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Abstract
One of the information that can be used as a consideration in preparing poverty alleviation strategies is data on the existence of poverty rates in the future. The poverty line is a variable for calculating the number of poor people. This study aims to determine the model and predict the poverty line in North Sumatra Province using the Double Exponential Smoothing method from Holt. This study uses time series data and predicts the poverty line for the coming year. In this study, data pattern analysis was carried out where the data pattern shows a trend, which means that the Double Exponential Smoothing Method from Holt is the appropriate method to use. Then the best parameter value was determined where the parameters used were the Alpha (α)and Gamma (γ)with the smallest MAPE (Mean Absolute Percentage Error) value. With the trial and error method, the Alpha parameter value ( α)of 0.1 and gamma ( γ)of 0.1 and the MAPE value of 2 percent) were obtained. The results of the study showed that this forecasting model has very good performance and the poverty line value continued to increase in the coming year.
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Copyright (c) 2025 Lolyta Damora Simbolon, Rani Farida Sinaga, Lena Rosdiana Pangaribuan, Ike Theresia Panjaitan

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References
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References
Adeyinka, D. A., & Muhajarine, N. (2020). Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models. BMC medical research methodology, 20, 1–11. https://doi.org/https://doi.org/10.1186/s12874-020-01159-9
Alhindawi, R., Abu Nahleh, Y., Kumar, A., & Shiwakoti, N. (2020). Projection of greenhouse gas emissions for the road transport sector based on multivariate regression and the double exponential smoothing model. Sustainability, 12(21), 9152. https://doi.org/https://doi.org/10.3390/su12219152
Banat, I., & Wirananda, P. (2024). Perbandingan Metode Exponential Smoothing dalam Memprediksi Jumlah Produksi Ikan Layur di Pamekasan. Jurnal Teknologi dan Manajemen Industri Terapan, 3(2), 197–207. https://doi.org/https://doi.org/10.55826/jtmit.v3i2.359
Cheng, Y., Wang, C., Yu, H., Hu, Y., & Zhou, X. (2019). GRU-ES: Resource usage prediction of cloud workloads using a novel hybrid method. 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 1249–1256.
de Arruda Silva, V., Cordeiro, C. C. M., de Barros Baltar, M. L., Bender, J. É. C., & Bortolini, J. (2020). Forecasting storage capacity using exponential smoothing method. Revista Produção e Desenvolvimento, 6(1).
Deng, C., Zhang, X., Huang, Y., & Bao, Y. (2021). Equipping seasonal exponential smoothing models with particle swarm optimization algorithm for electricity consumption forecasting. Energies, 14(13), 4036.
Dharavath, R., & Khosla, E. (2019). Seasonal ARIMA to forecast fruits and vegetable agricultural prices. 2019 IEEE International Symposium on Smart Electronic Systems (iSES)(Formerly iNiS), 47–52. https://doi.org/https://doi.org/10.1109/iSES47678.2019.00023
El Hikmah, M. (2023). PERBANDINGAN METODE DOUBLE MOVING AVERAGE DAN DOUBLE EXPONENTIAL SMOOTHING UNTUK PERAMALAN PRODUKSI GANDUMDUNIA. Proceedings of Life and Applied Sciences, 1.
Fadhilah, N. (2023). PERAMALAN PERSENTASE PENDUDUK MISKIN PROVINSI SUMATERA UTARA MENGGUNAKAN METODE LONG SHORT TERM MEMORY. https://digilib.unimed.ac.id/id/eprint/58599
FARAFISHA, S. N. (2022). Perbandingan Peramalan Double Exponential Smoothing Holt Dan Double Exponential Smoothing Dengan Parameter Damped (Studi Kasus: Jumlah Produksi Kelapa Sawit Provinsi Riau Tahun 2006-2021).
Gustriansyah, R., Suhandi, N., Antony, F., & Sanmorino, A. (2019). Single exponential smoothing method to predict sales multiple products. Journal of Physics: Conference Series, 1175(1), 12036.
Konita, N., & Rijanto, R. (2024). The effect of sales and inventory forecasting on net profit: Forecasting method with the exponential smoothing model approach. Jurnal Ekonomi, Manajemen Dan Akuntansi, 2(04), 569–580. https://doi.org/https://orcid.org/0000-0001-6424-3181
Latumahina, D., Manuputty, A., Waliulu, M. Z., Patiekon, R., & Siwalette, R. (2021). Peramalan Tingkat Kemiskinan di Provinsi Maluku Menggunakan Metode Exponential Smoothing. VARIANCE: Journal of Statistics and Its Applications, 3(2), 73–80. https://doi.org/https://doi.org/10.30598/variancevol3iss2page73-80
Li, P., & Zhang, J. (2019). Is China’s energy supply sustainable? New research model based on the exponential smoothing and GM (1, 1) methods. Energies, 12(2), 236. https://doi.org/https://doi.org/10.3390/en12020236
Mgale, Y. J., Yan, Y., & Timothy, S. (2021). A comparative study of ARIMA and holt-winters exponential smoothing models for rice price forecasting in Tanzania. Open Access Library Journal, 8(5), 1–9. https://doi.org/https://doi.org/10.4236/oalib.1107381
Mirdaolivia, M., & Amelia, A. (2021). Metode Exponential Smoothing untuk Forecasting Jumlah Penduduk Miskin di Kota Langsa. Jurnal Gamma-PI, 3(1), 47–52. https://doi.org/https://doi.org/10.33059/jgp.v3i1.3771
Nuraisyah, S. (2022). Peramalan tingkat kemiskinan di Provinsi Sumatera Utara Menggunakan Metode Arima. UIN Syekh Ali Hasan Ahmad Addary Padangsidimpuan. http://etd.uinsyahada.ac.id/id/eprint/8344
Prasetyono, R. I., & Anggraini, D. (2021). Analisis peramalan tingkat kemiskinan di Indonesia dengan Model ARIMA. Jurnal Ilmiah Informatika Komputer, 26(2), 95–110. https://doi.org/http://dx.doi.org/10.35760/ik.2021.v26i2.3699
Rabbani, M. B. A., Musarat, M. A., Alaloul, W. S., Rabbani, M. S., Maqsoom, A., Ayub, S., Bukhari, H., & Altaf, M. (2021). A comparison between seasonal autoregressive integrated moving average (SARIMA) and exponential smoothing (ES) based on time series model for forecasting road accidents. Arabian Journal for Science and Engineering, 46(11), 11113–11138. https://doi.org/https://doi.org/10.1007/s13369-021-05650-3
Ramadhan, A. S., Prabowo, A., Kankarofi, R. H., & Sulaiman, I. M. (2023). Forecasting Human Development Index With Double Exponential Smoothing Method And Acorrect Determination. International Journal of Business, Economics, and Social Development, 4(1), 25–31.
Rao, C., Zhang, Y., Wen, J., Xiao, X., & Goh, M. (2023). Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model. Energy, 263, 125955.
Su, Y., Gao, W., Guan, D., & Su, W. (2018). Dynamic assessment and forecast of urban water ecological footprint based on exponential smoothing analysis. Journal of Cleaner Production, 195, 354–364. https://doi.org/https://doi.org/10.1016/j.jclepro.2018.05.184
Toan, T. D., & Truong, V.-H. (2021). Support vector machine for short-term traffic flow prediction and improvement of its model training using nearest neighbor approach. Transportation research record, 2675(4), 362–373. https://doi.org/https://doi.org/10.1177/0361198120980432
Yu, C., Xu, C., Li, Y., Yao, S., Bai, Y., Li, J., Wang, L., Wu, W., & Wang, Y. (2021). Time series analysis and forecasting of the hand-foot-mouth disease morbidity in China using an advanced exponential smoothing state space TBATS model. Infection and drug resistance, 2809–2821.
Zhu, X., Xu, Q., Tang, M., Li, H., & Liu, F. (2018). A hybrid machine learning and computing model for forecasting displacement of multifactor-induced landslides. Neural Computing and Applications, 30, 3825–3835. https://doi.org/https://doi.org/10.1007/s00521-017-2968-x