Implementation of fuzzy mamdani method in predicting cayenne chili prices in Tegal Regency
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Published:
Jun 30, 2024
Keywords:
Cayenne pepper, Fuzzy mamdani, Price prediction, Price volatility, Tegal regency
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Abstract
This study investigates the application of Fuzzy Mamdani's method in predicting the price of cayenne pepper in Tegal Regency, one of the important agricultural commodities that has significant economic implications. This study aims to develop an accurate and reliable cayenne pepper price prediction model in Tegal Regency using the fuzzy Mamdani method. Research methods include collecting historical data on cayenne pepper prices, cayenne pepper production, and rainfall, as well as the implementation of the Mamdani fuzzy method consisting of fuzzification, inference, and defuzzification using Python programming language computing. The results showed that the fuzzy Mamdani method can predict the price of cayenne pepper with a good level of accuracy, with an average prediction error of 16.653285% and a prediction correctness rate of 83.346715%. This finding has implications for improving production planning capabilities and marketing strategies for cayenne pepper farmers in Tegal District, as well as contributing to the scientific literature in the application of fuzzy methods in agriculture
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Surorejo, S., Mutaqin, A. F., Kurniawan, R. D. ., & Gunawan, G. (2024). Implementation of fuzzy mamdani method in predicting cayenne chili prices in Tegal Regency. Journal of Intelligent Decision Support System (IDSS), 7(2), 137-145. https://doi.org/10.35335/idss.v7i2.231
References
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Zou, Z., & Zou, X. (2021). Geographical and ecological differences in pepper cultivation and consumption in China. Frontiers in Nutrition, 8, 718517.
Achour, Y., Ouammi, A., & Zejli, D. (2021). Technological progresses in modern sustainable greenhouses cultivation as the path towards precision agriculture. Renewable and Sustainable Energy Reviews, 147, 111251. https://doi.org/10.1016/j.rser.2021.111251
Akhter, R., & Sofi, S. A. (2022). Precision agriculture using IoT data analytics and machine learning. Journal of King Saud University-Computer and Information Sciences, 34(8), 5602–5618. https://doi.org/10.1016/j.jksuci.2021.05.013
Belhadi, A., Mani, V., Kamble, S. S., Khan, S. A. R., & Verma, S. (2024). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation. Annals of Operations Research, 333(2), 627–652. https://doi.org/10.1007/s10479-021-03956-x
Benyam, A. A., Soma, T., & Fraser, E. (2021). Digital agricultural technologies for food loss and waste prevention and reduction: Global trends, adoption opportunities and barriers. Journal of Cleaner Production, 323, 129099. https://doi.org/10.1016/j.jclepro.2021.129099
Benyezza, H., Bouhedda, M., & Rebouh, S. (2021). Zoning irrigation smart system based on fuzzy control technology and IoT for water and energy saving. Journal of Cleaner Production, 302, 127001. https://doi.org/10.1016/j.jclepro.2021.127001
Dewi, C. (2023). Diversity of Indonesian offal-based dishes.
Duffy, C., Toth, G. G., Hagan, R. P. O., McKeown, P. C., Rahman, S. A., Widyaningsih, Y., Sunderland, T. C. H., & Spillane, C. (2021). Agroforestry contributions to smallholder farmer food security in Indonesia. Agroforestry Systems, 95(6), 1109–1124.
