Analisis K-Medoids Clustering Dalam Pengelompokkan Data Imunisasi Campak Balita di Indonesia

Siti Sundari, Irfan Sudahri Damanik, Agus Perdana Windarto, Heru Satria Tambunan, Jalaluddin Jalaluddin, Anjar Wanto

Abstract


Measles is a contagious infections disease that attacks children caused by a virus. Transmission of measles from people through coughing and sneezing. Measles causes disability and death, so further threatment is needed. Measles immunization program that can inhibit the development of measles is one of the efforts in eradicating the disease. In this study the data used were sourced from the Central Statistics Agency National in 2013-2017. This study uses datamining techniques in data processing with K-Medoids algorithm. The K-Medoids method is a clustering method that functions to break datasets into groups. The advantages of this method are the ability to overcome the weaknesses of the K-Means method which is sensitive to outliers. Another advantage of this algorithm is that the results of the clustering process do not depend on the entry sequence of the dataset. The k-medoids clustering method can be applied to the data on the percentage of measles immunization can be identified based on province, so that the grouping of provinces based on these data. From the data grouping three clusters are obtained: low cluster (2 provinces), medium cluster (30 provinces) and high cluster (2 provinces) with the percentage of measles immunization in each of these provinces from data grouping in percentage. It is expected this research can provide information to the govermant about the data on grouping measles immunization for toddlers in Indonesia which has an impact on the distribution of immunization against measles toddlers in Indonesia.

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References


WHO, “Organisasi Kesehatan Dunia,” www.depkes.go.id (25 Februari 2018), 2015.

Kemenkes RI, Pusat Data dan Informasi Kementerian Kesehatan RI. 2016.

M. G. Sadewo, A. P. Windarto, and S. R. Andani, “Pemanfaatan Algoritma Clustering Dalam Pengelompokkan Jumlah Desa/Kelurahan Yang Memiliki Sarana Kesehatan,” KOMIK(Konferensi Nas. Teknol. Inf. dan Komputer), vol. I, no. 1, 2017.

D. F. Pramesti, M. T. Furqon, and C. Dewi, “Implementasi Metode K-Medoids Clustering Untuk Pengelompokan Data Potensi Kebakaran Hutan / Lahan Berdasarkan Persebaran Titik Panas (Hotspot),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 1, no. 9, 2017.

S. Defiyanti, M. Jajuli, and N. rohmawati W, “Optimalisasi K - Medoid Dalam Pengklasteran Mahasiswa Pelamar Beasiswa dengan Cubic Clustering Criterion,” TEKNOSI, vol. 3, no. 1, 2017.

E. Setyowati, A. Rusgiyono, and M. A. Mukid, “Analisis Pengelompokan Daerah Menggunakan Metode Non- Hierarchical Partitioning K-Medoids dari Hasil Komoditas Pertanian Tanaman Pangan,” J. GAUSSIAN, vol. 4, no. 4, 2015.

D. Listiyanti, Y. A. Syahbana, and S. R. Henim, “Perancangan dan Implementasi Aplikasi Android Penentu Salient Area pada Video dengan Algoritma K-Medoids,” in ANNUAL RESEARCH SEMINAR 2016, 2016, vol. 2, no. 1.




DOI: http://dx.doi.org/10.30645/senaris.v1i0.75

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