Penerapan Metode Clustering Dengan K-Means Untuk Memetakan Potensi Tanaman Padi di Sumatera


  • Irma Sanela * Mail Universitas Islam Negeri Sultan Syarif Kasim, Pekanbaru, Indonesia
  • Alwis Nazir Universitas Islam Negeri Sultan Syarif Kasim, Pekanbaru, Indonesia
  • Fadhilah Syafria Universitas Islam Negeri Sultan Syarif Kasim, Pekanbaru, Indonesia
  • Elin Haerani Universitas Islam Negeri Sultan Syarif Kasim, Pekanbaru, Indonesia
  • Lola Oktavia Universitas Islam Negeri Sultan Syarif Kasim, Pekanbaru, Indonesia
  • (*) Corresponding Author
Keywords: Clustering; Data Mining; K-Means; Python; Rice Crops

Abstract

Rice plants are the primary source of rice, the staple food for the majority of the Indonesian population. Despite the presence of other food alternatives, rice remains irreplaceable for those accustomed to consuming rice. According to data from the Food and Agriculture Organization of the United Nations (FAO) in 2018, Indonesia is the third-largest rice producer in the world, with a total production of 59.2 million tons. However, urban and agricultural spatial planning is not yet fully integrated, resulting in often conflicting decisions in land use planning for agriculture and urban development. To meet the rice demand in Sumatra, efforts are needed to increase rice production in each province. Therefore, this research aims to map the potential for rice cultivation in Sumatra based on production and harvest results from 1993 to 2020. The method used in this study is K-Means, which allows the grouping of rice potential areas into three categories: high, medium, and low. The research results produced three clusters, evaluated using the Davies Bouldin Index (DBI) with a value of 0.3943. The clustering results indicate that Cluster 0 contains 92 areas with a high success rate, Cluster 2 comprises 84 areas with a medium success rate, and Cluster 1 consists of 48 areas with a low success rate. The category of low success rate is found in Cluster 1 with 48 areas. Cluster 0 includes Aceh, North Sumatra, West Sumatra, South Sumatra, and Lampung within certain time periods. Cluster 1 encompasses other areas with different characteristics. Cluster 2 includes the provinces of Riau, Jambi, and Bengkulu.

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References

Edy, Pengantar Teknologi Budidaya Tanaman Serelia : Jagung dan Padi. PT Nas Media Pustaka, 2022.

F. Marisa et al., “Digitasi Produktivitas Panen Padi Berbasis K-Means Clustering,” SMARTICS Journal, vol. 7, no. 1, pp. 21–26, 2021, doi: 10.21067/smartics.v7i1.5270.

B. R. Aprildahani, C. T. H. Permana, and S. T. E. W. Hutama, “Kebutuhan Lahan Pertanian Minimum untuk Kesejahteraan Petani di Pulau Sumatera,” Journal of Science and Applicative Technology, vol. 5, no. 1, pp. 116–125, Mar. 2021, doi: 10.35472/jsat.v5i1.409.

Badan Pusat Statistik, “Luas Panen, Produksi, dan Produktivitas Padi Menurut Provinsi 2020,” 12 Juli 2021.

Z. Nabila, A. Rahman Isnain, and Z. Abidin, “Analisis Data Mining Untuk Clustering Kasus Covid-19 Di Provinsi Lampung Denga Algoritma K-Means,” Jurnal Teknologi dan Sistem Informasi (JTSI), vol. 2, no. 2, pp. 100–108, Jun. 2021, [Online]. Available: http://jim.teknokrat.ac.id/index.php/JTSI

T. Hartati and Y. Arie Wijaya, “Analisis Data Lalu Lintas Jaringan Di Kantor Cangehgar Cyber Operation Center Menggunakan Algritma K-Means Network Traffic Data Analysis At Cangehgar Cyber Operation Center Office Using K-Means Algorithm,” Jurnal Ilmiah NERO, vol. 7, no. 1, pp. 75–84, 2022.

A. Asroni, H. Fitri, and E. Prasetyo, “Penerapan Metode Clustering dengan Algoritma K-Means Pada Pengelompokkan Data Calon Mahasiswa Baru di Universitas Muhammadiyah Yogyakarta (Studi Kasus: Fakultas Kedokteran dan Ilmu Kesehatan, dan Fakultas Ilmu Sosial dan Ilmu Politik),” Semesta Teknika, vol. 21, no. 1, pp. 60–64, 2018, doi: 10.18196/st.211211.

F. Indriyani and Infriani E, “Clustering Data Penjualan pada Toko Perlengkapan Outdoor Menggunakan Metode K-Means (Clustering Sales Data At Outdoor Equipment Stores Using K-Means Method),” Jurnal Informatika (JUITA), vol. 7, no. 2, pp. 109–113, Nov. 2019.

