Pemetaan Produksi Tanaman Tomat di Indonesia Berdasarkan Provinsi Menggunakan Algoritma K-Means Clustering
- Syaifuddin Syaifuddin Politeknik Pembangunan Pertanian Gowa, Makassar, Indonesia
- Ramlah Ramlah Sekolah Tinggi Pertanian Kutai Timur, Sangatta, Indonesia
- Irma Hakim Universitas Muhammadiyah Makassar, Makassar, Indonesia
- Yunida Berliana Universitas Tjut Nyak Dhien, Medan, Indonesia
-
Nurhayati Nurhayati
*
Universitas Islam Sumatera Utara, Medan,
Indonesia
(*) Corresponding Author
Abstract
Tomato is one of the essential horticultural commodity vegetables because it has high economic value. The need for this plant continues to increase along with the increase in population, income levels, and heightened public awareness of the importance of nutritional value. Therefore, this research aims to see and map the production of tomato plants in Indonesia by the province in the form of clusters (grouping). The research data used in this paper is data on tomato production in Indonesia by the province in the last five years (2017-2021) obtained from the District/City Agriculture Service of each province and the Indonesian Central Statistics Agency. The algorithm proposed in this study is K-Means Clustering with the help of RapidMiner. The results of the proposed paper are grouping and mapping of tomato production in Indonesia, which is divided into 5 (five) zones, including the Black Zone (areas with very high tomato production), which consists of 1 province, Green Zone (areas with high production of tomatoes). Which consists of 2 provinces, the Blue Zone (areas with moderate production), which consists of 4 provinces. The Light Blue Zone (areas with low production), which consists of 8 provinces, and the Orange Zone (areas with moderately low production), which consists of 18 provinces.
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References
W. Wardana and W. O. Alzarliani, “Faktor-Faktor yang Mempengaruhi Minat Petani Menerapkan Teknologi Pengolahan Buah Tomat di Desa Wakuli Kecamatan Kapontori Kabupaten Buton,” Agrikan: Jurnal Agribisnis Perikanan, vol. 12, no. 1, pp. 145–151, 2019.
F. Fitriani, Y. Evayanti, and N. Isnaini, “Pemberian Jus Tomat Terhadap Kadar Hemoglobin Pada Ibu Hamil Trimester Iii Tahun 2019,” Jurnal Kebidanan Malahayati, vol. 6, no. 2, pp. 230–235, 2020.
Chitra Anggriani Salingkat, A. Noviyanty, and Syamsiar, “Pengaruh Jenis Bahan Pengemas, Suhu dan Lama Penyimpanan terhadap Karakteristik Mutu Buah Tomat,” Agroland: Jurnal Ilmu-ilmu Pertanian, vol. 27, no. 3, pp. 274–286, 2020.
D. Septiadi and A. I. Mundiyah, “Karakteristik dan Analisis Finansial Usaha Tani Tomat di Kabupaten Lombok Timur,” Agroteksos, vol. 31, no. 3, pp. 180–188, 2021.
S. A. Assagaf, “Pengaruh Pemberian Mulsa Alang-Alang dan Pupuk NPK Phonska Terhadap Pertumbuhan dan Produksi Tanaman Tomat (Solanum lycopersicum),” Jurnal Biosainstek, vol. 2, no. 1, pp. 40–46, 2020.
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.
C. Kamila, M. Adiyatma, G. R. Namang, R. Ramadhan, F. Syah, and D. Redaksi, “Pendidikan Teknik Informatika dan Komputer, Fakultas Teknik,” INTECH - Informatika Dan Teknologi, vol. 2, no. 1, pp. 19–24, 2021.
R. N. Fahmi, M. Jajuli, and N. Sulistiyowati, “Analisis Pemetaan Tingkat Kriminalitas di Kabupaten Karawang menggunakan Algoritma K-Means,” INTECOMS: Journal of Information Technology and Computer Science, vol. 4, no. 1, pp. 67–79, 2021.
H. A. Negara, A. R. Putra, and U. Enri, “Clustering Data Ekspor Buah-Buahan Berdasarkan Negera Tujuan Menggunakan Algoritma K-Means,” Bina Insani Ict Journal, vol. 8, no. 1, pp. 73–82, 2021.
W. Maulidi Molyono, S. Achmadi, and Y. Agus Pranoto, “Pemetaan Tambak Garam Serta Produksi Garam Pada Kabupaten Pamekasan Menggunakan K-Means Clustering,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 5, no. 2, pp. 794–799, 2021.
A. Wanto et al., Data Mining : Algoritma dan Implementasi. Yayasan Kita Menulis, 2020.
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