Pemanfaatan Machine Learning dengan Algoritma X-Means untuk Pemetaan Luas Panen, Produktivitas, dan Produksi Padi
-
Irma Hakim
*
Universitas Muhammadiyah Makassar, Makassar,
Indonesia
- M. Rafid Institut Agama Islam Al-Amanah Jeneponto, Jenepento, Indonesia
- Fitri Anggraini STIKOM Tunas Bangsa, Pematangsiantar, Indonesia (*) Corresponding Author
Abstract
Rice plants are essential for the world, especially Indonesia because it is a rice-producing plant that is useful as a staple food for its people. A decreased harvest area, production, and rice productivity can affect food availability. Therefore, this research aims to classify and map the harvested area, production, and productivity of rice in Indonesia based on each province. The research data used in this paper is data on the harvested area (ha), production (tons), and rice productivity (Ku/ha) by Provinces in Indonesia for 2020-2022 obtained from the Indonesian Central Bureau of Statistics website. In this study, the algorithm used is X-Means Clustering with the help of the Rapid Miner application. The results of this study are in the form of grouping or mapping of harvested area, production, and productivity of rice, divided into 3 (three) regions, including 1. Harvested Area (divided into five groups: Very high Harvested Area consists of 3 provinces, High Harvested Area consists of 1 province, Medium Harvest Area consists of 3 Provinces, Low Harvest Area consists of 8 Provinces, and Very low Harvest Area consists of 19 Provinces 2. Rice Production Area (divided into five groups: Very high rice production consists of 3 provinces, Rice production High rice production consists of 1 province, Medium rice production consists of 3 Provinces, Low rice production consists of 8 Provinces, and Very low rice production consists of 19 Provinces 3. Regions of Rice Productivity (divided into five groups: Very high rice productivity consists of 6 provinces, High Rice Productivity consists of 13 provinces, Medium Rice Productivity consists of 7 Provinces, Low Rice Productivity consists of 4 Provinces, and Very Low Rice Productivity consists of 4 Provinces. This can be information for the Indonesian government, especially for the respective provincial governments, to be able to maintain the harvested area, production, and productivity of rice in Indonesia to remain stabel.
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References
N. Sulistiyanto, Tri Aristy Saputri, “Deteksi Dini Hama dan Penyakit Padi Menggunakan Metode Certainty Factor,” JURIKOM (Jurnal Riset Komputer), vol. 9, no. 1, pp. 48–54, 2022.
K. Rangkuti, S. Harahap, S. Siregar, and T. Hutauruk, “Feasibility Analysis of Palm Sugar Business (Case Study: Buluh Awar Village, Sibolangit District, Deli Serdang Regency) Analisis Kelayakan Usaha Gula Aren (Studi Kasus : Desa Buluh Awar, Kecamatan Sibolangit Kabupaten Deli Serdang),” Journal of Agribusiness Sciences, vol. 4, no. 1, pp. 1–7, 2020.
R. J. Nugroho and I. N. Ramadhan, “Analisis Pendapatan dan Kelayakan Hasil Usahatani Padi Sawah di Desa Mrentul Kecamatan Bonorowo Kabupaten Kebumen,” Jurnal Kridatama Sains dan Teknologi, vol. 03, no. 01, pp. 79–87, 2021.
Isna Nur Azizah, P. R. Arum, and R. Wasono, “Model Terbaik Uji Multikolinearitas untuk Analisis Faktor-Faktor yang Mempengaruhi Produksi Padi di Kabupaten Blora Tahun 2020,” Prosiding Seminar Nasional UNIMUS, vol. 4, pp. 61–69, 2021.
S. S. Aulia, D. S. Rimbodo, and M. G. Wibowo, “Faktor-faktor yang Mempengaruhi Nilai Tukar Petani (NTP) di Indonesia,” Journal of Economics and Business Aseanomics, vol. 6, no. 1, pp. 44–59, 2021.
A. Nurkholis, M. Muhaqiqin, and T. Susanto, “Analisis Kesesuaian Lahan Padi Gogo Berbasis Sifat Tanah dan Cuaca Menggunakan ID3 Spasial,” JUITA: Jurnal Informatika, vol. 8, no. 2, pp. 235–244, 2020.
L. Hasanah, “Analisis Faktor-Faktor Pengaruh Terjadinya Impor Beras di Indonesia Setelah Swasembada Pangan,” Growth: Jurnal Ilmiah Ekonomi Pembangunan, vol. 1, no. 2, pp. 57–72, 2022.
