PEMANFAATAN DATAMINING PADA PENGELOMPOKAN PROVINSI TERHADAP PENCEMARAN LINGKUNGAN HIDUP
DOI:
https://doi.org/10.30865/komik.v3i1.1675Abstract
This research aims to provide input for the government so that it can immediately tackle water pollution given the many adverse effects that lurk in various aspects of life. The method used in this study researchers used the method of K-means clustering datamining algorithm. The data used in this study are the number of villages according to the type of environmental pollution in 2018 which consists of 34 provinces in Indonesia obtained through the official website of the Directorate of Statistics Indonesia. The variable used is water pollution. The variable used is water pollution. Data is grouped into 2 clusters, namely provinces that have high levels of water pollution (C1) and provinces that have low levels of water pollution (C2). K-Means Clustering algorithm in this study produces 2 iterations, so the final result is: high water pollution (C1) in the provinces of North Sumatra, West Java, Central Java, East Java, for low level water pollution (C2) is in provinces of Aceh, West Sumatra, Riau, Jambi, South Sumatra, Bengkulu, Lampung, Kep.Bangka Belitung, Kep.Riau, DKI Jakarta, DI Yogyakarta, Banten, Bali, West Nusa Tenggara, East Nusa Tenggara, West Kalimantan, Central Kalimantan, South Kalimantan, East Kalimantan, North Kalimantan, North Sulawesi, Central Sulawesi, South Sulawesi, Southeast Sulawesi, Gorontalo, West Sulawesi, Maluku, North Maluku, West Papua, Papua.
Keywords:Datamining, Clustering, K-means , Water pollution
References
S. M. Dewi and A. P. Windarto, “Analisis Metode Electre Pada Pemilihan Usaha Kecil Home Industry Yang Tepat Bagi Mahasiswa,†Sist. J. Sist. Inf., vol. 8, no. 3, pp. 377–385, 2019.
D. R. S. P, A. A. Muin, and M. Amin, “PEMILIHAN FACIAL WASH UNTUK KULIT WAJAH BERMINYAK DENGAN METODE PROMETHEE II,†CESS (Journal Comput. Eng. Syst. Sci., vol. 4, no. 2, pp. 222–229, 2019.
C. Astria, A. P. Windarto, and Z. Musiafa, “PEMILIHAN PRODUK SAMPO SESUAI JENIS KULIT KEPALA DENGAN METODE PROMETHEE II,†CESS (Journal Comput. Eng. Syst. Sci., vol. 4, no. 2, pp. 178–185, 2019.
D. N. Batubara, A. P. Windarto, and M. R. Raharjo, “PENERAPAN PROMETHEE II PADA PEMILIHAN PRODUK CONDITIONER SEBAGAI UPAYA PENINGKATAN MINAT BELI KONSUMEN,†CESS (Journal Comput. Eng. Syst. Sci., vol. 4, no. 2, pp. 191–197, 2019.
D. N. Batubara, D. R. S. P, and A. P. Windarto, “Penerapan Metode PROMETHEE II Pada Pemilihan Situs Travel Berdasarkan Konsumen,†J. SISFOKOM, vol. 8, no. 1, pp. 46–52, 2019.
A. P. Windarto, “Penerapan Datamining Pada Ekspor Buah-Buahan Menurut Negara Tujuan Menggunakan K-Means Clustering Method,†Techno.Com, vol. 16, no. 4, pp. 348–357, 2017.
M. G. Sadewo et al., “PENERAPAN ALGORITMA CLUSTERING DALAM MENGELOMPOKKAN BANYAKNYA DESA / KELURAHAN MENURUT UPAYA ANTISIPASI / MITIGASI BENCANA ALAM MENURUT PROVINSI DENGAN K-MEANS,†vol. 2, pp. 311–319, 2018.
H. Siahaan, H. Mawengkang, S. Efendi, A. Wanto, and A. P. Windarto, “Application of Classification Method C4 . 5 on Selection of Exemplary Teachers,†in IOP Conference Series, 2018, pp. 1–6.
Sudirman, A. P. Windarto, and A. Wanto, “Data mining tools | rapidminer: K-means method on clustering of rice crops by province as efforts to stabilize food crops in Indonesia,†IOP Conf. Ser. Mater. Sci. Eng., vol. 420, p. 12089, 2018.
S. Azhar, H. Latipa, S. Leni, and N. Zulita, “Sistem Pakar Penyakit Ginjal Pada Manusia Menggunakan Metode Forward Chaining,†J. Media Infotama, vol. 10, no. 1, pp. 16–26, 2014.
T. Budiharjo, Soemartono, T., Windarto, A.P., Herawan, “Predicting tuition fee payment problem using backpropagation neural network model,†Int. J. Adv. Sci. Technol., 2018.
T. Budiharjo, Soemartono, T., Windarto, A.P., Herawan, “Predicting school participation in indonesia using back-propagation algorithm model,†Int. J. Control Autom., 2018.
A. P. Windarto, M. R. Lubis, and Solikhun, “MODEL ARSITEKTUR NEURAL NETWORK DENGAN BACKPROPOGATION PADA PREDIKSI TOTAL LABA RUGI KOMPREHENSIF BANK UMUM KONVENSIONAL,†Kumpul. J. Ilmu Komput., vol. 5, no. 2, pp. 147–158, 2018.
A. P. Windarto, M. R. Lubis, and Solikhun, “IMPLEMENTASI JST PADA PREDIKSI TOTAL LABA RUGI KOMPREHENSIF BANK UMUM KONVENSIONAL DENGAN BACKPROPAGATION,†J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 4, pp. 411–418, 2018.
Sumijan, A. P. Windarto, A. Muhammad, and Budiharjo, “Implementation of Neural Networks in Predicting the Understanding Level of Students Subject,†Int. J. Softw. Eng. Its Appl., vol. 10, no. 10, pp. 189–204, 2016.
B. M. Metisen and H. L. Sari, “Analisis Clustering Menggunakan Metode K-Means dalam Pengelompokkan Penjualan Produk pada Swalayan Fadhila,†J. Media Infotama, vol. 11, no. 2, pp. 110–118, 2015.
M. G. Sadewo, A. P. Windarto, and S. R. Andani, “Pemanfaatan Algoritma Clushtering Dalam Mengelompokkan Jumlah Desa / Kelurahan Yang Memiliki Sarana Kesehatan,†vol. I, pp. 124–131, 2017.
J. Nasional and S. Informasi, “PENERAPAN DATA MINING DALAM PENGELOMPOKAN PENDERITA THALASSAEMIA,†vol. 2, pp. 299–305, 2017.
M. Anjelita, A. P. Windarto, and A. Wanto, “Analisis Metode K-Means pada Kasus Ekspor Barang Perhiasan dan Barang Berharga Berdasarkan Negara Tujuan,†pp. 476–482, 2019.
C. Hijau, “PENERAPAN DATA MINING DENGAN METODE CLUSTERING,†vol. 1, no. Senatkom, 2015.
S. Defiyanti, M. Jajuli, T. Informatika, F. Ilmu, K. Universitas, and S. Karawang, “IMPLEMENTASI ALGORITMA K-MEANS DALAM,†vol. I, no. 2, pp. 62–68, 2015.
