Implementation of Data Mining for Mapping the Distribution of Human Immunodeficiency Virus Infection in Riau Province
DOI:
https://doi.org/10.37859/coscitech.v5i1.6712
Abstract
Based on data released by the Riau Provincial Health Service until October 2022, there were 8034 people living with HIV/AIDS (PLWHA), of which 3,711 were in the AIDS stage. Human Immunodeficiency Virus is a virus that attacks the body's immune system, while Acquired ImmunoDeficiency Syndrome (AIDS) is a collection of diseases caused by the HIV virus due to damage to the immune system in humans, resulting in the body being susceptible to potential diseases. This research aims to map the spread of HIV/AIDS in Riau Province to prevent and control the spread of the HIV/AIDS virus by the relevant agencies. The method used in this research is Fuzzy C-Means to carry out clustering in districts/cities which will then be visualized using a map or with a Geography Informatics System (GIS). The Fuzzy C-Means method is a data grouping technique that uses the existence of each data point in A cluster as determined by the degree of membership. The output from Fuzzy C-Means is a series of cluster centers and several degrees of membership for each data point. The data used in this research is HIV/AIDS data in Riau Province from 1997 to 2023. Based on the results of the tests that have been carried out, the results obtained are 3 clusters, namely the safe zone has 5 districts/cities, the alert zone has 5 districts/cities, and There are 2 districts/cities in the dangerous zone. There needs to be treatment through the Health Service, the AIDS Control Commission, and related Non-Governmental Organizations (NGOs) to prevent and control HIV/AIDS in Riau Province for areas that have a high potential for the spread of HIV/AIDS. The tests that have been carried out obtain a minimum error value of 0.008251 in the 8th iteration with the performance of Fuzzy C-Means being 13.271 in the distance between clusters.
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References
[2] F. Novianti, Y. R. Aisyah Yasmin, and D. C. R. Novitasari, “Penerapan Algoritma Fuzzy C-Means (FCM) dalam Pengelompokan Provinsi di Indonesia berdasarkan Indikator Penyakit Menular Manusia,” JUMANJI (Jurnal Masy. Inform. Unjani), vol. 6, no. 1, p. 23, 2022, doi: 10.26874/jumanji.v6i1.103.
[3] D. Andreswari, R. Efendi, and K. Prastio, “Clustering Data Rekam Medis untuk Penentuan Penyakit Endemi di Daerah Kabupaten Bengkulu Selatan dengan Mengimplementasikan Metode FUZZY C-MEANS,” J. Rekursif, vol. 11, no. 1, pp. 42–52, 2023, [Online]. Available: http://ejournal.unib.ac.id/index.php/rekursif/42
[4] Y. Hidayat, A. Nazir, R. M. Candra, S. Sanjaya, and F. Syafria, “Clustering Vaksinasi Penyakit Mulut dan Kuku Menggunakan Algoritma Fuzzy C-Means,” J. Comput. Syst. Informatics, vol. 4, no. 3, pp. 587–593, 2023, doi: 10.47065/josyc.v4i3.3416.
[5] M. Mohammdian-khoshnoud, A. R. Soltanian, A. Dehghan, and M. Farhadian, “Optimization of fuzzy c-means (FCM) clustering in cytology image segmentation using the gray wolf algorithm,” BMC Mol. Cell Biol., vol. 23, no. 1, pp. 1–9, 2022, doi: 10.1186/s12860-022-00408-7.
[6] H. Verma, D. Verma, and P. K. Tiwari, “A population based hybrid FCM-PSO algorithm for clustering analysis and segmentation of brain image,” Expert Syst. Appl., vol. 167, p. 114121, 2021, doi: 10.1016/j.eswa.2020.114121.
[7] M. Rioarda Irfa’i, B. Fatkhurrozi, and I. Setyowati, “Klasifikasi Tingkat Kematangan Buah Kopi Menggunakan Algoritma Fuzzy C-Means,” THETA OMEGA J. Electr. Eng., vol. 2, no. 1, pp. 37–43, 2021.
