Performance evaluation of single moving average and exponential smoothing in shallot production prediction
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Published:
May 30, 2024
Keywords:
Fluctuation, Production prediction, Shallots, Single exponential smoothing, Single moving average
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Computer Science
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Abstract
Shallots are a strategic commodity that has significant health benefits, including its ability to prevent cancer. The commodity also plays an important role in the agricultural economy, especially in Indonesia, where high demand in domestic and international markets contributes greatly to farmer’s income. However, fluctuations in shallot production often lead to price instability, which has a negative impact not only on consumers but also on the sustainability of farmers' income. This research aims to develop a forecasting model that can assist in more effective planning of shallot production. To achieve this goal, the study tested and compared two forecasting methods: Single Moving Average (SMA) and Single Exponential Smoothing (SES), which are known for their ease of implementation and accuracy in predicting time series data. Using a dataset of shallot production from Brebes Regency over the period 2020-2023, the study found that Single Exponential Smoothing consistently provided more accurate results than Single Moving Average. SES performance is more responsive to recent changes in production data, which is particularly important given the rapid fluctuations that often occur in the agricultural sector. The findings suggest that the application of the SES method in shallot production forecasting can facilitate more informed decision-making in production management and distribution planning, potentially stabilizing market prices and improving farmers' economic conditions
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How to Cite
Santoso, A. A. ., Surorejo, S. ., Kurniawan, R. D. . and Gunawan, G. . (2024) “Performance evaluation of single moving average and exponential smoothing in shallot production prediction”, Jurnal Mantik, 8(1), pp. 714-723. doi: 10.35335/mantik.v8i1.5205.
References
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Dudek, G., Pe?ka, P., & Smyl, S. (2021). A hybrid residual dilated LSTM and exponential smoothing model for midterm electric load forecasting. IEEE Transactions on Neural Networks and Learning Systems, 33(7), 2879–2891.
Fahrizal, H., Fatimah Nur, I., & Lusiana, A. (2021). Aplikasi Pupuk Hayati Mikoriza terhadap Tanaman Bawang Merah (Allium cepa var. aggregatum) pada Latosol Dramaga. Jurnal Ilmu Tanah Dan Lingkungan, 23(2), 59–65. https://doi.org/10.29244/jitl.23.2.61-67
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Miguéis, V. L., Pereira, A., Pereira, J., & Figueira, G. (2022). Reducing fresh fish waste while ensuring availability: Demand forecast using censored data and machine learning. Journal of Cleaner Production, 359, 131852.
Nasus, E., & Mutmainah, R. (2023). Manfaat Bawang Merah Sebagai Antipiretik Pada Penanganan Anak Dengan Hipertermi: Studi Fenomenology. Media Publikasi Penelitian Kebidanan, 6(1), 77–83.
Parmezan, A. R. S., Souza, V. M. A., & Batista, G. E. (2019). Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model. Information Sciences, 484, 302–337.
Purwayoga, V., & Nurkholis, A. (2023). VISUALISASI REKOMENDASI PEMILIHAN JURNAL BIDANG INFORMATIKA DENGAN MENGGUNAKAN R DAN SHINY. Jurnal Teknoinfo, 17(1), 183–190.
Putra, A., & Toba, H. (2020). Pengembangan Gudang Data Pendukung Analisis Tren Penyewaan Peralatan Katering dengan Algoritma Apriori. Journal of Information System and Technology (JOINT), 1(1), 5–14.
Raharja, A. R., Pramudianto, A., & Muchsam, Y. (2024). Penerapan Algoritma Decision Tree dalam Klasifikasi Data “Framingham” Untuk Menunjukkan Risiko Seseorang Terkena Penyakit Jantung dalam 10 Tahun Mendatang. Technologia Journal, 1(1).
Rahman, N., Hermawan, B., Reflis, Sulistyo, B., Marlin, & Barchia, M. F. (2022). Land suitability evaluation of shallot (Allium ascalonicum L.) at irrigated marginal lowland in Bengkulu, Indonesia.
Raup, A., Ridwan, W., Khoeriyah, Y., Supiana, S., & Zaqiah, Q. Y. (2022). Deep Learning dan Penerapannya dalam Pembelajaran. JIIP-Jurnal Ilmiah Ilmu Pendidikan, 5(9), 3258–3267.
Romadhon, M. S., Nugroho, R. T., Saputra, Y., & Paduloh, P. (2024). ANALISIS PERAMALAN PENJUALAN SUSU FORMULA DI TOKO SUSU TY-SYAR DENGAN MENGGUNAKAN METODE MOVING AVERAGE. Jurnal Salome: Multidisipliner Keilmuan, 2(1), 117–125.
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Shi, H., Worden, K., & Cross, E. J. (2019). A cointegration approach for heteroscedastic data based on a time series decomposition: an application to structural health monitoring. Mechanical Systems and Signal Processing, 120, 16–31.
Su, Y., Cui, C., & Qu, H. (2022). Self-attentive moving average for time series prediction. Applied Sciences, 12(7), 3602.
Svetunkov, I., Kourentzes, N., & Ord, J. K. (2022). Complex exponential smoothing. Naval Research Logistics (NRL), 69(8), 1108–1123.
Wainer, J., & Cawley, G. (2021). Nested cross-validation when selecting classifiers is overzealous for most practical applications. Expert Systems with Applications, 182, 115222.
Zhao, K., Wulder, M. A., Hu, T., Bright, R., Wu, Q., Qin, H., Li, Y., Toman, E., Mallick, B., & Zhang, X. (2019). Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm. Remote Sensing of Environment, 232, 111181.
Anggraeni, E. W., Handayati, Y., & Novani, S. (2022). Improving Local Food Systems through the Coordination of Agriculture Supply Chain Actors. Sustainability, 14(6), 3281.
Archontoulis, S. V, Castellano, M. J., Licht, M. A., Nichols, V., Baum, M., Huber, I., Martinez?Feria, R., Puntel, L., Ordóñez, R. A., & Iqbal, J. (2020). Predicting crop yields and soil?plant nitrogen dynamics in the US Corn Belt. Crop Science, 60(2), 721–738.
Astuti, L. T. W., Daryanto, A., Syaukat, Y., & Daryanto, H. K. (2019). Analisis Resiko Produksi Usahatani Bawang Merah pada Musim Kering dan Musim Hujan di Kabupaten Brebes. Jurnal Ekonomi Pertanian Dan Agribisnis, 3(4), 840–852. https://doi.org/10.21776/ub.jepa.2019.003.04.19
Aziza, J. N. A. (2022). Perbandingan Metode Moving Average, Single Exponential Smoothing, dan Double Exponential Smoothing Pada Peramalan Permintaan Tabung Gas LPG PT Petrogas Prima Services. Jurnal Teknologi Dan Manajemen Industri Terapan, 1(I), 35–41.
Chang, Y.-C., Chen, W.-T., Su, S.-H., Jung, C.-R., & Hwang, B.-F. (2022). PM2. 5 exposure and incident attention-deficit/hyperactivity disorder during the prenatal and postnatal periods: A birth cohort study. Environmental Research, 214, 113769.
Chanifah, C., & Suwandi, S. (n.d.). Introduksi Teknologi USAhatani Bawang Merah untuk Meningkatkan Produksi di Kabupaten Demak Jawa Tengah. Jurnal Pengkajian Dan Pengembangan Teknologi Pertanian, 21(2), 85–100.
Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peerj Computer Science, 7, e623.
Davydenko, A., & Fildes, R. (2013). Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts. International Journal of Forecasting, 29(3), 510–522.
De Bruin, S., Brus, D. J., Heuvelink, G. B. M., van Ebbenhorst Tengbergen, T., & Wadoux, A. M.-C. (2022). Dealing with clustered samples for assessing map accuracy by cross-validation. Ecological Informatics, 69, 101665.
Dudek, G., Pe?ka, P., & Smyl, S. (2021). A hybrid residual dilated LSTM and exponential smoothing model for midterm electric load forecasting. IEEE Transactions on Neural Networks and Learning Systems, 33(7), 2879–2891.
Fahrizal, H., Fatimah Nur, I., & Lusiana, A. (2021). Aplikasi Pupuk Hayati Mikoriza terhadap Tanaman Bawang Merah (Allium cepa var. aggregatum) pada Latosol Dramaga. Jurnal Ilmu Tanah Dan Lingkungan, 23(2), 59–65. https://doi.org/10.29244/jitl.23.2.61-67
Hill, C., Du, L., Johnson, M., & McCullough, B. D. (2024). Comparing programming languages for data analytics: Accuracy of estimation in Python and R. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, e1531.
Immanuella, B. D. U., & Tinaprilla, N. (2023). Analisis Efisiensi Tataniaga Bawang Merah Di Kecamtan Wanasari Kabupaten Brebes Jawa Tengah. RISALAH KEBIJAKAN PERTANIAN DAN LINGKUNGAN Rumusan Kajian Strategis Bidang Pertanian Dan Lingkungan, 10(1), 34–46. https://doi.org/10.29244/jkebijakan.v10i1.34845
Koutsandreas, D., Spiliotis, E., Petropoulos, F., & Assimakopoulos, V. (2022). On the selection of forecasting accuracy measures. Journal of the Operational Research Society, 73(5), 937–954.
Kumar, V., Unal, S., Bhagat, S. K., & Tiyasha, T. (2024). A data-driven approach to river discharge forecasting in the Himalayan region: Insights from Aglar and Paligaad rivers. Results in Engineering, 102044.
Liu, L.-W., Ma, X., Wang, Y.-M., Lu, C.-T., & Lin, W.-S. (2021). Using artificial intelligence algorithms to predict rice (Oryza sativa L.) growth rate for precision agriculture. Computers and Electronics in Agriculture, 187, 106286.
Miguéis, V. L., Pereira, A., Pereira, J., & Figueira, G. (2022). Reducing fresh fish waste while ensuring availability: Demand forecast using censored data and machine learning. Journal of Cleaner Production, 359, 131852.
Nasus, E., & Mutmainah, R. (2023). Manfaat Bawang Merah Sebagai Antipiretik Pada Penanganan Anak Dengan Hipertermi: Studi Fenomenology. Media Publikasi Penelitian Kebidanan, 6(1), 77–83.
Parmezan, A. R. S., Souza, V. M. A., & Batista, G. E. (2019). Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model. Information Sciences, 484, 302–337.
Purwayoga, V., & Nurkholis, A. (2023). VISUALISASI REKOMENDASI PEMILIHAN JURNAL BIDANG INFORMATIKA DENGAN MENGGUNAKAN R DAN SHINY. Jurnal Teknoinfo, 17(1), 183–190.
Putra, A., & Toba, H. (2020). Pengembangan Gudang Data Pendukung Analisis Tren Penyewaan Peralatan Katering dengan Algoritma Apriori. Journal of Information System and Technology (JOINT), 1(1), 5–14.
Raharja, A. R., Pramudianto, A., & Muchsam, Y. (2024). Penerapan Algoritma Decision Tree dalam Klasifikasi Data “Framingham” Untuk Menunjukkan Risiko Seseorang Terkena Penyakit Jantung dalam 10 Tahun Mendatang. Technologia Journal, 1(1).
Rahman, N., Hermawan, B., Reflis, Sulistyo, B., Marlin, & Barchia, M. F. (2022). Land suitability evaluation of shallot (Allium ascalonicum L.) at irrigated marginal lowland in Bengkulu, Indonesia.
Raup, A., Ridwan, W., Khoeriyah, Y., Supiana, S., & Zaqiah, Q. Y. (2022). Deep Learning dan Penerapannya dalam Pembelajaran. JIIP-Jurnal Ilmiah Ilmu Pendidikan, 5(9), 3258–3267.
Romadhon, M. S., Nugroho, R. T., Saputra, Y., & Paduloh, P. (2024). ANALISIS PERAMALAN PENJUALAN SUSU FORMULA DI TOKO SUSU TY-SYAR DENGAN MENGGUNAKAN METODE MOVING AVERAGE. Jurnal Salome: Multidisipliner Keilmuan, 2(1), 117–125.
Saharuddin, S., & Prihatmono, M. W. (2022). Pengenalan Dan Pelatihan Dasar Bahasa Pemrograman Python Pada Siswa/I Sma Negeri 3 Makassar. SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan, 6(4), 2233–2237.
Sampebua, M. R., & Suyono, I. J. (2022). E-Commerce Papua Tani sebagai Marketplace Hasil Pertanian Distrik Skanto Kabupaten Keerom Papua. Jurnal Surya Masyarakat, 4(2), 169–174.
Shi, H., Worden, K., & Cross, E. J. (2019). A cointegration approach for heteroscedastic data based on a time series decomposition: an application to structural health monitoring. Mechanical Systems and Signal Processing, 120, 16–31.
Su, Y., Cui, C., & Qu, H. (2022). Self-attentive moving average for time series prediction. Applied Sciences, 12(7), 3602.
Svetunkov, I., Kourentzes, N., & Ord, J. K. (2022). Complex exponential smoothing. Naval Research Logistics (NRL), 69(8), 1108–1123.
Wainer, J., & Cawley, G. (2021). Nested cross-validation when selecting classifiers is overzealous for most practical applications. Expert Systems with Applications, 182, 115222.
Zhao, K., Wulder, M. A., Hu, T., Bright, R., Wu, Q., Qin, H., Li, Y., Toman, E., Mallick, B., & Zhang, X. (2019). Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm. Remote Sensing of Environment, 232, 111181.
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