Fan, D., Sun, H., Yao, J., Zhang, K., Yan, X., & Sun, Z. (2021). Well production forecasting based on ARIMA-LSTM model considering manual operations. Energy, 220, 119708. https://doi.org/10.1016/j.energy.2020.119708
Haqqoni, M. G. Al, & Pramana, S. (2022). Implementation of marketplace data in the production of Consumer Price Index in Indonesia. Data Science, 5(2), 79–95. https://doi.org/10.3233/DS-210037
Ingram, J., Gaskell, P., Mills, J., & Dwyer, J. (2020). How do we enact co-innovation with stakeholders in agricultural research projects? Managing the complex interplay between contextual and facilitation processes. Journal of Rural Studies, 78, 65–77. https://doi.org/10.1016/j.jrurstud.2020.06.003
Jamroen, C., Komkum, P., Fongkerd, C., & Krongpha, W. (2020). An intelligent irrigation scheduling system using low-cost wireless sensor network toward sustainable and precision agriculture. IEEE Access, 8, 172756–172769. https://doi.org/10.1109/ACCESS.2020.3025590
Khoury, C. K., Brush, S., Costich, D. E., Curry, H. A., De Haan, S., Engels, J. M. M., Guarino, L., Hoban, S., Mercer, K. L., & Miller, A. J. (2022). Crop genetic erosion: understanding and responding to loss of crop diversity. New Phytologist, 233(1), 84–118. https://doi.org/10.1111/nph.17733
Küçüktopçu, E., Cemek, B., & Simsek, H. (2023). Application of Mamdani Fuzzy Inference System in Poultry Weight Estimation. Animals, 13(15), 2471. https://doi.org/doi.org/10.3390/ani13152471
Lezoche, M., Hernandez, J. E., Díaz, M. del M. E. A., Panetto, H., & Kacprzyk, J. (2020). Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture. Computers in Industry, 117, 103187. https://doi.org/10.1016/j.compind.2020.103187
Liu, W., Shao, X.-F., Wu, C.-H., & Qiao, P. (2021). A systematic literature review on applications of information and communication technologies and blockchain technologies for precision agriculture development. Journal of Cleaner Production, 298, 126763. https://doi.org/10.1016/j.jclepro.2021.126763
Lukas, A., Kairupan, A. N., Hendriadi, A., Arianto, A., Manalu, L. P., Sumarno, L., Munarso, J., Hadipernata, M., Elmatsani, H. M., & Benyamin, B. O. (2023). Fresh Chili Agribusiness: Opportunities and Problems in Indonesia. https://doi.org/10.5772/intechopen.112786
Machala, M. L., Tan, F. L., Poletayev, A., Khan, M. I., & Benson, S. M. (2022). Overcoming barriers to solar dryer adoption and the promise of multi-seasonal use in India. Energy for Sustainable Development, 68, 18–28. https://doi.org/10.1016/j.esd.2022.02.001
Muflikh, Y. N., Smith, C., Brown, C., & Aziz, A. A. (2021). Analysing price volatility in agricultural value chains using systems thinking: A case study of the Indonesian chilli value chain. Agricultural Systems, 192, 103179. https://doi.org/10.1016/j.agsy.2021.103179
Poldrack, R. A., Huckins, G., & Varoquaux, G. (2020). Establishment of best practices for evidence for prediction: a review. JAMA Psychiatry, 77(5), 534–540. https://doi.org/10.1001/jamapsychiatry.2019.3671
Puji, A. E., Titik, E., & Kusmiyati, F. (2022). Analysis of the Balance of Supply and Demand for Curly Red Chili in Magelang Regency, Central Java Province, Indonesia. Russian Journal of Agricultural and Socio-Economic Sciences, 121(1), 94–104. https://doi.org/10.18551
Song, C., & Dong, H. (2021). Application of Intelligent Recommendation for Agricultural Information: A Systematic Literature Review. IEEE Access, 9, 153616–153632. https://doi.org/10.1109/ACCESS.2021.3127201
Steenkamp, J., Cilliers, E. J., Cilliers, S. S., & Lategan, L. (2021). Food for thought: Addressing urban food security risks through urban agriculture. Sustainability, 13(3), 1267. https://doi.org/10.3390/su13031267
Visentin, C., da Silva Trentin, A. W., Braun, A. B., & Thomé, A. (2020). Life cycle sustainability assessment: A systematic literature review through the application perspective, indicators, and methodologies. Journal of Cleaner Production, 270, 122509. https://doi.org/10.1016/j.jclepro.2020.122509
Zhai, Z., Martínez, J. F., Beltran, V., & Martínez, N. L. (2020). Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 170, 105256. https://doi.org/10.1016/j.compag.2020.105256
Zou, Z., & Zou, X. (2021). Geographical and ecological differences in pepper cultivation and consumption in China. Frontiers in Nutrition, 8, 718517.
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