International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 13, No. 3, 8 Jun. 2021
Cover page and Table of Contents: PDF (size: 510KB)
Rainfall Forecasting to Recommend Crops Varieties Using Moving Average and Naive Bayes Methods
Full Text (PDF, 510KB), PP.23-33
Views: 0 Downloads: 0
Author(s)
Muhammad Resa Arif Yudianto
1,*
Tinuk Agustin
1
Ronaldus Morgan James
1
Firstyani Imannisa Rahma
1
Arham Rahim
1
Ema Utami
1
Index Terms
Rainfall forecasting, Naive Bayes, Moving Average, Crops, Prediction.
Abstract
Indonesia has been known as an agrarian country because of its fertile soil and is very suitable for agricultural land, including rice. Yogyakarta is one of the most significant granary regions in Indonesia, especially in the Sleman region. However, one of the main challenges in rice planting in recent years is the erratic rainfall patterns caused by climate anomalies due to the El Nino and La Nina phenomena. As a result of this phenomenon, farmers have difficulty determining planting time and harvest time and planting other plants. Therefore, we make rainfall predictions to recommend planting varieties with Moving Average and Naive Bayes Methods in Sleman District. The results showed that moving averages well use in predicting rainfall. From these results, we can estimate that in 2020 rice production will below. That can saw from the calculation of the probability of naive Bayes on rice plants being low at 0.999 and 0.923. So that the recommended intercrops planted in 2020 are corn and peanuts. We also find that rainfall prediction with Moving Average using data from several previous years in the same month is more accurate than using data from four past months or periods.
Cite This Paper
Muhammad Resa Arif Yudianto, Tinuk Agustin, Ronaldus Morgan James, Firstyani Imannisa Rahma, Arham Rahim, Ema Utami, " Rainfall Forecasting to Recommend Crops Varieties Using Moving Average and Naive Bayes Methods", International Journal of Modern Education and Computer Science(IJMECS), Vol.13, No.3, pp. 23-33, 2021.DOI: 10.5815/ijmecs.2021.03.03
Reference
[1] Data on the Amount of Production, Export and Import are quoted from the FAO (Food and Agriculture Organization) Publication of the United Nations, Rice Market Monitor, Volume XVIII Issue No. 2, July 2015.
[2] Prasetyo, Y., Nabilah, F. 2017. "Pattern Analysis of El Nino and La Nina Phenomenon Based on Sea Surface Temperature (SST) and Rainfall Intensity using Oceanic Nino Index (ONI) in West Java Area". Conference Series Earth and Environmental Science 98(1):012041
[3] Geetha,G., and Selvaraj,R.S. 2011. "Prediction of monthly rainfall in Chennai using Back Propagation Neural Network model". Int. J. of Eng. Sci. and Technology, vol. 3, no. 1, pp. 211-213
[4] Darji, M.P., Dabhi,V.K., Prajapati, H.B., "Rainfall Forecasting Using Neural Network: A Survey". Conference: Computer Engineering and Applications (ICACEA), International Conference on Advances in, At Ghaziabad, India, 10.1109/ICACEA.2015.7164782
[5] Asfawa, A. Simaneb, B. Hassenc, A. Bantiderd, A. 2017. "Variability and time series trend analysis of rainfall and temperature in north central Ethiopia: A case study in Woleka sub-basin". Weather and Climate Extremes. Volume 19. Elsevier
[6] Seo, S. N., Mendelsohn, R., Dinar, A., Hassan, R., & Kurukulasuriya, P. (2009). "A ricardian analysis of the distribution of climate change impacts on agriculture across agro-ecological zones in Africa". Environmental and Resource Economics, 43(3), 313–332.
[7] Nelson, G. C., Rosegrant, M. W., Palazzo, A., Gray, I., Ingersoll, C., Robertson, R., et al. (2010). Food security, farming, and climate change to 2050: Scenarios, Results, Policy Options (p 155). Washington, DC: IFPRI.
