Biostatistics

K-MEANS CLUSTER ANALYSIS RELATED TO UNMET NEED FOR FAMILY PLANNING IN BANYUWANGI, INDONESIA: A CASE STUDY

population cluster unmet need family planning

Authors

  • Agustin Putri Pramudiyanti
    agustin.putri.pramudiyanti-2018@fkm.unair.ac.id
    Department of Epidemiology, Population Biostatistics and Health Promotion, Faculty of Public Health, Universitas Airlangga, Indonesia
  • Mitha Farihatus Shafiro Department of Epidemiology, Population Biostatistics and Health Promotion, Faculty of Public Health, Universitas Airlangga, Indonesia
  • Lutfi Agus Salim Department of Epidemiology, Population Biostatistics and Health Promotion, Faculty of Public Health, Universitas Airlangga, Indonesia https://orcid.org/0000-0002-4745-614X
  • Wasyik Bidang Pengendalian Penduduk dan Keluarga Berencana, Dinas Sosial Pemberdayaan Perempuan dan Keluarga Berencana Kabupaten Banyuwangi, Indonesia
March 1, 2024

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Background: The population growth rate in Indonesia from 2010-2020 was 1.25% per year. The rate of population growth must be accompanied by an increase in the quality of human life. Human quality of life begins from within the womb, so that preventive efforts can be undertaken. The Family Planning Program was implemented to overcome the problem of population density so that it becomes more controlled. However, in line with the existence of the family planning program, there are still incidents of unmet need for family planning that occur among couples of productive ages. Purpose: This study aims to undertake a cluster analysis to see which variables are the dominant reasons for couples of childbearing ages to have unmet needs. Methods: This research was conducted using the K-Means cluster analysis method, using secondary data in 25 sub-districts from the Banyuwangi Regency Social, Women's Empowerment and Family Planning Service. Results: Research showed that 3 clusters were formed, each cluster had a dominant incidence of unmet need. Cluster 1 was dominant in Drop Out incidents in 14 sub-districts, Cluster 2 was dominant in IAT incidents in 9 sub-districts, and Cluster 3 was dominant in TIAL incidents in 2 sub-districts. Conclusion: The implementation of cluster grouping can make it easier for officers to focus on reducing the number of unmet need incidents that occur among residents in each sub-district.