ANALYSIS OF DATA MINING METHODS IN PALM OIL SALES PREDICTION USING RANDOM FOREST AND XGBOOST (CASE STUDY: PT GEKA ADHI RAKSA)
Keywords:
Random Forest, XGBoost, Sales Prediction, Palm Oil, Data Mining
Article Metrics
Abstract view : 425 timesAbstract
The palm oil industry plays a strategic role in Indonesia's economy, yet challenges such as price fluctuations, market demand variability, and external factors like climate change often hinder accurate sales planning. This study aims to analyze palm oil sales patterns and compare the performance of the Random Forest and XGBoost algorithms in predicting sales at PT Geka Adhi Raksa. The methodology involves data preprocessing, model training, and prediction evaluation based on historical sales data from 2023 to 2024. Model performance is assessed using evaluation metrics such as RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), and R² (coefficient of determination). The results indicate that both algorithms provide reliable prediction accuracy, with XGBoost outperforming Random Forest in processing speed and overall accuracy. By adopting this data mining approach, the company can optimize sales strategies, avoid overstock or understock risks, and improve supply chain efficiency. This research is expected to serve as a reference for data-driven decision-making in agribusiness, particularly in the palm oil sector.
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Baskara, A. K., Nazir, A., Irsyad, M., Yusra, Y., & Insani, F. (2023). Implementasi Data Mining Memprediksi Penjualan Crude Palm Oil Berdasarkan Kapasitas Tangki Menggunakan Multiple Linear Regression. Jurnal Sistem Komputer Dan Informatika (JSON), 4(3), 493. https://doi.org/10.30865/JSON.V4I3.5665
Chen, N., Fan, F., Geng, J., Yang, Y., Gao, Y., Jin, H., Chu, Q., Yu, D., Wang, Z., & Shi, J. (2022a). Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms. Frontiers in Public Health, 10. https://doi.org/10.3389/FPUBH.2022.984621/PDF
Chen, N., Fan, F., Geng, J., Yang, Y., Gao, Y., Jin, H., Chu, Q., Yu, D., Wang, Z., & Shi, J. (2022b). Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms. Frontiers in Public Health, 10. https://doi.org/10.3389/FPUBH.2022.984621/PDF
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Erina, N. G. F. (2024). Peran RSPO dan ISPO Pada Keberlanjutan Industri Minyak Kelapa Sawit di Indonesia: Studi Kasus Indofood. Briliant, 9(2), 290–298. https://doi.org/10.28926/BRILIANT.V9I2.1814
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Hong, W., Zhou, X., Jin, S., Lu, Y., Pan, J., Lin, Q., Yang, S., Xu, T., Basharat, Z., Zippi, M., Fiorino, S., Tsukanov, V., Stock, S., Grottesi, A., Chen, Q., & Pan, J. (2022). A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile. Frontiers in Cellular and Infection Microbiology, 12. https://doi.org/10.3389/FCIMB.2022.819267
Iqbal, M., & Efendi, S. (2023). Data-Driven Approach for Credit Risk Analysis Using C4.5 Algorithm. ComTech: Computer, Mathematics and Engineering Applications, 14(1), 11–20. https://doi.org/10.21512/COMTECH.V14I1.8243
Lisanthoni, A., Sari, F. I., Gunawan, E. L., & Adhigiadany, C. A. (2023). Model Prediksi Kepadatan Lalu Lintas: Perbandingan Algoritma Random Forest dan XGBoost. PROSIDING SEMINAR NASIONAL SAINS DATA, 3(1), 296–303. https://doi.org/10.33005/SENADA.V3I1.126
Maulidah, N. (2023). Prediksi Peningkatan Jumlah Nasabah Deposito Berjangka Menggunakan Algoritma KNN, Decision Tree, Random Forest Dan Xgboost. Jurnal Telekomunikasi Dan Komputer, 13(2), 90. https://doi.org/10.22441/INCOMTECH.V13I2.16921
Oktarina, S. D., Nurkhoiry, R., Amalia, R., Pradiko, I., & Rahutomo, S. (2022). DAMPAK KETIDAKPASTIAN COVID-19, IKLIM, DAN KOMPLEKSITAS LAINNYA PADA INDUSTRI KELAPA SAWIT. WARTA Pusat Penelitian Kelapa Sawit, 27(2), 70–77. https://doi.org/10.22302/IOPRI.WAR.WARTA.V27I2.83
Rasyid, M., Sitorus, Z., Wijaya, R. F., Iqbal, M., & Khairul. (2024). MACHINE LEARNING ANALYSIS IN IMPROVING THE EFFICIENCY OF THE STUDENT ADMISSION DECISION MAKING PROCESS NEW AT PANCA BUDI MEDAN DEVELOPMENT UNIVERSITY. Bulletin of Engineering Science, Technology and Industry, 2(3), 216–225. https://doi.org/10.59733/BESTI.V2I3.62
Sarica, A., Cerasa, A., & Quattrone, A. (2017). Random forest algorithm for the classification of neuroimaging data in Alzheimer’s disease: A systematic review. Frontiers in Aging Neuroscience, 9(OCT). https://doi.org/10.3389/FNAGI.2017.00329/PDF
Sihombing, D. P. S., Yulida, R., & Rosnita, R. (2024). Karakteristik dan Pelaksanaan Peremajaan Kelapa Sawit oleh Pekebun di Kampung Sialang Sakti Kecamatan Dayun Kabupaten Siak. JURNAL TRITON, 15(1), 120–130. https://doi.org/10.47687/JT.V15I1.612
Wahyuni, S., Nasution, A. D., & Hermansyah, H. (2023). Optimization Of Data Mining In Predicting Tourist Visits At The Deli Serdang Museum. International Conference on Sciences Development and Technology, 3(1), 115–120. https://jurnal.uinsu.ac.id/index.php/icosdtech/article/view/18696
Xu, Z., Guo, K., Chu, W., Lou, J., & Chen, C. (2022a). Performance of Machine Learning Algorithms for Predicting Adverse Outcomes in Community-Acquired Pneumonia. Frontiers in Bioengineering and Biotechnology, 10. https://doi.org/10.3389/FBIOE.2022.903426/PDF
Xu, Z., Guo, K., Chu, W., Lou, J., & Chen, C. (2022b). Performance of Machine Learning Algorithms for Predicting Adverse Outcomes in Community-Acquired Pneumonia. Frontiers in Bioengineering and Biotechnology, 10. https://doi.org/10.3389/FBIOE.2022.903426/PDF
Zulfiakhoir, F. E., Umaidah, Y., & Purwantoro, P. (2022). Pemetaan Daerah Produksi Perkebunan Kelapa Sawit Pada Provinsi Indonesia Menggunakan Algoritma K-medoids. Jurnal Ilmiah Wahana Pendidikan, 8(16), 195–208. https://doi.org/10.5281/ZENODO.7067527
Published
2025-06-01
How to Cite
Syahputra, I., Muhammad Iqbal, & Sri Wahyuni. (2025). ANALYSIS OF DATA MINING METHODS IN PALM OIL SALES PREDICTION USING RANDOM FOREST AND XGBOOST (CASE STUDY: PT GEKA ADHI RAKSA). Jurnal Multimedia Dan Teknologi Informasi (Jatilima), 7(02), 68-79. https://doi.org/10.54209/jatilima.v7i02.1346
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Articles
Copyright (c) 2025 Irwan Syahputra, Muhammad Iqbal, Sri Wahyuni
This work is licensed under a Creative Commons Attribution 4.0 International License.
