PENERAPAN DATA MINING PADA JUMLAH PELANGGAN PERUSAHAAN AIR BERSIH MENURUT PROVINSI MENGGUNAKAN METODE K-MEANS CLUSTERING

Authors

  • Lestari Sinaga STIKOM Tunas Bangsa Pematangsiantar
  • Abdullah Ahmad STIKOM Tunas Bangsa Pematangsiantar
  • Muhammad Safii STIKOM Tunas Bangsa Pematangsiantar

DOI:

https://doi.org/10.31598/jurnalresistor.v2i2.418

Keywords:

Maining Data, K-Means, Customers, Clean Water

Abstract

Water is one of the primary needs for humans so that everyone has the right to get clean water for their daily needs. Along with increasing population, the need for water will increase. So with that the PDAM must sell clean / decent water to its customers, clean water becomes the focus of attention and has the greatest power compared to other problems. Because water is a basic necessity, most of the companies impose rates that can be reached by the community and prices are adjusted to the growth in demand. The purpose of this research is to get a grouping of the number of customers of clean water companies in all provinces using the K-Means Algorithm, K-Means is a method for grouping data into a cluster by calculating the closest distance from a data to a centroid point. Clusters used are high level clusters (C1), medium level clusters (C2), and for low level clusters (C3). Centroid data obtained is for high-level clusters (C1) which are as many as 7710154, for medium-level clusters as much as 929586, and for low-level clusters as much as 112462. Based on the calculated data obtained high-level results, namely the province of Indonesia, for the medium level namely province North Sumatra, DKI Jakarta, West Java, Central Java and East Java, and other provinces are low levels. So that this result can be a support for the company in order to increase water needs.

Downloads

Download data is not yet available.

References

D. T. RAHAYU, SRI NUGRAHADI and F. INDRIANI, “Clustering Penentuan Potensi Kejahatan Daerah Di Kota Banjarbaru Dengan Metode K-Means,†vol. 01, no. 01, pp. 33–45, 2014.

A. BASTIAN, H. SUJADI, and G. FENRIANTO, “Penerapan Algoritma K-Means Clustering Analysis Pada Penyakit Menular Manusia (Studi Kasus Kabupaten Majalengka), Program Studi Teknik Informatika, Universitas Majalengka,†no. 1, pp. 26–32.

C. Layadi, M. Fajar, and I. A. Musdar, “Analisis Data Pada Jaringan Sensor Nirkabel Menggunakan,†vol. 1, no. 1, pp. 8–15, 2018.

A. K. WARDHANI, “K-Means Algoritma Untuk Pengelompokan Penyakit Pasien Pada Puskesmas Kajen Pekalongan, Magister Sistem Informasi Universitas Diponegoro,†vol. 14, pp. 30–37, 2016.

G. ABDILLAH, F. A. PUTRA, F. RENALDI, and P. S. Informatika, “Penerapan Data Mining Pemakaian Air Pelanggan Untuk Menetukan Klasifikasi Potensi Pemakaian Air Pelanggan Baru Di PDAM Tirta Raharja Menggunakan Algoritma K-Means, Universitas Jenderal Achmad Yani, Jawa Barat,†vol. 2016, no. Sentika, pp. 18–19, 2016.

F. TASLIM, “Penerapan algorithma k-mean untuk clustering data obat pada puskesmas rumbai,Program Studi Teknik Informatika, Universitas Lancang Kuning Pekanbaru,†vol. x, no. x, pp. 108–114, 2016.

M. G. SADEWO, A. P. WINDARTO, and D. HARTAMA, “Penerapan Datamining Pada Populasi Daging Ayam Ras Pedaging Di Indonesia Berdasarkan Provinsi Menggunakan K-Means Clustering,†pp. 60–67, 2016.

L. MAULIDA, “Penerapan Data Mining Dalam Mengelompokkan Kunjungan Wisatawan Ke Objek Wisata Unggulan Di Prov.DKI Jakarta Dengan K-Means, Program Studi Manajemen Informatika, AMIK BSI Tangerang,†vol. 2, no. 3, pp. 167–174, 2018.

A. P. WINDARTO, “Penerapan Data Mining Pada Ekspor Buah-Buahan Menurut Negara Tujuan Menggunakan K-Means Clustering, Program Studi Sistem Informasi, Data Mining,†vol. 16, no. 4, pp. 348–357, 2017.

E. RIANTI, “Data Mining Dalam Menentukan Kacamata Pada Optik Zal Laris Dan Tidak Laris Menggunakan Metode Clustering, Universitas Indonesia,†vol. 4, no. 2, pp. 267–283, 2017.

Downloads

Published

2019-10-28

How to Cite

Sinaga, L., Ahmad, A., & Safii, M. (2019). PENERAPAN DATA MINING PADA JUMLAH PELANGGAN PERUSAHAAN AIR BERSIH MENURUT PROVINSI MENGGUNAKAN METODE K-MEANS CLUSTERING. Jurnal RESISTOR (Rekayasa Sistem Komputer), 2(2), 119-125. https://doi.org/10.31598/jurnalresistor.v2i2.418

Issue

Vol. 2 No. 2 (2019): Jurnal RESISTOR Edisi Oktober 2019

Section

Articles

License

Copyright in each article belongs to the author.

  1. The authors admit that RESISTOR Journal as a publisher who published the first time under   Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
  2. Authors can include writing separately, regulate distribution of non-ekskulif of manuscripts that have been published in this journal into another version (eg sent to respository institution author, publication into a book, etc.), by recognizing that the manuscripts have been published for the first time in RESISTOR Journal