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(Software Engineering, STMIK Pelita Nusantara, Medan, Indonesia)(2) Bosker Sinaga
(Software Engineering, STMIK Pelita Nusantara, Medan)(3) Jonson Manurung
(Software Engineering, STMIK Pelita Nusantara, Medan, Indonesia)(4) Safa Ayoub Al Hashim
(College of Information Technology, University of Babylon, Iraq)*corresponding author
AbstractIndonesia is the third-largest country in the world with rice production reaching 83,037,000 and became the highest production in southeast Asia spread in several provinces in Indonesia The problem found that such product has not been able to cover the needs of Indonesian people with a very high population so that in the research conducted information excavation to generate potential to the pile of data that has been described and analyzed by BPS with clustering topics. Clustering will help related parties, especially the ministry of agriculture, in determining land development priorities and can minimize the shortage of rice production nationally. Grouping process by involving the K-means algorithm to group rice production with a combination of the elbow method as part of determining the number of clusters that will be recommended with attributes supporting the area of harvest, productivity, and production. Method of researching with data cleaning activities, data integration, data transformation, and application of K-means with a combination of elbow and pattern evaluation. The results achieved based on the work description with a combination of K-Means and elbow provide cluster recommendations that are the best choice or the most optimal is iteration 2 which is the lowest rice production group with a total of 22 provinces, rice production with a medium category of 9 and production with the highest category with 3 regions KeywordsCluster K-Means Elbow production rice
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DOIhttps://doi.org/10.29099/ijair.v6i1.232 |
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References
S. Suharyanto, J. Rinaldy, and N. Ngurah Arya, “Analisis Risiko Produksi Usahatani Padi Sawah,†Agrar. J. Agribus. Rural Dev. Res., 2015 https://doi.org/10.18196/agr.1210.
M. Maulana, “Peranan Luas Lahan, Intensitas Pertanaman dan Produktivitas sebagai Sumber Pertumbuhan Padi Sawah di Indonesia 1980–2001,†J. Agro Ekon., 2016 https://doi.org/10.21082/jae.v22n1.2004.74-95.
BPS and BPPT, Luas Panen dan Produksi Padi di Indonesia 2019. 2020.
I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques. 2016 https://doi.org/10.1016/B978-0-12-804291-5.00010-6.
W. Hardi, W. A. Kusuma, and S. Basuki, “Clustering topic groups of documents using K-Means algorithm: Australian Embassy Jakarta media releases 2006-2016,†Berk. Ilmu Purpose. dan Inf., vol. 15, no. 2, p. 226, 2019 https://doi.org/10.22146/bip.36451.
L. Morissette and S. Chartier, “The k-means clustering technique: General considerations and implementation in Mathematica,†Tutor. Quant. Methods Psychol., vol. 9, no. 1, pp. 15–24, 2013 https://doi.org/10.20982/tqmp.09.1.p015.
A. Dharmarajan and T. Velmurugan, “Performance analysis on K-means and fuzzy C-means clustering algorithms using CT-DICOM images of lung cancer,†J. Adv. Res. Dyn. Control Syst., 2019 https://doi.org/10.5373/JARDCS/V11/20192597.
R. Annisa, D. Rosiyadi, and D. Riana, “Improved point center algorithm for k-means clustering to increase software defect prediction,†Int. J. Adv. Intell. Informatics, vol. 6, no. 3, pp. 328–339, 2020 https://doi.org/10.26555/ijain.v6i3.484.
D. P. Ismi, S. Panchoo, and Murinto, “K-means clustering based filter feature selection on high dimensional data,†Int. J. Adv. Intell. Informatics, 2016 https://doi.org/10.26555/ijain.v2i1.54.
S. S. Subbulaxmi and G. Arumugam, “K-Means Cluster-Based Oversampling Algorithm for Imbalanced Data Classification,†no. 5, pp. 3436–3440, 2020 https://doi.org/10.35940/ijrte.E6535.018520.
A. P. Windarto, “Implementation of Data Mining on Rice Imports by Major Country of Origin Using Algorithm Using K-Means Clustering Method,†Int. J. Artif. Intell. Res., vol. 1, no. 2, p. 26, 2017 https://doi.org/10.29099/ijair.v1i2.17.
P. M. Hasugian, H. D. Hutahaean, B. Sinaga, Sriadhi, and S. Silaban, “Villages Status Classification Analysis Involving K-Means Algorithm to Support Kementerian Desa Pembangunan Daerah Tertinggal dan Transmigrasi Work Programs,†J. Phys. Conf. Ser., vol. 1641, no. 1, 2020 https://doi.org/10.1088/1742-6596/1641/1/012058.
J. Karimov and M. Ozbayoglu, “Clustering Quality Improvement of k-means Using a Hybrid Evolutionary Model,†in Procedia Computer Science, 2015 https://doi.org/10.1016/j.procs.2015.09.143.
S. Zahra, M. A. Ghazanfar, A. Khalid, M. A. Azam, U. Naeem, and A. Prugel-Bennett, “Novel centroid selection approaches for KMeansclustering-based recommender systems,†Inf. Sci. (Ny)., 2015 https://doi.org/10.1016/j.ins.2015.03.062.
M. Mohammed, M. B. Khan, and E. B. M. Bashier, “k-Means Clustering,†in Machine Learning, 2020.
A. K. Jain, “Data clustering: 50 years beyond K-means,†Pattern Recognit. Lett., 2010 https://doi.org/10.1016/j.patrec.2009.09.011.
N. Putu, E. Merliana, and A. J. Santoso, “Analisa Penentuan Jumlah Cluster Terbaik pada Metode K-Means,†pp. 978–979.
M. A. Syakur, B. K. Khotimah, E. M. S. Rochman, and B. D. Satoto, “Integration K-Means Clustering Method and Elbow Method for Identification of the Best Customer Profile Cluster,†in IOP Conference Series: Materials Science and Engineering, 2018 https://doi.org/10.1088/1757-899X/336/1/012017.
B. Purnima and K. Arvind, “EBK-Means: A Clustering Technique based on Elbow Method and K-Means in WSN,†Int. J. Comput. Appl., 2014.
C. Yuan and H. Yang, “Research on K-Value Selection Method of K-Means Clustering Algorithm,†J, 2019 https://doi.org/10.3390/j2020016.
T. M. Kodinariya and P. R. Makwana, “Review on determining the number of Cluster in K-Means Clustering,†Int. J. Adv. Res. Comput. Sci. Manag. Stud., 2013.
U. R. Raval and C. Jani, “Implementing & Improvisation of K-means Clustering Algorithm,†Int. J. Comput. Sci. Mob. Comput., 2016.
D. A. I. C. Dewi and D. A. K. Pramita, “Analisis Perbandingan Metode Elbow dan Silhouette pada Algoritma Clustering K-Medoids dalam Pengelompokan Produksi Kerajinan Bali,†Matrix J. Manaj. Teknol. dan Inform., 2019.
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