Teknik Data Mining dalam Mengelompokkan Produktivitas Padi Menurut Provinsi Menggunakan K-Medoids

Safitri Ani Ritonga, M. Safii, Iin Parlina, Heru Satria Tambunan, Susiani Susiani

Abstract


Rice is a staple food raw material that is vital for the people, and one of the leading commodities that is cultivated by most farmers, making it an indicator of the Indonesian economy. Where the price of rice is a reflection of a country's ability to manage its economy. Rice productivity is decreasing because there are many obstacles faced by farmers such as superior seeds, fertilizers, pest eradication drugs, plant diseases, and labor in the agricultural sector. This study proposes the use of the K-Medoids method to determine the high and low productivity of rice in the province. The research results obtained were Cluster 1 (low) = 17 provinces, Cluster 2 (moderate) = 7 Provinces, and Cluster 3 (High) = 10 Provinces.

Full Text:

PDF

References


A. K. Wardhani, “Implementasi Algoritma K-Means untuk Pengelompokkan Penyakit Pasien pada Puskesmas Kajen Pekalongan,” Jurnal Transformatika, vol. 14, no. 1, pp. 1–8, 2016.

I. Parlina, A. P. Windarto, A. Wanto, and M. R. Lubis, “Memanfaatkan Algoritma K-Means dalam Menentukan Pegawai yang Layak Mengikuti Asessment Center untuk Clustering Program SDP,” CESS (Journal of Computer Engineering System and Science), vol. 3, no. 1, pp. 87–93, 2018.

S. 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 Conference Series: Materials Science and Engineering, vol. 420, no. 12089, pp. 1–8, 2018.

R. W. Sari, A. Wanto, and A. P. Windarto, “Implementasi Rapidminer dengan Metode K-Means (Study Kasus : Imunisasi Campak pada Balita Berdasarkan Provinsi),” KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer), vol. 2, no. 1, pp. 224–230, 2018.

M. G. Sadewo, A. P. Windarto, and A. Wanto, “Penerapan Algoritma Clustering dalam Mengelompokkan Banyaknya Desa/Kelurahan Menurut Upaya Antisipasi/ Mitigasi Bencana Alam Menurut Provinsi dengan K-Means,” KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer), vol. 2, no. 1, pp. 311–319, 2018.

D. A. S. Simamora, M. T. Furqon, and B. Priyambadha, “Clustering Data Kejadian Tsunami Yang Disebabkan Oleh Gempa Bumi Dengan Menggunakan Algoritma K-Medoids,” vol. 1, no. 8, pp. 635–640, 2017.

W. A. Triyanto, “ALGORITMA K-MEDOIDS UNTUK PENENTUAN STRATEGI PEMASARAN,” vol. 6, no. 1, pp. 183–188, 2015.

M. T. Furqon, A. Ridok, and W. F. Mahmudy, “Paralelisasi Algoritma K-MedoidS Pada General Purpose menggunakan Open Computing Language,” Konferensi Nasional Sistem Informasi 2015, pp. 1–7, 2015.

Defidelwina, A. Ariyantodan, and Y. Aini, “Strategi peningkatan produksi dan produktivitas padi sawah di kabupaten rokan hulu,” Prosiding Seminar Nasional dan call for papers, vol. VII, pp. 1266–1275, 2017.

Vandana Aditya Putra Sangga, “No Title,” 2015.

D. F. Pramesti, M. T. Furqon, and C. Dewi, “Implementasi Metode K-Medoids Clustering Untuk Pengelompokan Data Potensi Kebakaran Hutan / Lahan Berdasarkan Persebaran Titik Panas ( Hotspot ),” vol. 1, no. 9, pp. 723–732, 2017.

H. Zayuka, S. M. Nasution, and Y. Purwanto, “Perancangan Dan Analisis Clustering Data Menggunakan Metode K-Medoids Untuk Berita Berbahasa Inggris,” e-Proceeding of Engineering : Vol.4, No.2 Agustus 2017, vol. 4, no. 2, pp. 1–9, 2017




DOI: http://dx.doi.org/10.30645/senaris.v1i0.71

Refbacks

  • There are currently no refbacks.


&nbsp