R. Pradena Harjono, A. Magdalena, and I. Pakereng, “Penerapan Metode K-Means Clustering Untuk Analisis Potensi Lahan Pangan Pada Provinsi Kalimantan Selatan,” Jurnal Sains Komputer & Informatika (J-SAKTI, vol. 7, no. 1, pp. 332–338, Mar. 2023.

M. A. Sembiring et al., “Penerapan Metode Algoritma K-Means Clustering Untuk Pemetaan Penyebaran Penyakit Demam Berdarah Dengue (DBD),” Journal of Science and Social Research, no. 3, pp. 336–341, 2021, [Online]. Available: http://jurnal.goretanpena.com/index.php/JSSR

A. Setiadi and E. Delima Sikumbang, “K-Means Clustering Dalam Penerimaan Karyawan Baru,” Informatics For Educators And Professionals, vol. 4, no. 2, pp. 103–112, Jun. 2020.

E. Prasetyaningrum and P. Susanti, “Perbandingan Algoritma K-Means Dan K-Medoids Untuk Pemetaan Hasil Produksi Buah-Buahan,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 7, pp. 1775–1783, 2023, doi: 10.30865/mib.v7i4.6477.

H. Mahulae, “Pengelompokan Potensi Produksi Buah-Buahan Di Provinsi Sumatera Utara dengan Menerapkan K-Clustering (Studi Kasus : Dinas Tanaman Pangan dan Holtikultura),” JURIKOM (Jurnal Riset Komputer), vol. 7, no. 2, pp. 312–325, Apr. 2020, doi: 10.30865/jurikom.v7i2.2122.

D. Fadhillah, A. Faisol, and N. Vendyansyah, “Penerapan Metode K-Means Clustering Pada Pemetaan Lahan Kopi Di Kabupaten Malang,” Jurnal Mahasiswa Teknik Informatika), vol. 6, no. 1, pp. 162–170, Feb. 2022.

S. Wijayanto, M. Yoka Fathoni, J. DI Panjaitan No, K. Purwokerto Selatan, K. Banyumas, and J. Tengah, “Pengelompokkan Produktivitas Tanaman Padi di Jawa Tengah Menggunakan Metode Clustering K-Means,” Jurnal JUPITER, vol. 13, no. 2, pp. 212–219, Oct. 2021.

Lidya, R. Buaton, and Nurhayati, “Clustering Hasil Panen Berdasarkan Lokasi dan Jenis Bibit (Studi Kasus: Dinas Pangan Dan Pertanian Kota Binjai),” Jurnal Informatika Kaputama (JIK), vol. 6, no. 3, pp. 336–344, Aug. 2022.

I. Vhallah and J. Santony, “Pengelompokan Mahasiswa Potensial Drop Out menggunakan Metode Clustering K-Means,” SMARTICS Journal, vol. 2, no. 2, pp. 572–577, 2018, [Online]. Available: http://jurnal.iaii.or.id

Teguh Pribadi, Rahmad Irsyada, Hastie Audytra, and Doni Abdul Fatah, “Implementasi Algoritma K-Means Untuk Klasterisasi Potensi Desa Pada Sektor Produksi Pertanian Di Kabupaten Bojonegoro,” Jurnal SimanteC, vol. 9, pp. 20–28, 2020.

S. Nanda Saputra, E. Haerani, L. Oktavia, and F. Syafria, “Penerapan Algoritma K-Means Pada Clustering Penerima Bantuan Pangan Non Tunai (BPNT) Application of K-Means Algorithm on Clustering Recipients of Non-Cash Food Assistance (NCFA),” Journal of Computing Engineering, System and Science), vol. 8, no. 2, pp. 438–449, 2023, [Online]. Available: www.jurnal.unimed.ac.id

E. Irfiani, S. Sulistia Rani, S. Nusa Mandiri Jl Kramat Raya No, and J. Pusat, “Algoritma K-Means Clustering untuk Menentukan Nilai Gizi Balita,” Jurnal Sistem Dan Teknologi Informasi, vol. 6, no. 4, pp. 17–27, Oct. 2018.

E. Muningsih, I. Maryani, and V. R. Handayani, “Penerapan Metode K-Means dan Optimasi Jumlah Cluster dengan Index Davies Bouldin untuk Clustering Propinsi Berdasarkan Potensi Desa,” Jurnal Sains dan Manajemen, vol. 9, no. 1, pp. 95–100, Mar. 2021, [Online]. Available: www.bps.go.id


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Article History
Submitted: 2023-11-02
Published: 2023-11-30
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