Prediksi Harga Tandan Buah Segar Kelapa Sawit Menggunakan Fuzzy Mamdani
DOI:
https://doi.org/10.30865/jurikom.v13i2.9707Keywords:
Fuzzy Mamdani, MATLAB, Mean Absolute Percentage Error (MAPE), Fresh Fruit Bunch price prediction, Palm OilAbstract
Fluctuations in the price of Fresh Fruit Bunches (FFB) of palm oil remain a problem in the pricing process because they are influenced by several dynamically changing economic factors. This study aims to apply the Mamdani Fuzzy Logic method to predict FFB prices by considering data uncertainty and relationships between variables that are not always linear. The variables used include the K Index, Crude Palm Oil (CPO) price, CPO ratio (R-CPO), palm kernel price (IS), and palm kernel ratio (R-IS). All variables are processed in a rule-based fuzzy inference system using MATLAB, then validated through a comparison between manual calculations and simulation results. The scientific contribution of this study lies in the application of a FFB price prediction system that integrates several key economic indicators in one Mamdani Fuzzy model, as well as the use of double validation to ensure the consistency of the calculation process. The test results show a Mean Absolute Percentage Error (MAPE) value of 18.7%, which is included in the good category and indicates that the model is able to follow the actual data pattern with an acceptable error rate. The comparison of the manual calculation result of 2499.13 and the MATLAB result of 2570 shows a relatively small difference, thus supporting the consistency of the method used. With its rule-based and easily traceable characteristics, the Fuzzy Mamdani method can be the basis for an interpretive and applicable decision support system to help predict FFB prices in the palm oil plantation sector.
References
[1] S. Zheng and Z. Zhang, “Adaptive tourism forecasting using hybrid artificial intelligence model: a case study of Xi’an international tourist arrivals,” PeerJ Comput. Sci., vol. 9, p. e1573, Nov. 2023, doi: 10.7717/peerj-cs.1573.
[2] S. Mazzuco and N. Keilman, Developments in Demographic Forecasting, vol. 49. Cham: Springer International Publishing, 2020. doi: 10.1007/978-3-030-42472-5.
[3] X. Kong et al., “Deep learning for time series forecasting: a survey,” International Journal of Machine Learning and Cybernetics, vol. 16, no. 7–8, pp. 5079–5112, Aug. 2025, doi: 10.1007/s13042-025-02560-w.
[4] S. Haben, M. Voss, and W. Holderbaum, Core Concepts and Methods in Load Forecasting. Cham: Springer International Publishing, 2023. doi: 10.1007/978-3-031-27852-5.
[5] Y. Roza, Y. Pernando, I. Verdian, E. L. Febrianti, and I. Syafrinal, “Prediksi Penjualan Menggunakan Metode Fuzzy Mamdani Pada PT. XYZ,” JURIKOM (Jurnal Riset Komputer), vol. 9, no. 6, p. 1989, Dec. 2022, doi: 10.30865/jurikom.v9i6.5333.
[6] P. Kibazo, W. J. Nabende, and M. R. Atim, “Design and simulation of an intelligent irrigation system using fuzzy logic,” Discover Electronics, vol. 2, no. 1, p. 5, Jan. 2025, doi: 10.1007/s44291-025-00045-2.
[7] A. Hess-Dunlop, H. Kakani, S. Taylor, D. Louie, J. Eshraghian, and C. Josephson, “Time-series forecasting of microbial fuel cell energy generation using deep learning,” Front. Comput. Sci., vol. 6, Jan. 2025, doi: 10.3389/fcomp.2024.1447745.
[8] D. C. Pandey, G. S. Kushwaha, and S. Kumar, “Mamdani fuzzy rule-based models for psychological research,” SN Appl. Sci., vol. 2, no. 5, p. 913, May 2020, doi: 10.1007/s42452-020-2726-z.
[9] A. Hudon, “A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision support,” Front. Artif. Intell., vol. 8, Jun. 2025, doi: 10.3389/frai.2025.1606250.
[10] R. Vidal-Martínez, J. R. García-Martínez, R. Rojas-Galván, J. M. Álvarez-Alvarado, M. Gozález-Lee, and J. Rodríguez-Reséndiz, “A Review of Mamdani, Takagi–Sugeno, and Type-2 Fuzzy Controllers for MPPT and Power Management in Photovoltaic Systems,” Technologies (Basel)., vol. 13, no. 9, p. 422, Sep. 2025, doi: 10.3390/technologies13090422.
[11] F. Camastra, A. Ciaramella, G. Salvi, S. Sposato, and A. Staiano, “On the interpretability of fuzzy knowledge base systems,” PeerJ Comput. Sci., vol. 10, p. e2558, Dec. 2024, doi: 10.7717/peerj-cs.2558.
[12] L. Tong, J. Fang, X. Wang, and Y. Zhao, “Cattle welfare assessment based on adaptive fuzzy logic and multimodal data fusion,” Front. Vet. Sci., vol. 12, Apr. 2025, doi: 10.3389/fvets.2025.1568715.
[13] F. Bellini, Y. Barzegar, A. Barzegar, S. Marrone, L. Verde, and P. Pisani, “Sustainable Water Quality Evaluation Based on Cohesive Mamdani and Sugeno Fuzzy Inference System in Tivoli (Italy),” Sustainability, vol. 17, no. 2, p. 579, Jan. 2025, doi: 10.3390/su17020579.