F. M. Ariska and B. Qurniawan, “Perkembangan Impor Beras di Indonesia,” Jurnal Agrimals, vol. 1, no. 1, pp. 27–34, 2021.
F. Marisa et al., “Digitasi Produktivitas Panen Padi Berbasis K-Means Clustering,” SMARTICS Journal, vol. 7, no. 1, pp. 21–26, 2021.
S. Paipan and M. Abrar, “Analisis Kondisi Ketergantungan Impor Beras Di Indonesia,” Jurnal Perspektif Ekonomi Darussalam, vol. 6, no. 2, pp. 212–222, 2020.
U. Maman, I. Aminudin, and E. Novriana, “Efektifitas Pupuk Bersubsidi Terhadap Peningkatan Produktivitas Padi Sawah,” Jurnal Agribisnis Terpadu, vol. 14, no. 2, p. 176, 2021.
Irawan, Hermansyah, and A. K. Khoerulloh, “Konsep Ba’i Salam dan Implementasinya dalam Mewujudkan Ketahanan pangan Nasional,” Iqtisadiya: Jurnal Ilmu Ekonomi Islam, vol. 7, no. 14, pp. 43–60, 2020.
D. A. Muji and C. P. Rahmadani, “Peran ASEAN Plus Three Melalui Komitmen ASEAN Plus Three Emergency Rice Reserve (APTERR) Dalam Penangan Isu Ketahanan Pangan Di Asia Tenggara,” Jurnal Transbordensi, vol. 4, no. 1, pp. 36–45, 2020.
M. Wiettimena, T. Sau, and Syahrullah, “Persepsi Petani terhadap Dampak Kerebahan Tanaman Padi di Kel. Wiringpalennae Kec. Tempe Kab. Wajo,” Jurnal Ilmiah Agrotani, vol. 3, no. 2, pp. 241–251, 2021.
N. K. F. Permatasari, M. P. Tambunan, M. D. M. Mannesa, and R. P. Tambunan, “Pengaruh Kekeringan Pada Produksi Tanaman Padi Di Kabupaten Majalengka Dengan Penginderaan Jauh Metode Ndvi,” Jurnal Geosaintek, vol. 7, no. 1, pp. 17–26, 2021.
T. D. Isna, “Daftar Negara Penghasil Beras Terbesar Dunia, Indonesia Urutan Berapa?,” Fortune Media IP Limited, 2022. [Online]. Available: https://www.fortuneidn.com/market/tanayastri/daftar-negara-penghasil-beras-terbesar-dunia-indonesia-urutan-berapa-. [Accessed: 02-Dec-2022].
A. Nurkholis, Muhaqiqin, and T. Susanto, “Algoritme Spatial Decision Tree untuk Evaluasi Kesesuaian Lahan Padi Sawah Irigasi,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 4, no. 5, pp. 978–987, 2020.
K. Muttaqien, A. T. S. Haji, and A. A. Sulianto, “Analisis Kesesuaian Lahan Tanaman Padi Yang Berkelanjutan Di Kabupaten Indramayu,” Jurnal Ilmiah Rekayasa Pertanian dan Biosistem, vol. 8, no. 1, pp. 48–57, 2020.
H. A. Katili and N. M. Sari, “Keseuaian Lahan Untuk Pengembangan Padi Varietas Ranta Dan Habo Kecamatan Batui Kabupaten Banggai,” Jurnal Pertanian Cemara, vol. 18, no. 2, pp. 38–45, 2021.
T. H. Sinaga, A. Wanto, I. Gunawan, S. Sumarno, and Z. M. Nasution, “Implementation of Data Mining Using C4.5 Algorithm on Customer Satisfaction in Tirta Lihou PDAM,” Journal of Computer Networks, Architecture, and High-Performance Computing, vol. 3, no. 1, pp. 9–20, 2021.
A. Pradipta, D. Hartama, A. Wanto, S. Saifullah, and J. Jalaluddin, “The Application of Data Mining in Determining Timely Graduation Using the C45 Algorithm,” IJISTECH (International Journal of Information System & Technology), vol. 3, no. 1, pp. 31–36, 2019.
I. Parlina et al., “Naive Bayes Algorithm Analysis to Determine the Percentage Level of visitors the Most Dominant Zoo Visit by Age Category,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–5, 2019.
S. F. Damanik, A. Wanto, and I. Gunawan, “Penerapan Algoritma Decision Tree C4.5 untuk Klasifikasi Tingkat Kesejahteraan Keluarga pada Desa Tiga Dolok,” Jurnal Krisnadana Volume, vol. 1, no. 2, pp. 21–32, 2022.
