Penerapan Data Mining Menggunakan Algoritma Regresi Linear Untuk Prediksi Hasil Produksi Pertanian Berbasis Web
Article Sidebar
Main Article Content
Abstract
Hasil produksi pertanian dipengaruhi oleh berbagai faktor, seperti luas lahan, jenis tanaman, biaya produksi, penggunaan pupuk, serta kondisi produksi pada periode sebelumnya. Ketidakpastian hasil produksi dapat menyulitkan petani dalam melakukan perencanaan dan pengambilan keputusan, terutama apabila perkiraan produksi masih dilakukan berdasarkan pengalaman tanpa didukung oleh pengolahan data historis. Penelitian ini bertujuan menerapkan teknik Data Mining menggunakan algoritma Regresi Linear untuk memprediksi hasil produksi pertanian berbasis web. Data yang digunakan berupa data historis kegiatan produksi pertanian yang mencakup variabel luas lahan, biaya produksi, dan hasil produksi pada periode sebelumnya. Tahapan penelitian meliputi pengumpulan data, preprocessing data, pemilihan variabel, pembentukan model Regresi Linear, pengujian model, evaluasi hasil prediksi, dan implementasi model ke dalam sistem berbasis web. Algoritma Regresi Linear digunakan untuk mengetahui hubungan antara variabel yang memengaruhi produksi dengan hasil produksi serta menghasilkan nilai prediksi berdasarkan data masukan. Sistem berbasis web dikembangkan untuk memudahkan pengguna dalam memasukkan data, melakukan proses prediksi, dan memperoleh informasi hasil estimasi produksi. Evaluasi kinerja model dilakukan menggunakan metrik Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), dan koefisien determinasi (R²). Hasil penelitian diharapkan dapat menghasilkan model prediksi yang mampu memberikan estimasi hasil produksi secara objektif berdasarkan data historis serta membantu petani dalam melakukan perencanaan kegiatan produksi. Penerapan sistem berbasis web juga diharapkan dapat meningkatkan kemudahan akses terhadap informasi prediksi dan mendukung pemanfaatan data dalam pengambilan keputusan pada kegiatan pertanian.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
Sellam, V., & Poovammal, E. (2016). Prediction of crop yield using regression analysis. Indian Journal of Science and Technology, 9(38), 1–5. https://doi.org/10.17485/ijst/2016/v9i38/91714.
van Klompenburg, T., Kassahun, A., & Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics in Agriculture, 177, 105709. https://doi.org/10.1016/j.compag.2020.105709.
Maya Gopal, P. S., & Bhargavi, R. (2019). A novel approach for efficient crop yield prediction. Computers and Electronics in Agriculture, 165, 104968. https://doi.org/10.1016/j.compag.2019.104968.
Matsumura, K., Gaitan, C. F., Sugimoto, K., Cannon, A. J., & Hsieh, W. W. (2015). Maize yield forecasting by linear regression and artificial neural networks in Jilin, China. The Journal of Agricultural Science, 153(3), 399–410. https://doi.org/10.1017/S0021859614000392.
Ansarifar, J., Wang, L., & Archontoulis, S. V. (2021). An interaction regression model for crop yield prediction. Scientific Reports, 11, 17754. https://doi.org/10.1038/s41598-021-97221-7.
Bali, N., & Singla, A. (2021). Deep learning based wheat crop yield prediction model in Punjab region of North India. Applied Artificial Intelligence, 35(15), 1304–1328. https://doi.org/10.1080/08839514.2021.1976091.
Vinson Joshua, S., Selwin Mich Priyadharson, A., Kannadasan, R., Khan, A. A., Lawanont, W., Khan, F. A., Rehman, A. U., & Ali, M. J. (2022). Crop yield prediction using machine learning approaches on a wide spectrum. Computers, Materials & Continua, 72(3), 5663–5679. https://doi.org/10.32604/cmc.2022.027178.
Conradt, T. (2022). Choosing multiple linear regressions for weather-based crop yield prediction with ABSOLUT v1.2 applied to the districts of Germany. International Journal of Biometeorology, 66, 2287–2300. https://doi.org/10.1007/s00484-022-02356-5.
Talaat, F. M. (2023). Crop yield prediction algorithm (CYPA) in precision agriculture based on IoT techniques and climate changes. Neural Computing and Applications, 35, 17281–17292. https://doi.org/10.1007/s00521-023-08619-5.
Fei, S., Xiao, S., Zhu, J., Xiao, Y., & Ma, Y. (2024). Dual sampling linear regression ensemble to predict wheat yield across growing seasons with hyperspectral sensing. Computers and Electronics in Agriculture, 216, 108514. https://doi.org/10.1016/j.compag.2023.108514.
Htun, A. M., Shamsuzzoha, M., & Ahamed, T. (2023). Rice yield prediction model using normalized vegetation and water indices from Sentinel-2A satellite imagery datasets. Applied Journal of Environmental Engineering, 7(2). https://doi.org/10.1007/s41685-023-00299-2.
Wang, Y., Shi, W., Wen, T., et al. (2023). Prediction of winter wheat yield and dry matter in North China Plain using machine learning algorithms for optimal water and nitrogen application. Agricultural Water Management, 277, 108140. https://doi.org/10.1016/j.agwat.2023.108140.
Tripathi, A., Tiwari, R. K., & Tiwari, S. P. (2022). A deep learning multi-layer perceptron and remote sensing approach for soil health based crop yield estimation. International Journal of Applied Earth Observation and Geoinformation, 113, 102959. https://doi.org/10.1016/j.jag.2022.102959.
Torgbor, B. A., Rahman, M. M., Brinkhoff, J., Sinha, P., & Robson, A. (2023). Integrating remote sensing and weather variables for mango yield prediction using a machine learning approach. Remote Sensing, 15(12), 3075. https://doi.org/10.3390/rs15123075.
De Clercq, D., & Mahdi, A. (2024). Feasibility of machine learning-based rice yield prediction in India at the district level using climate reanalysis and remote sensing data. Agricultural Systems, 104099. https://doi.org/10.1016/j.agsy.2024.104099.
Santos, L. B., Gentry, D., Tryforos, A., Fultz, L., Beasley, J., & Gentimis, T. (2024). Soybean yield prediction using machine learning algorithms under a cover crop management system. Smart Agricultural Technology, 8, 100442. https://doi.org/10.1016/j.atech.2024.100442.