Application of the K-Medoids Algorithm in Clustering PAM Customers Based on Provinces in Indonesia
DOI:
https://doi.org/10.35870/ijsecs.v4i1.2229Keywords:
K-Medoids Algorithm, PAM Customer Clusterization, PAM Water ServicesAbstract
Clean water is a basic need that is very important for human life. In Indonesia, many clean water companies supply water for community needs. Indonesia's government-owned clean water company has many customers spread across various provinces. This research aims to apply the k-medoids algorithm in clustering PAM customers based on provinces in Indonesia to determine which provinces are included in the low, medium, and high-level clusters of PAM customers. The data used in this research is on the number of PAM customers, the amount of water distributed, and the value of the water distributed from 1998 to 2021 to 34 provinces in Indonesia. This research obtained 3 clusters, namely low, medium, and high-level clusters determined by the number of PAM customers, the amount of water distributed, and the value of the water distributed in each province. Cluster 1 (C1) is a low-level cluster that obtained results from 19 provinces, Cluster 2 (C2) is a medium-level cluster that obtained results from 11 provinces, and Cluster 3 (C3) is a high-level cluster that obtained results from 4 provinces. And get a k-medoids cluster validation value of 0.014846718. In this way, it is hoped that the government can identify areas in each province that require improvement or improvement in making appropriate decisions regarding clean water resources to provide better services to customers
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Simanjuntak, D. S. M., Gunawan, I., Sumarno, S., Poningsih, P., & Sari, I. P. (2023). Penerapan Algoritma K-Medoids Untuk Pengelompokkan Pengangguran Umur 25 tahun Keatas Di Sumatera Utara. J. Krisnadana, 2(2). https://doi.org/10.58982/krisnadana.v2i2.264
Anggarwati, D., Nurdiawan, O., Ali, I., & Kurnia, D. A. (2021). Penerapan Algoritma K-Means Dalam Prediksi Penjualan Karoseri. J. Data Sci. Inform., 1(2), 58–62. [Online]. Available: https://www.publikasi.bigdatascience.id/index.php/jdsi/article/view/32/15
Pangestu, A., & Ridwan, T. (2022). Penerapan Data Mining Menggunakan Algoritma K-Means Pengelompokan Pelanggan Berdasarkan Kubikasi Air Terjual Menggunakan Weka. JUST IT J. Sist. Informasi, Teknol. Inf. dan Komput., 11(3), 67–71.
Simanjuntak, A. D. (2022). Data Mining Kepuasan Pelanggan Terhadap Pelayanan Pdam Menggunakan Metode Clustering. Semin. Nas. Inform., 6(3). [Online]. Available: http://jurnal.kaputama.ac.id/index.php/SENATIKA/article/view/947/0%0Ahttp://jurnal.kaputama.ac.id/index.php/SENATIKA/article/viewFile/947/672
Fajriana, F. (2021). Analisis Algoritma K-Medoids pada Sistem Klasterisasi Produksi Perikanan Tangkap Kabupaten Aceh Utara. J. Edukasi dan Penelit. Inform., 7(2), 263. https://doi.org/10.26418/jp.v7i2.47795
Lestari, P. D., Kecomberan, D., Talun, K., Clustering, M., Clustering, A. K., & Pengguna, N. (2023). DATAMINING PADA PENJUALAN AIR BERSIH DI SPAM AKIDAH MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING, 7(1), 412–416.
Ula, M., Maulana, R., & Ilhadi, V. (2023). Terbit online pada laman web jurnal: https://ejurnalunsam.id/index.php/jicom/ Penerapan KNN Penentuan Pelanggan Baru PDAM dan Clustering K-Means Berdasarkan Wilayah, 04(01), 1–6. [Online]. Available: https://ejurnalunsam.id/index.php/jicom/.
