Random Forest Regression Algorithm in Predicting Coconut Plantation Yields
Keywords:
Oil Palm, Production Prediction, Random Forest Regression.
Article Metrics
Abstract view : 556 timesAbstract
Oil palm is one of Indonesia’s leading commodities with a significant contribution to the national economy. Production fluctuations caused by environmental and technical factors require an accurate predictive model. This study aims to predict Fresh Fruit Bunch (FFB) production using the Random Forest Regression algorithm based on data from PT Perkebunan Nusantara IV Regional 1, Bandar Selamat Unit (2022–2024). The research employed historical data including land area, number of trees, plant density, bunch count, and planting year. The model underwent preprocessing, training, testing, and evaluation using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R²). Results show that Random Forest Regression achieved excellent accuracy with R² = 0.9846, MAE = 31,889.58 kg, and RMSE = 55,164.62 kg. The most influential factors were planting year, number of trees, and land area. In conclusion, Random Forest Regression is highly effective for predicting oil palm production and captures complex non-linear relationships among variables.
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Tjandra, W., Ginting, C., & Gunawan, S. (2023). Determination of Fertilizer Dosage Based on Fresh Fruit Bunch (FFB) Tonnage Data on Oil Palm Plantations. AGROISTA: Journal of Agrotechnology, 7(1), 8–16. https://doi.org/10.55180/agi.v7i1.341
Bishnoi, S., & Hooda, B. K. (2022). Decision Tree Algorithms and their Applicability in Agriculture for Classification. Journal of Experimental Agriculture International, 44(7), 20–27. https://doi.org/10.9734/jeai/2022/v44i730833
Breiman, L. (2020). Random Forests. Machine Learning, 45, 5–32. https://doi.org/10.1007/978-3-030-62008-0_35
Danny, M., & Muhidin, A. (2025). Optimization of Random Forest Algorithm for Global Palm Oil Export Prediction. Https://Hostjournals.Com/Bulletincsr/Article/View/744?Utm_source=chatgpt.Com, 5(5), 1129–1138.
Firdawanti, A. R., Sumertajaya, I. M., & Sartono, B. (2020). Random Forest Lag Distributed Regression for Forecasting on Palm Oil Production. CSA 2019: Proceedings of the 1st International Conference on Statistics and Analytics. https://doi.org/10.4108/eai.2-8-2019.2290493
Gómez-Méndez, I., & Joly, E. (2023). Regression with missing data, a comparison study of techniques based on random forests. Journal of Statistical Computation and Simulation, 93(12), 1924–1949. https://doi.org/10.1080/00949655.2022.2163646
Hastie, T., Tibshirani, R., & Friedman, J. (2021). The Elements of Statistical Learning : Data Mining, Inference and Prediction (2nd Ed.). Springer. https://doi.org/10.3390/math11194129
Heizer, J., Render, B., & Munson, C. (2024). Operations Management : Sustainability and Supply Chain Management (19th Ed.). Pearson.
Hermawan, R., Suarna, N., Ali, I., & Rohman, D. (2025). Optimization of sales turnover prediction in tofu processing plants using linear regression algorithms. Journal of Informatics and Applied Electrical Engineering, 13(1). https://doi.org/10.23960/jitet.v13i1.5888
Hidayah, K. T., Arifitama, B., & Permana, S. D. H. (2024). Classification of Cervical Cancer Diseases Based on Habits and Medical Records with the C4.5 Method. National Journal of Information Technology and Systems, 10(1), 36–44. https://doi.org/10.25077/teknosi.v10i1.2024.36-44
Imawan, R., Sidhi, E. Y., Sutiknjo, T. D., & Aji, S. B. (2022). Comparison of Income of Self-Employed Oil Palm Farming in Block A and Block B of Bumi Jaya Village, Central Seruyan District, Seruyan Regency, Central Kalimantan. JINTAN : National Agricultural Scientific Journal, 2(2), 137. https://doi.org/10.30737/jintan.v2i2.2776
Jackins, V., Vimal, S., Kaliappan, M., & Lee, M. Y. (2021). AI-Based Smart Prediction of Clinical Disease Using Random Forest Classifier and Naive Bayes. Journal of Supercomputing, 77(5), 5198–5219. https://doi.org/10.1007/s11227-020-03481-x
Justam, J., Jamilah, N., Umar, S. M., Erlita, E., & Ramba, J. (2024). Application of C4.5 and Random Forest Algorithms for Road Damage Mapping with WebGIS. Scientific Journal of Information Systems and Informatics Engineering (JISTI), 7(2), 326–339. https://doi.org/10.57093/jisti.v7i2.270
Khan, N., Kamaruddin, M. A., Ullah Sheikh, U., Zawawi, M. H., Yusup, Y., Bakht, M. P., & Mohamed Noor, N. (2022). Prediction of Oil Palm Yield Using Machine Learning In The Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow. Plants, 11(13). https://doi.org/10.3390/plants11131697
Monita, C. F., & Zebua, D. D. N. (2023). Factors Affecting Palm Oil Productivity at PT. Mustika Agung Sentosa. Journal Of Agribusiness Management, 11(01), 231. https://doi.org/10.24843/jma.2023.v11.i01.p18
Nain, F. N. M., Malim, N. H. A. H., Abdullah, R., Rahim, M. F. A., Mokhtar, M. A. A., & Fauzi, N. S. M. (2022). A Review of An Artificial Intelligence Framework For Identifying The Most Effective Palm Oil Prediction. Algorithms, 15(6), 1–54. https://doi.org/10.3390/a15060218
Norhalimi, M., & Siswa, T. A. Y. (2022). Optimization of Information Gain Feature Selection on Naïve Bayes and K-Nearest Neighbor Algorithms. JISKA (Sunan Kalijaga Journal of Informatics), 7(3), 237–255. https://doi.org/10.14421/jiska.2022.7.3.237-255
Pamuji, F. Y., & Ramadhan, V. P. (2021). Comparison of Random Forest and Decision Tree Algorithms to Predict Immunotherapy Success. Journal of Information Technology and Management, 7(1), 46–50. https://doi.org/10.26905/jtmi.v7i1.5982
Perkovic, L. (2022). Introduction to Computing Using Python: An Application Development Focus (2nd Ed.). Wiley.