[8] Benin, S., Wood, S., & Nin-Pratt, A. (2016). "Introduction In Agricultural productivity in Africa: Trends, patterns, and determinants". In S. Benin (Ed.), Chapter 1. (pp. 1–23). Washington, DC: International Food Policy Research Institute (IFPRI)
[9] Khotimah, N. (2019). Pranata mangsa and the sustainability of agricultural land resources management in Imogiri sub-district of Bantul regency. IOP Conference Series: Earth and Environmental Science, 338, 1–8.
[10] Lana, M.A., Vasconcelos, A.C.F., Gornott, C. et al. "Is dry soil planting an adaptation strategy for maize cultivation in semi-arid Tanzania?". Food Sec. 10, 897–910 (2018) doi:10.1007/s12571-017-0742-7
[11] Hari, Y., & Dewi, L. P. (2018). "Forecasting system approach for stock trading with relative strength index and moving average indicator". Journal of Telecommunication, Electronic and Computer Engineering, 10(2–3), 25–29.
[12] Kartikasari, M. D., & Prayogi, A. R. (2018). "Demand forecasting of electricity in Indonesia with limited historical data". Journal of Physics: Conference Series, 974(1).
[13] Maricar, M. A., Widiadnyana, P., & Arta Wijaya, I. W. (2017). "Analysis of Data Mining for Forecasting Total Goods Delivery with Moving Average Method". International Journal of Engineering and Emerging Technology, 2(1), 7.
[14] S. Kaur and S. Kalsi. 2019. "Analysis of Wheat Production using Naïve Bayes Classifier". Int. J. Comput. Appl., vol. 178, no. 14, pp. 38–41.
[15] P. Tsangaratos and I. Ilia. 2016. "Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size". Catena, vol. 145, pp. 164–179.
[16] H. Huo and L. Yang. 2018. "Prediction of conotoxin superfamilies by the Naive Bayes classifier". Proc. - 2017 10th Int. Congr. Image Signal Process. Biomed. Eng. Informatics, CISP-BMEI 2017, vol. 2018-January, pp. 1–5.
[17] D. Singh and B. Singh, "Investigating the impact of data normalization on classification performance," Appl. Soft Comput. J., no. xxxx, p. 105524, 2019.
[18] Geoff Dougherty. 2012. "Pattern Recognition and Classification: an Introduction". Springer Science & Business Media.
[19] Salvador García, Julián Luengo, Francisco Herrera. 2015. “Data Preprocessing in Data Mining”. Springer.
[20] S. A. Alasadi and W. S. Bhaya. 2017. "Review of data preprocessing techniques in data mining". J. Eng. Appl. Sci., vol. 12, no. 16, pp 4102–4107.
[21] Evans, M. K. 2001. "Practical Business Forecasting". Wiley-Blackwell.
[22] Molugaram, K., Rao, G. S., Shah, A., & Davergave, N. 2017. "Statistical Techniques for Transportation Engineering". First Edition. Butterworth-Heinemann.
[23] Storm, K. 2019. "Industrial Process Plant Construction Estimating and Man-Hour Analysis". First Edition. Gulf Professional Publishing.
[24] Johnston, F. R., Boyland, J. E., Meadows, M., & Shale, E. 1999. "Some Properties of A Simple Moving Average When Applied to Forecasting A Time Series". Journal of the Operational Research Society, 50(12), 1267–1271.
[25] Lehmann, E. L.; Casella, George. 1998. "Theory of Point Estimation". Second Edition. New York: Springer.
[26] Zhao, Y., & Cen, Y. 2013. "Data Mining Applications with R". Academic Press.
[27] Grimnes, S., & Martinsen, Ø. G. 2015. "Bioimpedance and Bioelectricity Basics". Third Edition. Academic Press.
[28] Mao, W., & Wang, F.-Y. 2012. "New Advances in Intelligence and Security Informatics". Academic Press.
[29] Nisbet, R., Miner, G., & Yale, K. 2018. "Handbook of Statistical Analysis and Data Mining Applications". Second Edition. Academic Press.
[30] Stern, M., Beck, J., & Woolf, B. P. 1999. "Naive Bayes Classifiers for User Modeling". Center for Knowledge Communication, Computer Science Department, University of Massachusetts.