Implementation of the Decision Tree Algorithm for Seed Selection Recommendation, Harvest Prediction, and Profit Estimation in the Agricultural Sector
DOI:
https://doi.org/10.37676/jki.v5i3.1815Keywords:
Decision Tree, Data Mining, Agriculture, Harvest, Prediction, Seed Recommendation, Profit EstimationAbstract
The agricultural sector requires accurate decision-making in selecting suitable seeds, predicting harvest yields, and estimating farming profits based on land conditions and cultivation factors. This study aims to implement the Decision Tree algorithm as a classification method to provide seed selection recommendations, predict harvest yields, and estimate farmers' profits. The variables used include crop type, seed type, soil pH, soil moisture, rainfall, temperature, land area, fertilizer type, production cost, and pest infestation level. The research process consists of data collection, preprocessing, Decision Tree model development, model training, and evaluation using a confusion matrix, accuracy, precision, recall, and F1-score. The results indicate that the Decision Tree algorithm is effective in generating classification models capable of recommending appropriate seed types, predicting harvest categories, and estimating farming profits. The developed system is expected to support farmers in improving productivity and making data-driven agricultural decisions
References
Aulia, N., Rizky, M., & Shaleh, C. (2025). Memprediksi jenis tanaman berdasarkan parameter lingkungan menggunakan Decision Tree dan Random Forest. Jurnal Teknologi Informasi dan Terapan.
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
Bishnoi, S., & Hooda, B. K. (2022). Decision Tree algorithms and their applicability in agriculture for classification. Journal of Experimental Agriculture International, 44(10), 65–75.
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth International Group.
Dillon, J. L., & Hardaker, J. B. (1993). Farm Management Research for Small Farmer Development. Food and Agriculture Organization (FAO).
Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann.
Islam, T., dkk. (2021). A deep neural network approach for crop selection and yield prediction. arXiv preprint arXiv:2108.03320.
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90.
Khaki, S., & Wang, L. (2019). Crop yield prediction using deep neural networks. Frontiers in Plant Science.
Kusrini, & Luthfi, E. T. (2009). Algoritma Data Mining. Andi Offset.
Larose, D. T. (2014). Discovering Knowledge in Data: An Introduction to Data Mining (2nd ed.). Wiley.
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.
Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.
Power, D. J. (2002). Decision Support Systems: Concepts and Resources for Managers. Quorum Books.
Prasetyo, T. A., dkk. (2026). Enhanced rice yield prediction in Indonesia with integrated climate and agricultural data using Decision Tree Regression. Register: Jurnal Ilmiah Teknologi Sistem Informasi.
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106.
Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann.
Roni, Abdullah, & Insani. (2025). Implementasi Naïve Bayes dan Decision Tree untuk klasifikasi jenis tanaman. Jurnal Tekno Insentif.
Satria, A., Badri, R. M., & Safitri, I. (2023). Prediksi hasil panen tanaman pangan menggunakan metode Machine Learning. Digital Transformation Technology Journal.
Soekartawi. (2002). Analisis Usahatani. UI Press.
Suradiradja, K., Sitanggang, I. S., Abdullah, L., & Hermadi, I. (2022). Estimation of harvest time of forage sorghum using Decision Tree algorithm. Tropical Animal Science Journal, 45(2), 188–198.
Tan, P. N., Steinbach, M., & Kumar, V. (2019). Introduction to Data Mining (2nd ed.). Pearson.
Turban, E., Sharda, R., & Delen, D. (2011). Decision Support and Business Intelligence Systems (9th ed.). Pearson.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2017). Data Mining: Practical Machine Learning Tools and Techniques (4th ed.). Morgan Kaufmann.
Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming: A review. Agricultural Systems, 153, 69–80.
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