N. Arminarahmah, A. D. GS, G. W. Bhawika, M. P. Dewi, and A. Wanto, “Mapping the Spread of Covid-19 in Asia Using Data Mining X-Means Algorithms,” IOP Conf. Series: Materials Science and Engineering, vol. 1071, no. 1, p. 012018, 2021.
F. S. Napitupulu, I. S. Damanik, I. S. Saragih, and A. Wanto, “Algoritma K-Means untuk Pengelompokkan Dokumen Akta Kelahiran pada Tiap Kecamatan di Kabupaten Simalungun,” Building of Informatics, Technology and Science (BITS) Volume, vol. 2, no. 1, pp. 55–63, 2020.
J. Hutagalung, N. L. W. S. R. Ginantra, G. W. Bhawika, W. G. S. Parwita, A. Wanto, and P. D. Panjaitan, “COVID-19 Cases and Deaths in Southeast Asia Clustering using K-Means Algorithm,” Journal of Physics: Conference Series, vol. 1783, no. 1, p. 012027, 2021.
N. A. Febriyati, A. D. GS, and A. Wanto, “GRDP Growth Rate Clustering in Surabaya City uses the K- Means Algorithm,” International Journal of Information System & Technology, vol. 3, no. 2, pp. 276–283, 2020.
M. A. Hanafiah and A. Wanto, “Implementation of Data Mining Algorithms for Grouping Poverty Lines by District/City in North Sumatra,” International Journal of Information System & Technology, vol. 3, no. 2, pp. 315–322, 2020.
G. B. Kaligis and S. Yulianto, “Analisa Perbandingan Algoritma K-Means, K-Medoids, dan X-Means untuk Pengelompokkan Kinerja Kinerja Pegawai,” IT-EXPLORE: Jurnal Penerapan Teknologi Informasi dan Komunikasi, vol. 1, no. 3, pp. 179–193, 2022.
S. Wijayanto and M. Y. Fathoni, “Pengelompokkan Produktivitas Tanaman Padi di Jawa Tengah Menggunakan Metode Clustering K-Means,” Jurnal JUPITER, vol. 13, no. 2, pp. 212–219, 2021.
C. J. Silalahi, A. Situmorang, and J. F. Naibaho, “Implementasi Metode K-Means Clustering Untuk Memetakan Daerah Potensial Penghasil Padi di Provinsi Sumatera Utara,” Methotika : Jurnal Ilmiah Teknik Informatika, vol. 2, no. 2, pp. 49–57, 2022.
Luth Fimawahib, I. R. Bakti, and A. Supriyanto, “Algoritma K-Medoids untuk Pengelompokan Produksi Padi dan Beras sebagai Upaya Optimalisasi Ketahanan Pangan di Provinsi Riau,” SATIN - Sains dan Teknologi Informasi, vol. 8, no. 2, pp. 13–24, 2022.
S. Monica, F. Natalia, and S. Sudirman, “Clustering Tourism Object in Bali Province Using K- Means and X-Means Clustering Algorithm,” in 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2018, pp. 1462–1467.
F. Noorbehbahani and S. Mansoori, “A New Semi-supervised Method for Network Traffic Classification Based on X-means Clustering and Label Propagation,” in 2018 8th International Conference on Computer and Knowledge Engineering, ICCKE 2018, 2018, pp. 120–125.
J. Ge et al., “LPX: Overlapping community detection based on X-means and label propagation algorithm in attributed networks,” Computational Intelligence, vol. 37, no. 1, pp. 484–510, 2021.
M. Anoop and P. Sripriya, “Focused information criterion based partitioned iterative X-means dice correlation clustering for big Geo-social data,” Journal of Critical Reviews, vol. 7, no. 6, pp. 54–62, 2020.
BPS, “Luas Panen, Produktivitas, dan produksi Padi Menurut Provinsi 2020-2022,” Badan Pusat Statistik Indonesia, 2022. [Online]. Available: https://www.bps.go.id/indicator/53/1498/1/luas-panen-produksi-dan-produktivitas-padi-menurut-provinsi.html. [Accessed: 24-Nov-2022].
N. Arminarahmah, A. D. GS, G. W. Bhawika, M. P. Dewi, and A. Wanto, “Mapping the Spread of Covid-19 in Asia Using Data Mining X-Means Algorithms,” IOP Conference Series: Materials Science and Engineering, vol. 1071, no. 1, p. 012018, 2021.
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