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Perbandingan Algoritma K-Means dan K-Medoids Untuk Pemetaan Daerah Penanganan Diare Pada Balita di Kabupaten Kuningan

*Tri Septiar Syamfithriani orcid scopus  -  Information Systems, Faculty of Computer Science, Universitas Kuningan, Jalan Tjut Nyak Dhien, Cijiho Kuningan, Jawa Barat, Indonesia, Indonesia
Nita Mirantika orcid scopus  -  Information Systems, Faculty of Computer Science, Universitas Kuningan, Jalan Tjut Nyak Dhien, Cijiho Kuningan, Jawa Barat, Indonesia, Indonesia
Ragel Trisudarmo orcid  -  Information Systems, Faculty of Computer Science, Universitas Kuningan, Jalan Tjut Nyak Dhien, Cijiho Kuningan, Jawa Barat, Indonesia, Indonesia
Open Access Copyright (c) 2022 JSINBIS (Jurnal Sistem Informasi Bisnis)

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Abstract
Diarrhea is an endemic disease that contributes to the high mortality rate in Indonesia, especially among children under five. The Kuningan District Health Office had difficulties in monitoring and supervising the spread of diarrheal diseases. This study aims to produce a mapping scheme of priority areas in handling the prevention and control of the spread of diarrheal disease in children under five in Kuningan Regency. The method used is the Data Mining Clustering method by comparing two algorithms, namely the K-Means algorithm and the K-Medoids algorithm. Determination of the optimum number of clusters using the Elbow and Silhouette Coefficient methods. With this method, the result is that in the K-Means algorithm the optimum number of clusters is 3 clusters while the K-Medoids algorithm is 2 clusters. The best cluster evaluation uses the Davies-Bouldin Index (DBI) method and the results show that the K-Means DBI value is always smaller than the K-Medoids algorithm in either 2 clusters or 3 clusters, this shows that the K-Means algorithm is better than the K-Medoids algorithm. Based on these results, it is recommended to map priority areas for handling diarrheal diseases using the K-Means algorithm with 3 clusters, namely medium priority areas consisting of 9 regions, high priority areas consisting of 3 regions and low priority areas consisting of 25 regions. The results of the mapping can be used as input for the Kuningan District Health Office to develop strategies for preventing and preventing diarrheal diseases in children under five.
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Keywords: Data Mining; K-Means; K-Medoids; Diarrhea.

Article Metrics:

  1. Adiana, B.E., 2018. Analisis segmentasi pelanggan menggunakan kombinasi rfm model dan teknik clustering. Jurnal Terapan Teknologi Informasi 2(1), 23-32
  2. Andini, A.D., 2020. Implementasi algoritma k-medoids untuk klasterisasi data penyakit pasien di RSUD Kota Bandung. Jurnal Responsif: Riset Sains dan Informatika 2(2), 128-138
  3. Aryuni, M.M., 2018. Penerapan k-means dan k-medoids clustering pada data internet banking di Bank XYZ. Jurnal Teknik dan Ilmu Komputer
  4. Chapman, P.C., 1999. The CRISP-DM user guide. In 4th CRISP-DM SIG Workshop in Brussels in March, Vol. 1999. Brussels: CRISP-DM SIG Workshop
  5. Dinkes, P.J., 2020. Profil kesehatan Provinsi Jawa Barat tahun 2020. Bandung: Dinas Kesehatan Provinsi Jawa Barat
  6. Farahdinna, F.N., 2019. Perbandingan algoritma k-means dan k-medoids dalam klasterisasi produk asuransi perusahaan nasional. Jurnal Ilmiah Fifo 11(2), 208-214
  7. Farissa, R.A., 2021. Perbandingan algoritma k-means dan k-medoids untuk pengelompokkan data obat dengan silhouette coefficient di Puskesmas Karangsambung. Journal of Applied Informatics and Computing (JAIC) 5(2), 109-116
  8. Gie, W., Jollyta, D., 2020. Perbandingan euclidean dan manhattan untuk optimasi cluster menggunakan davies bouldin index: status covid-19 wilayah Riau. Seminar Nasional Riset Information Science (SENARIS) Vol. 2, 187-191
  9. Hermawati, F.A., 2013. data mining . Yogyakarta: Andi Offset
  10. Hung, P.D., 2019. Customer segmentation using hierarchical agglomerative clustering. International Conference on Information Science and Systems, 33-37
  11. Kartikasari, M.D., 2021. Self-organizing map menggunakan davies-bouldin index dalam pengelompokan Wilayah Indonesia berdasarkan konsumsi pangan. Jambura Journal of Mathematics 3(2), 187-196
  12. Kemenkes., 2020. Profil Kesehatan Indonesia. Jakarta: Kementerian Kesehatan Republik Indonesia
  13. Mirantika, N., 2021. Penerapan algoritma k-means clustering untuk pengelompokan penyebaran covid-19 di Provinsi Jawa Barat. Nuansa Informatika 15(2), 92-98
  14. Monalisa, S.N., 2019. Analysis for customer lifetime value categorization with RFM model. Procedia Computer Science 161, 834-840
  15. Riyanto, B., 2019. Penerapan algoritma k-medoids clustering untuk pengelompokkan penyebaran diare di Kota Medan (Studi Kasus: Kantor Dinas Kesehatan Kota Medan). KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) 3(1), 562-568
  16. Selviana, T.E., 2017. Faktor-faktor yang berhubungan dengan kejadian diare pada anak usia 4-6 Tahun. Jurnal Vokasi Kesehatan 3(1), 28–34
  17. Supriyadi, A.T., 2021. Perbandingan algoritma k-means dengan k-medoids pada pengelompokan armada kendaraan truk berdasarkan produktivitas. JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) 6(2), 229-240
  18. Utomo, W., 2021. The comparison of k-means and k-medoids algorithms for clustering the spread of the covid-19 outbreak in Indonesia. ILKOM Jurnal Ilmiah 13(1), 31-35
  19. Vercellis, C., 2009. Business intelligence: data mining and optimization for decision making. John Wiley & Sons

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