Prediction of Rice Productivity Using the Random Forest Regression Algorithm in Cikaret Subdistrict for the Years 2020-2024
DOI:
https://doi.org/10.12962/geoid.v20i1.2570Keywords:
challanges, forecasting, MAPE, MSE, RMSEAbstract
The challenges surrounding rice productivity in Indonesia are growing more complex due to factors like climate change, population growth, and limited agricultural land. As the primary food source and main carbohydrate provider, rice is crucial for the majority of Indonesians. This study focuses on predicting rice productivity using the random forest regression algorithm, incorporating predictor variables such as NDVI, NDMI, land area, land surface temperature, rainfall, fertilizer type, and pests. To ensure the accuracy of the model, multicollinearity tests were conducted to check for strong correlations among the independent variables. The tests confirmed the absence of significant linear relationships, allowing all variables to be included in the model. The prediction model was built using time-series data from 2020 to 2023, resulting in 840 samples after eliminating outliers. The optimization process targeted the mtry parameter and the number of decision trees to reduce prediction error. The optimal model, utilizing 7 predictor features and 150 decision trees, achieved a low out of bag (OOB) error and stable mean square error (MSE). Model performance metrics showed a Mean Absolute Error (MAE) of 0.324 tons/hectare, MSE of 0.158 tons/hectare, Root Mean Square Error (RMSE) of 0.398 tons/hectare, and a coefficient of determination (R²) of 0.87. These results demonstrated that the random forest regression algorithm is highly effective in predicting rice productivity, particularly when dealing with complex data involving multiple predictor variables and potential multicollinearity.
References
Agustian, I., Saputra, H. E., & Imanda, A. (2019). Pengaruh Sistem Informasi Manajamen Terhadap Peningkatan Kualitas Pelayanan Di Pt. Jasaraharja Putra Cabang Bengkulu. Profesional: Jurnal Komunikasi Dan Administrasi Publik, 6(1), 42–60. https://doi.org/10.37676/professional.v6i1.837
Amrullah., S. D. (2014). Peningkatan Produktivitas Tanaman Padi (Oryza sativa L.) melalui Pemberian Nano Silika. Pangan, 23(1-2), 17-32.
Ariani, D., Prasetyo, Y., & Sasmito, B. (2020). Estimasi Tingkat Produktivitas Padi Berdasarkan Algoritma NDVI, EVI Dan SAVI Menggunakan Citra Sentinel-2 Multitemporal (Studi Kasus: Kabupaten Pekalongan, Jawa Tengah). Jurnal Geodesi Undip, 9(1), 207–216.
Bagio., & Athaillah, T. (2020). Pembukuan Usaha Tani Padi di Desa Leuhan Kecamatan Johan Pahlawan Kabupaten Aceh Barat. Jurnal Abdimas Bina Bangsa, 1(1), 80-86. https://doi.org/10.46306/jabb.v1i1.13
Boamah, P. O., Onumah, J., Apam, B., Salifu, T., Abunkudugu, A. A., & Alabil, S. A. (2024). Climate variability impact on crop evapotranspiration in the upper East region of Ghana. Environmental Challenges, 14, 100828. https://doi.org/10.1016/j.envc.2023.100828
BPS. (2024). Luas Panen Padi dan Produksi Padi di Indonesia 2023. Badan Pusat Statistik.
Champaneri, M., Chachpara, D., Chandvidkar, C., & Rathod, M. (2020). Crop Yield Prediction Using Machine Learning. International Journal of Science and Research, 9(4), 1-5.
Chang, X., Xing, Y., Gong, W., Yang, C., Guo, Z., Wang, D., . . . Yang, S. (2023). Evaluating gross primary productivity over 9 ChinaFlux sites based on random forest regression models, remote sensing, and eddy covariance data. Science of the Total Environment 875(162601).
Firmansyach, W. A., Hayati, U., & Arie Wijaya, Y. (2023). Analisa Terjadinya Overfitting Dan Underfitting Pada Algoritma Naive Bayes Dan Decision Tree Dengan Teknik Cross Validation. JATI (Jurnal Mahasiswa Teknik Informatika), 7(1), 262–269. https://doi.org/10.36040/jati.v7i1.6329
Google, E. E. (2004). Earth Engine Data Catalog. Dipetik July 24, 2024, dari https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED#bands
Hidayati, D., & Aldrian, E. (2012). Perubahan iklim: upaya peningkatan pengetahuan dan adaptasi petani dan nelayan melalui radio. Bogor: Sarana Komunikasi Utama.
López, M. O. A., López, M. A., & Crossa, J. (2022). Multivariate statistical machine learning methods for genomic prediction. Mexico: Springer.
Marzani, Y., & Juliannisa, I. A. (2024). Assessment Ketersediaan Beras Pada 34 Provinsi Di Indonesia. Jurnal Ekonomi Pertanian dan Agribisnis (JEPA), 8(2), 700–711.
Masdian, A. R., Bashit, N., & Hadi, F. (2023). Analisis Produktivitas Padi Menggunakan Algoritma Machine Learning Random Forest Di Kabupaten Batang Tahun 2018 - 2022. Elipsoida: Jurnal Geodesi dan Geomatika, 06(01), 43-51.
Nti, I. K., Zaman, A., Nyarko-Boateng, O., Adekoya, A. F., & Keyeremeh, F. (2023). A predictive analytics model for crop suitability and productivity with tree-based ensemble learning. Decision Analytics Journal, 8(August), 1-11.
Nur, N., Wajid, F., Sulfayanti, & Wildayani. (2023). Implementasi Algoritma Random Forest Regression untuk Memprediksi Hasil Panen Padi di Desa Minanga. Jurnal Komputer Terapan, 9(1), 58-64.
Oshiro, T.M., Perez, P.S., Baranauskas, J.A. (2012). How Many Trees in a Random Forest?. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2012. Lecture Notes in Computer Science, vol 7376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31537-4_13
Putri, F. A. (2023). Optimalisasi Produksi Padi Menuju Ketahanan Pangan di Jawa Tengah. Seminar Nasional Official Statistics, 2023(1), 827–838. https://doi.org/10.34123/semnasoffstat.v2023i1.1888
Ramadhona, G., Setiawan, B. D., & Bachtiar, F. A. (2018). Prediksi Produktivitas Padi Menggunakan Jaringan Syaraf Tiruan Backpropagation. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(12), 6048–6057.
Ruminta. (2016). Analisis Penurunan Produksi Tanaman Padi Akibat Perubahan Iklim di Kabupaten Bandung Jawa Barat. Jurnal Kultivasi, 15(1), 37-45.
Suwarno. (2010). Meningkatkan Produksi Padi Menuju Ketahanan Pangan yang Lestari. Pangan, 19(3), 234-243.
Teng, Z., Chen, Y., Meng, S., Duan, M., Zhang, J., & Ye, N. (2023). Environmental Stimuli: A Major Challenge during Grain Filling in Cereals. International Journal of Molecular Sciences, 24(3). https://doi.org/10.3390/ijms24032255
Triscowati, D. W., Buana, W. P., & Marsuhandi, A. H. (2021). Pemetaan Potensi Lahan Jagung Menggunakan Citra Satelit Dan Random Forest Pada Cloud computing Google Earth Engine. Seminar Nasional Official Statistics, Daring: Politeknik Statistika STIS, 1001-1011