[8] M. Sipan and R. K. Pramuyanti, “Implementasi Fuzzy C Mean Clustering Menggunakan Segmentasi Warna pada Mata Tua (Presbyopia),” Elektrika, vol. 15, no. 2, p. 113, 2023, doi: 10.26623/elektrika.v15i2.7976.
[9] R. N. Turrahma, A. Nanda Caesario, M. D. Alfajri, R. Gusmanto, and W. K. Oktoeberza, “Implementasi Fuzzy C-Means Untuk Clustering Data Harga Saham Harian Pada PT. Astra International TBK,” J. Rekursif, vol. 11, pp. 64–69, 2023, [Online]. Available: https://ejournal.unib.ac.id/rekursif/article/view/27167/12023
[10] R. A. Ningtyas, Y. N. Nasution, and S. Syaripuddin, “Pengelompokan Kabupaten/Kota Di Pulau Kalimantan Dengan Fuzzy C-Means Berdasarkan Indikator Kemiskinan,” Eksponensial, vol. 13, no. 2, p. 141, 2022, doi: 10.30872/eksponensial.v13i2.1054.
[11] P. S. Saputra, “Perbandingan Algoritma Fuzzy C-Means Dan Algoritma Naive Bayes Dalam Menentukan Keluarga Penerima Manfaat (Kpm) Berdasarkan Status Sosial Ekonomi (Sse) Terendah,” JST (Jurnal Sains dan Teknol., vol. 10, no. 1, pp. 1–8, 2021, doi: 10.23887/jstundiksha.v10i1.23340.
[12] Y. S. Firdaus, “Analisis Klaster Kelurahan di Kota Blitar Berdasarkan Kependudukan Menggunakan Metode Fuzzy C-Means Clustering,” vol. 10, no. 2, pp. 104–116, 2021, doi: 10.24843/JMAT.2022.v12.i02.p153.
[13] S. N. Aisah, A. Nurcahyani, and D. C. Rini, “Implementasi Fuzzy C–Means Clustering (Fcm) Pada Pemetaan Daerah Potensi Transmigrasi Di Jawa Timur,” J. Tek. Inform. UNIKA St. Thomas, vol. 07, pp. 33–40, 2022, doi: 10.54367/jtiust.v7i1.1841.
[14] M. Hutabalian, S. Sunanto, and Januar Al Amien, “Sistem Informasi Geografis Pemetaan Tempat Pembungan Sampah Sementara di Kota Pekanbaru Dengan Mencari Rute Terdekat Menggunakan Algoritma A Star (A*),” J. CoSciTech (Computer Sci. Inf. Technol., vol. 2, no. 2, pp. 33–42, 2022, doi: 10.37859/coscitech.v2i2.2936.
[15] F. N. Amalina and A. I. Achmad, “Perbandingan Fuzzy C-Means Clustering dan Fuzzy Possibilistic C-Means Clustering dalam Pengelompokan Kabupaten/Kota di Jawa Barat terhadap Sumber Air dan Sanitasi Layak Pada Tahun 2020 Berdasarkan Akses,” DataMath J. Stat. Math., vol. 1, no. 1, pp. 27–34, 2023, [Online]. Available: https://journal.sbpublisher.com/index.php/datamath/article/view/16
[16] A. E. Pramitasari and Y. Nataliani, “Perbandingan Clustering Karyawan Berdasarkan Nilai Kinerja Dengan Algoritma K-Means Dan Fuzzy C-Means,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 8, no. 3, pp. 1119–1132, 2021, doi: 10.35957/jatisi.v8i3.957.
[17] A. Syahputra, M. F. Devara, M. Habibi, and H. Asnal, “Sistem informasi geografis pemetaan masjid di desa dayo kabupaten rokan hulu,” J. Comput. Sci. Inf. Technol. CoSciTech, vol. 3, no. 3, pp. 396–405, 2022.
