Implementasi Data Mining Dalam Mengelompokkan Jumlah Produktivitas Ubi Kayu Menurut Provinsi Menggunakan Algoritma K-Means

 (*)Sri Wulandari Mail (STIKOM Tunas Bangsa, Pematangsiantar, Indonesia)
 Irfan Sudahri Damanik (STIKOM Tunas Bangsa, Pematangsiantar, Indonesia)
 Eka Irawan (STIKOM Tunas Bangsa, Pematangsiantar, Indonesia)
 Heru Satria Tambunan (STIKOM Tunas Bangsa, Pematangsiantar, Indonesia)
 Irawan Irawan (STIKOM Tunas Bangsa, Pematangsiantar, Indonesia)

(*) Corresponding Author

DOI: http://dx.doi.org/10.30865/komik.v4i1.2727

Abstract

AbstractCassava is one of the main foodstuffs, not only in Indonesia but also in the world. In Indonesia, cassava is the third staple food after rice and corn. The spread of cassava plants extends to all provinces in Indonesia. Using data mining is one of the ideas of information to classify the amount of cassava productivity by province, by using the k-means clustering method the amount of cassava productivity will be collected based on the year (2011-2018) of 30 provinces. K-means is a method with unsupervised classification type where the data is grouped into one or more clusters. k-means modeling the dataset into clusters where one cluster has the same characteristics and has different characteristics from other clusters. This study aims to classify the amount of cassava productivity by province. Where the highest cluster results are obtained with a total of  2 provinces, medium cluster with 4  provinces, and a low cluster with 24 provinces.

Keywords: Data Mining, K-means, Clustering, Cassava, RapidMiner Studio

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References

A. K. Wardhani, “( K-Means Algorithm Implementation For Clustering Of Patients Disease In Kajen Clinic Of Pekalongan ) Anindya Khrisna Wardhani Magister Sistem Informasi Universitas Diponegoro,” Vol. 14, Pp. 30–37, 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.

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.

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.

T. Wibowo, “Penerapan Data Mining Pemilihan Siswa Kelas Unggulan Dengan Metode K-Means Clustering Di Smp N 02 Tasikmadu,” 2018.

Y. R. Nasution Et Al., “Penerapan Algoritma K-Means Clustering Pada Aplikasi Menentukan Berat Badan Ideal,” Vol. 6341, No. April, Pp. 77–81, 2018.

R. W. Sari And D. Hartama, “Data Mining : Algoritma K-Means Pada Pengelompokkan Wisata Asing Ke Indonesia Menurut Provinsi,” Semin. Nas. Sains Teknol. Inf., Pp. 322–326, 2018.

Mardalius, “Pengelompokan Data Penjualan Aksesoris Menggunakan Algoritma K-Means,” Vol. Iv, No. 2, Pp. 401–411, 2018.

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