Triyandana, G., Putri, L. A., & Umaidah, Y. (2022). Penerapan Data Mining Pengelompokan Menu Makanan dan Minuman Berdasarkan Tingkat Penjualan Menggunakan Metode K-Means. J. Appl. Informatics Comput., 6(1), 40–46. https://doi.org/10.30871/jaic.v6i1.3824
Pahlawan, M. R., Nurlia, N., Laba, A. R., Pakki, E., & Hardiyono, H. (2019). Pengaruh Kualitas Produk Dan Kualitas Pelayanan Terhadap Peningkatan Kepuasan Dan Loyalitas Pelanggan Perusahaan Daerah Air Minum (Pdam) Kota Makassar. J. Appl. Bus. Adm., 3(2), 228–244. https://doi.org/10.30871/jaba.v3i2.1560
Sinaga, L., Ahmad, A., & Safii, M. (2019). Penerapan Data Mining Pada Jumlah Pelanggan Perusahaan Air Bersih Menurut Provinsi Menggunakan Metode K-Means Clustering. J. Resist. (Rekayasa Sist. Komputer), 2(2), 119–125. https://doi.org/10.31598/jurnalresistor.v2i2.418.
Yaumi, A. S., Zulfiqkar, Z., & Nugroho, A. (2020). Klasterisasi Karakter Konsumen Terhadap Kecenderungan Pemilihan Produk Menggunakan K-Means. JOINTECS (Journal Inf. Technol. Comput. Sci., 5(3), 195. https://doi.org/10.31328/jointecs.v5i3.1523.
Fauzan, M. F., Purnamasari, A. I., & Dwilestari, G. (2023). Penerapan Data Mining Untuk Menganalisis Penjualan Air Minum Dalam Kemasan Selama Masa Pandemi Covid-19. JATI (Jurnal Mhs. Tek. Inform., 7(1), 700–706. https://doi.org/10.36040/jati.v7i1.6290.
Ningsih, S. R., Damanik, I. S., Windarto, A. P., Tambunan, H. S., Jalaluddin, J., & Wanto, A. (2019). Analisis K-Medoids Dalam Pengelompokkan Penduduk Buta Huruf Menurut Provinsi. Pros. Semin. Nas. Ris. Inf. Sci., 1(September), 721. https://doi.org/10.30645/senaris.v1i0.78.
Meizar, A., Fahrozi, W., Indra, E., & Saputra, M. (2022). Analisis Trend Moment Pada Datamining Forecasting Dalam Memprediksi Jumlah Persediaan Obat Herbal. J. Sist. Inf. dan Ilmu Komput. Prima(JUSIKOM PRIMA), 5(2), 103–106. https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v5i2.2452.
Siregar, A. R., Andani, S. R., & Saputra, W. (2020). Clustering Pada Pelanggan Perusahaan Air Bersih Dengan Algoritma K-Means. BRAHMANA J. Penerapan Kecerdasan Buatan, 1(1), 7–14. https://doi.org/10.30645/brahmana.v1i1.2.
Siska, Y. (2019). Penerapan Data Mining Dengan Algoritma Naive Bayes Pelanggan Terhadap Pelayanan Servis Mobil ( Studi Kasus : Katamso Service ). Maj. Ilm. INTI, 6, 354.
Andini, A. D., & Arifin, T. (2020). Implementasi Algoritma K-Medoids Untuk Klasterisasi Data Penyakit Pasien Di Rsud Kota Bandung. J. Responsif Ris. Sains dan Inform., 2(2), 128–138. https://doi.org/10.51977/jti.v2i2.247.
Herdini, F., & Widiyarta, A. (2020). Responsivitas Pelayanan Publik Dalam Menangani Keluhan Pelanggan Di Perusahaan Daerah Air Minum (Pdam) Kabupaten Nganjuk. 10(1), 54–75.
Kairupan, S. B., Mokat, J. E., & Pakasi, K. M. (2020). Pelayanan Perusahaan Daerah Air Minum dalam Penyediaan Air Bersih di Kecamatan Pasan Kabupaten Minahasa Tenggara. J. Kaji. Kebijak. dan Ilmu Adm. Negara (JURNAL Adm., 1(2), 41–44. https://doi.org/10.36412/jan.v1i2.1636.
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