia Sarana Indonesia, 2006
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