Prasakti, L. A., & Juliane, C. (2023). Application of Forecasting Using the Time Series Method to Determine Sales Projections in Furniture Manufacturing Companies. Building of Informatics, Technology and Science (BITS), 4(4). https://doi.org/10.47065/bits.v4i4.2802
Primajaya, A., & Sari, B. N. (2020). Random Forest Algorithm for Prediction of Precipitation. Indonesian Journal of Artificial Intelligence and Data Mining, 1(1), 27–31. https://doi.org/10.24014/ijaidm.v1i1.4903
Rhodes, J. S., Cutler, A., & Moon, K. R. (2023). Geometry- and Accuracy-Preserving Random Forest Proximities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9), 10947–10959. https://doi.org/10.1109/TPAMI.2023.3263774
Saadah, S., & Salsabila, H. (2021). Bitcoin price prediction uses the Random Forest method. Journal of Applied Computers, 7(1), 24–32. https://doi.org/10.35143/jkt.v7i1.4618
Salman, H. A., Kalakech, A., & Steiti, A. (2024). Random Forest Algorithm Overview. Babylonian Journal of Machine Learning, 2024, 69–79. https://doi.org/10.58496/bjml/2024/007
Santra, A. K., & Christy, C. J. (2022). An Efficient Document Clustering by Optimization Technique for Cluster Optimality. International Journal of Computer Applications, 43(16), 15–20. https://doi.org/10.5120/6187-8666
Sirtin, A. A., Makky, M., Santosa, & Cherie, D. (2025). Non-Destructive Evaluation Quality of Oil Palm Fresh Fruit Bunch (FFB) (Elaeis guineensis Jacq.) Using Thermal Imaging in the Grading Process. Eksakta : Berkala Ilmiah Bidang MIPA, 26(03), 312–328. https://doi.org/10.24036/eksakta/vol26-iss03/611
Sulistya, Y. I., Musdholifah, A., Sapuletea, C., Br Bangun, E. T., Hamda, H., Anjani, S., & Septiadi, A. D. (2024). Prediction and Analysis of Rice Production and Yields Using Ensemble Learning Techniques. ILKOM Jurnal Ilmiah, 16(2), 115–124. https://doi.org/10.33096/ilkom.v16i2.1948.115-124
Sumartini, S. H., & Purnam, S. W. (2022). The Use of the Classification and Regression Trees (CART) Method for the Classification of Recurrence of Cervical Cancer Patients at Dr. Soetomo Surabaya Hospital. ITS Journal of Science and Arts, 4(2), 211–216. https://doi.org/10.12962/j23373520.v4i2.10673
Syairozi, M. I. (2021). ANALYSIS OF POVERTY IN THE AGRICULTURAL SECTOR (Case Study of Rice Commodity in Malang Regency). Media Economics, 28(2), 113–128. https://doi.org/10.25105/me.v28i2.7169
Tjandra, W., Ginting, C., & Gunawan, S. (2023). Determination of Fertilizer Dosage Based on Fresh Fruit Bunch (FFB) Tonnage Data on Oil Palm Plantations. AGROISTA: Journal of Agrotechnology, 7(1), 8–16. https://doi.org/10.55180/agi.v7i1.341
Published
2025-10-20
How to Cite
Nadia, C. M., & M. Fakhriza. (2025). Random Forest Regression Algorithm in Predicting Coconut Plantation Yields. Jurnal Multimedia Dan Teknologi Informasi (Jatilima), 7(03), 728-739. https://doi.org/10.54209/jatilima.v7i03.1736
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Copyright (c) 2025 Cut Mirna Nadia, M. Fakhriza
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