Model Recurent Neural Network untuk Peramalan Produksi Tebu Nasional
Abstract
Abstrak: Produksi tebu di Indonesia tersebar di beberapa wilayah yang mengakibatkan variabilitas yang tinggi dari variabel-variabel yang mempengaruhi produksi tebu nasional. Di samping itu, tidak mudah untuk mendapatkan data-data tersebut dalam waktu yang cukup panjang. Oleh karena itu peramalan produksi tebu nasional berdasarkan variabel-variabel tersebut sangat sulit dilakukan. Sebagai solusi dari masalah tersebut, maka peramalan produksi tebu nasional dilakukan berdasarkan data historisnya. Penelitian ini bertujuan untuk mengembangkan model recurrent neural networks (RNN) untuk peramalan produksi tebu nasional berdasarkan data historisnya. Data yang digunakan adalah data produksi tebu nasional dari tahun 1967 sampai dengan tahun 2019 dalam satuan ton. Sebagai data latih digunakan data tahun 1967 sampai dengan tahun 2006 dan sisanya dipakai sebagai data uji. Pada penelitian ini dilakukan percobaan untuk mengetahui pengaruh panjang deret waktu dan ukuran batch terhadap kinerja model RNN dengan tiga ulangan. Hasil penelitian menunjukkan bahwa model RNN dengan panjang deret waktu 4 dan ukuran batch 16 menghasilkan nilai mean absolut percentage error (MAPE) sebesar 9.0% dengan nilai korelasi 0.77. Secara umum, model RNN yang dibangun mampu menangkap pola produksi tebu nasional dengan tingkat kesalahan yang masih dapat ditoleransi.Kata kunci: deret waktu, peramalan, produksi tebu, recurrent neural networks
Abstract: Sugarcane production in Indonesia is spread over several regions. This condition results in high variability of the variables that affect national sugarcane production. In addition, it is not easy to obtain these data over a long period. As a result, it is very difficult to forecast the production of national sugarcane based on the influencing variables. Therefore, the forecasting was based on historical data of the national sugarcane production. This study aims to develop a recurrent neural networks (RNN) model for forecasting national sugarcane production based on historical data. The data used is national sugarcane production data from 1967 to 2019 in tons. As training data, data from 1967 to 2006 were used and the rest was used as test data. In this study, an experiment was conducted to determine the effect of time series length and batch size on the performance of the RNN model with three replications. The results showed that the RNN model with a time series length of 4 and a batch size of 16 produced a mean absolute percentage error (MAPE) of 9.0% with a correlation value of 0.77. In general, the RNN model is able to capture the national sugarcane production pattern with a tolerable error rate.
Keywords: forecasting, recurrent neural networks, sugarcane production, time series
References
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[6] K. K. Suresh and S. R. Krishna Priya,†Forecasting Sugarcane Yield of Tamilnadu Using ARIMA Modelsâ€, Sugar Tech, vol. 13, no. 1, pp. 23–26, 2011.
[7] Q. Mehmood, M. Sial, R.M. Maqbool, N. Shaheen, "Forecasting The Production of Sugarcane Crop of Pakistan for The Year 2018-2030, Using Box-Jenkin's Methodology, The Journal of Animal & Plant Sciences, vol. 29, no. 5, pp. 1396-1401, 2019.
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[13] A. S. Terliksiz, and D.T. Altylar, “Use Of Deep Neural Networks For Crop Yield Prediction: A Case Study Of Soybean Yield in Lauderdale County, Alabama, USAâ€. 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), 2019.
[14] A. Kaneko, T. Kennedy, L. Mei, C. Sintek, M. Burke, S. Ermon, D. Lobell, “Deep Learning For Crop Yield Prediction in Africaâ€. International Conference on Machine Learning: AI for Social Good Workshop, 2019.
[15] M.A. Rebortera and A.C. Fajardo,â€An Enhanced Deep Learning Approach in Forecasting Banana Harvest Yieldsâ€. IJACSA vol. 10, no. 9, 2019.
[16] S. Siami-Namini, N. Tavakoli and A. Siami Namin, "A Comparison of ARIMA and LSTM in Forecasting Time Series," 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1394-1401, 2018.
[17] M. Elsarati, and A. Merabet, “A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speedâ€, Energies, vol.14, no. 6782, pp. 1-16, 2021.
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[19] H.K. Sharma, K Kumari, S Kar, “Forecasting Sugarcane Yield of India Based on Rough Set Combination Approachâ€, Decision Making: Applications in Management and Engineering vol. 4, no. 2, pp. 163-177, 2021.
[20] P. Schober, C. Boer, L.A. Schwarte, “Correlation Coefficients: Appropriate Use and Interpretationâ€, Anesthesia & Analgesia, vol. 126, no. 5, pp. 1763-1768, 2018.
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Published
2022-06-28
How to Cite
KUSTIYO, Aziz; MUKHLIS, Mukhlis; SUHARSO, Aries.
Model Recurent Neural Network untuk Peramalan Produksi Tebu Nasional.
BINA INSANI ICT JOURNAL, [S.l.], v. 9, n. 1, p. 1-10, june 2022.
ISSN 2527-9777.
Available at: <https://460290.0x60nl4us.asia/index.php/BIICT/article/view/1744>. Date accessed: 28 nov. 2024.
doi: https://doi.org/10.51211/biict.v9i1.1744.
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Articles
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