Penerapan Metode K-Means Dalam Mengelompokkan Banyaknya Desa/Kelurahan Menurut Jenis Pencemaran Lingkungan Hidup Berdasarkan Provinsi

Agus Tiranda Sipayung(1*), S Saifullah(2), Riki Winanjaya(3),

(1) STIKOM Tunas Bangsa, Pematangsiantar, Sumatera Utara, Indonesia
(2) STIKOM Tunas Bangsa, Pematangsiantar, Sumatera Utara, Indonesia
(3) STIKOM Tunas Bangsa, Pematangsiantar, Sumatera Utara, Indonesia
(*) Corresponding Author

Abstract


Environmental pollution is hazardous for every living thing; environmental pollution can cause an imbalance in the environment or existing ecosystem. This study discusses the grouping of villages according to the type of environmental pollution based on the provinces in Indonesia. The method used is DataMining with the K-means Clustering algorithm. By using this method, the data obtained can be grouped into 2 clusters. This study uses secondary data, namely data obtained through intermediary media recorded on the Central Bureau of Statistics website with the URL address: http://www.bps.go.id. The results obtained in this study are grouping environmental pollution into 2 clusters, namely the highest cluster and the lowest cluster. In this research, it is hoped that it can provide input to related government parties to pay more attention to the provinces included in the highest cluster to tackle environmental pollution in the province.

Full Text:

PDF

References


M. S. Muslimah, S.Si, “Dampak Pencemaran Tanah Dan Langkah Pencegahan,” J. Penelit. Agrisamudra, Vol. 2, No. 1, Pp. 11–20, 2017, Doi: 10.33059/Jpas.V2i1.224.

Y. Darmi And A. Setiawan, “Penerapan Metode Clustering K-means Dalam,” J. Media Infotama, Vol. 12, No. 2, Pp. 148–157, 2016.

N. Puspitasari And Haviluddin, “Penerapan Metode K-means Dalam Pengelompokkan Curah Hujan,” Semin. Nas. Ris. Ilmu Komput. (Snrik ), Vol. 1, No. March 2017, 2016.

J. Eska, “Penerapan Datamining Untuk Prekdiksi Penjualan Wallpaper Menggunakan Algoritma C4.5 Stmik Royal Ksiaran,” Jurteksi (Jurnal Teknol. Dan Sist. Informasi), Vol. 2, Pp. 9–13, 2016.

A. P. Windarto, “Implementation Of Datamining On Rice Imports By Major Country Of Origin Using Algorithm Using K-means Clustering Method,” Int. J. Artif. Intell. Res., Vol. 1, No. 2, Pp. 26–33, 2017.

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,” Infotekjar (Jurnal Nas. Inform. Dan Teknol. Jaringan), Vol. 2, No. 1, Pp. 60–67, 2017.

T. Imandasari, E. Irawan, A. P. Windarto, And A. Wanto, “Algoritma Naive Bayes Dalam Klasifikasi Lokasi Pembangunan Sumber Air,” Semin. Nas. Ris. Inf. Sci., 2019, Doi: 10.30645/Senaris.V1i0.81.

P. Sari, B. Pramono, L. Ode, And H. S. Sagala, “K-means Terhadap Status Nilai Gizi Pada Balita *1,2,3,” Vol. 3, No. 1, Pp. 143–148, 2017.

R. A. Asroni, “Penerapan Metode K-means Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika Umm Magelang,” Ilm. Semesta Tek., Vol. 18, No. 1, Pp. 76–82, 2015, Doi: 10.1038/Hdy.2009.180.

G. Abdillah Et Al., “Penerapan Datamining Pemakaian Air Pelanggan Untuk Menentukan Klasifikasi Potensi Pemakaian Air Pelanggan Baru Di Pdam Tirta Raharja Menggunakan Algoritma K-means,” Vol. 2016, No. Sentika, Pp. 18–19, 2016.

B. R. C.T.I. Et Al., “Implemetasi K-means Clustering Pada Rapidminer Untuk Analisis Daerah Rawan Kecelakaan,” Semin. Nas. Ris. Kuantitatif Terap. 2017, No. April, Pp. 58–60, 2017.




DOI: https://doi.org/10.30645/kesatria.v1i4.35

DOI (PDF): https://doi.org/10.30645/kesatria.v1i4.35.g35

Refbacks

  • There are currently no refbacks.


Published Papers Indexed/Abstracted By: