Penerapan Algoritma K-Means untuk Klasterisasi Penduduk Miskin pada Kota Pagar Alam
DOI:
https://doi.org/10.14421/jiska.2023.8.1.66-77Keywords:
Data Mining, Poverty, K-Means, Clustering, Black BoxAbstract
The purpose of this study was to obtain a poverty data cluster in Pagar Alam City. The data collection of beneficiaries of the Program Keluarga Harapan (PKH) is not correct, the provision of assistance only pays attention to the criteria for poverty in general, so there are still many poor people who feel more deserving of PKH assistance. To overcome the problem of PKH recipients, it is necessary to cluster the community into various levels, so that the government can know the level of poverty of the community and can provide PKH assistance appropriately. The methods used in this study are CRISP-DM and the K-Means clustering algorithm. The attributes used are Identity Number, Name, Family Family Card Number, Poverty Rate, Pregnant Women, Early Childhood, Elementary School, Junior High School, Senior High School, Elderly, and Family Hope Program Recipient Group. This clustering process produced three clusters, namely cluster_0 as many as 156 people, cluster_1 as many as 82 people, and cluster_2 as many as 233 people. Furthermore, it was developed into a system with the Rapid Application Development (RAD) system development method. Thus producing a K-Means algorithm system to classify the poor in Pagar Alam City. The system test method uses black box testing with the alpha method and obtained database test results with a value of 4, interfaces with a value of 4, functionality of 4.42, and algorithms with a value of 4. In the testing process with UAT, in the system aspect got 87% of users agreed, in the user aspect 86% agreed, and in the interaction aspect 87% of users agreed. So it can be concluded that this system is worth using.
References
Astuti, F. D. (2017). Penerapan Data Mining Untuk Clustering Data Penduduk Miskin Menggunakan Algoritma Hard C-Means. Data Manajemen Dan Teknologi Informasi (DASI), 18(1), 64–69. https://ojs.amikom.ac.id/index.php/dasi/article/view/1836
Bahauddin, A., Fatmawati, A., & Sari, F. P. (2021). Analisis Clustering Provinsi di Indonesia Berdasarkan Tingkat Kemiskinan Menggunakan Algoritma K-Means. Jurnal Manajemen Informatika Dan Sistem Informasi, 4(1), 1–8. https://doi.org/10.36595/misi.v4i1.216
Butarbutar, N., Windarto, A. P., Hartama, D., & Solikhun, S. (2017). Komparasi Kinerja Algoritma Fuzzy C-Means dan K-Means dalam Pengelompokan Data Siswa Berdasarkan Prestasi Nilai Akademik Siswa. Jurasik (Jurnal Riset Sistem Informasi Dan Teknik Informatika), 1(1), 46. https://doi.org/10.30645/jurasik.v1i1.8
Fatmawati, K., & Windarto, A. P. (2018). Data Mining: Penerapan Rapidminer dengan K-Means Cluster pada Daerah Terjangkit Demam Berdarah Dengue (DBD) Berdasarkan Provinsi. Computer Engineering, Science and System Journal, 3(2), 173. https://doi.org/10.24114/cess.v3i2.9661
Feblian, D. (2021). Implementasi Model CRISP-DM untuk Menentukan Sales Pipeline pada PT. X [Universitas Trisakti]. In THESIS-2016. http://repository.trisakti.ac.id/usaktiana/index.php/home/detail/detail_koleksi/0/THE/judul/00000000000000105201/
Jainuddin, J., Agus, F., & Astuti, I. F. (2018). Sistem Informasi Data Kriteria Rakyat Miskin Desa Liang Ilir Kecamatan Kota Bangun. Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer, 13(1), 39. https://doi.org/10.30872/jim.v13i1.1004
Marzuki, I. (2015). Temu Kembali Informasi Big Data Menggunakan K-Means Clustering. SMATIKA JURNAL : STIKI Informatika Jurnal, 5(02), 01–07. https://doi.org/10.32664/SMATIKA.V5I02.75
Masripah, S., & Ramayanti, L. (2020). Penerapan Pengujian Alpha dan Beta pada Aplikasi Penerimaan Siswa Baru. Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, Dan Sosial, 8(1), 100–105. https://doi.org/10.31294/SWABUMI.V8I1.7448
Nasution, I., Windarto, A. P., & Fauzan, M. (2020). Penerapan Algoritma K-Means Dalam Pengelompokan Data Penduduk Miskin Menurut Provinsi. Building of Informatics, Technology and Science (BITS), 2(2), 76–83. https://doi.org/10.47065/bits.v2i2.492
Paramitha, I. A. S. D., Sasmita, G. M. A., & Raharja, I. M. S. (2020). Analisis Data Log IDS Snort dengan Algoritma Clustering Fuzzy C-Means. Majalah Ilmiah Teknologi Elektro, 19(1), 95. https://doi.org/10.24843/MITE.2020.v19i01.P14
Parjito, P., & Permata, P. (2021). Penerapan Data Mining Untuk Clustering Data Penduduk Miskin Menggunakan Metode K-Means. Ainet : Jurnal Informatika, 3(1), 31–37. https://doi.org/10.26618/AINET.V3I1.5878
Rahayu, A. E., Hikmah, K., Yustia, N., & Fauzan, Abd. C. (2019). Penerapan K-Means Clustering Untuk Penentuan Klasterisasi Beasiswa Bidikmisi Mahasiswa. ILKOMNIKA: Journal of Computer Science and Applied Informatics, 1(2), 82–86. https://doi.org/10.28926/ilkomnika.v1i2.23
Rofiqo, N., Windarto, A. P., & Hartama, D. (2018). Penerapan Clustering pada Penduduk yang Mempunyai Keluhan Kesehatan dengan Datamining K-Means. KOMIK (Konferensi Nasional Teknologi Informasi Dan Komputer), 2(1). https://doi.org/10.30865/komik.v2i1.929
Sucipto, A. (2019). Klasterisasi Calon Mahasiswa Baru Menggunakan Algoritma K-Means. Science Tech: Jurnal Ilmu Pengetahuan Dan Teknologi, 5(2), 50–56. https://doi.org/10.30738/jst.v5i2.5829
Sudibyo, N. A., Iswardani, A., Sari, K., & Suprihatiningsih, S. (2020). Penerapan Data Mining pada Jumlah Penduduk Miskin di Indonesia. Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika Dan Statistika, 1(3), 199–207. https://doi.org/10.46306/lb.v1i3.42
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