Perbandingan Metode Holt-Winter dan SARIMA dalam Peramalan Jumlah Peserta Pelatihan PPSDM Migas
Abstract
Planning the number of training participants is an important aspect in supporting the effectiveness of training program implementation at PPSDM Migas. Inaccuracy in estimating the number of participants can impact training capacity planning, resource allocation, and performance target achievement. This study aims to compare the performance of the Triple Exponential Smoothing (Holt-Winter) method and the Seasonal Autoregressive Integrated Moving Average (SARIMA) in forecasting the number of training participants resulting from the collaboration at PPSDM Migas. The data used are monthly data on the number of training participants from the collaboration period January 2020 to December 2024. Modeling was conducted using the HoltWinter and SARIMA methods, then evaluated using the Mean Absolute Percentage Error (MAPE) to determine the model with the best accuracy. The research results show that both methods are capable of modeling seasonal patterns in historical data, but the Holt-Winter method produced a MAPE value of 34.19%, lower than the SARIMA method at 41.58%. These results indicate that the Holt-Winter method has a better accuracy level in forecasting the number of training participants from the collaboration at PPSDM Migas. Therefore, the Holt-Winter method is recommended as an alternative to support planning the number of training participants, training capacity planning, resource allocation, and more effective decision-making at PPSDM Migas.
References
Arumugam, V. and Natarajan, V. (2023). Time Series Modeling and Forecasting Using Autoregressive Integrated Moving Average and Seasonal Autoregressive Integrated Moving Average Models: Instrumentation, Mesures, Métrologies. Instrumentation, Mesures, Métrologies, 22(4), 161–168. https://doi.org/10.18280/i2m.220404.
Atkinson, A.C., Riani, M. and Corbellini, A. (2021). The Box–Cox Transformation: Review and Extensions. Statistical Science, 36(2). https://doi.org/10.1214/20-sts778.
Box, G.E.P., Jenkins, G.M., Reinsel, G.C. and Ljung, G.M. (2015). Time series analysis : forecasting and control. Hoboken, New Jersey: John Wiley & Sons.
Boyko, N. (2023). Application of Adaptive and Multiplicative Models for Analysis and Forecasting of Time Series. International Journal of Computing, 202–211. https://doi.org/10.47839/ijc.22.2.3089.
Chhabra, G. (2023). Comparison of Imputation Methods for Univariate Time Series. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 286–292. https://doi.org/10.17762/ijritcc.v11i2s.6161.
Fadhilahrizka, N.D., Thania, D.K. and Kurnia, D.R. (2026). Analisis Performa Holt-Winter Dan SARIMA Dalam Peramalan Multivariabel Iklim Bulanan Di Wilayah Pesisir Kota Semarang. Rabit: Jurnal Teknologi Dan Sistem Informasi Univrab, 11(1), 1878–1893. 10.36341/rabit.v11i1.7487.
Falcão, D., Reis, F., Farinha, J., Lavado, N. and Mendes, M. (2024). Fault Detection in Industrial Equipment through Analysis of Time Series Stationarity. Algorithms, 17(10), p.455. https://doi.org/10.3390/a17100455.
Hedar, Y., Emilzoli, M., Priandani, A.P. and Abdullah, M.L. (2025). Strategy to Improve Training Quality in PPSDM Migas: Approach to Educational Aspects, Facilities, and Services. FINGER : Jurnal Ilmiah Teknologi Pendidikan, 4(2), 57–72. https://doi.org/10.58723/finger.v4i2.385.
Holt, C.C. (1957). Forecasting Seasonals and Trends by Exponentially Weighted Moving Averages. ONR Memorandum, 52. Carnegie Institute of Technology, Pittsburgh.
Holt, C.C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5–10. https://doi.org/10.1016/j.ijforecast.2003.09.015.
Kim, S. and Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32(3), 669–679. https://doi.org/10.1016/j.ijforecast.2015.12.003.
Kusuma, Maulana. (2026). Perbandingan Metode Holt-Winters Dan Seasonal ARIMA Dalam Memprediksi Produksi Beras Di Indonesia. Jurnal Agribisnis, 26(2), 31–45. https://doi.org/10.31849/agr.v26i02.17672.
Nur, M, Eis, N., Alimul, A. and Sari (2024). Peramalan Jumlah Penumpang Domestik Pada Bandar Udara Sultan Syarif Kasim II Dengan Menggunakan Metode Winter’s Exponential Smoothing. Jurnal Teknologi dan Manajemen Industri Terapan, 3(I), 57–66. https://doi.org/10.55826/tmit.v3ii.302.
Nurhamidah, N., Nusyirwan, N. and Faisol, A. (2020). Forecasting Seasonal Time Series Data Using the Holt-Winters Exponential Smoothing Method of Additive Models. Jurnal Matematika Integratif, 16(2), p.151. https://doi.org/10.24198/jmi.v16.n2.29293.151-157.
Okonkwo, F., Eleogu, T., Adijat Elufioye, O., Louis Eyo-Udo, N. and Daraojimba, R. (2024). Materials & Corrosion Engineering Management (MACEM) Navigating HR Challenges in the Oil and Gas Sector Amidst Global Energy Transitions. Materials & Corrosion Engineering Management (MACEM), [online] 5(1), 1–10. https://doi.org/10.26480/macem.01.2024.01.10.
Pei, J., Zhu, F. and Li, Q. (2023). Diagnostic checks in time series models based on a new correlation coefficient of residuals. Journal of Applied Statistics, 51(12), 2402–2419. https://doi.org/10.1080/02664763.2023.2297155.
Rahardja, D. (2024). Statistical time-series forecast error and bias assessment using hold-out samples and a single benchmark (data-reduction) scoring metric. Journal of Statistics and Management Systems, 27(3), 569–578. https://doi.org/10.47974/jsms-974.
Septiana, D. (2024). Forecasting Rice Prices with Holt-Winter Exponential Smoothing Model. Hanif Journal of Information Systems, 1(2), 62–67. https://doi.org/10.56211/hanif.v1i2.17.
Sutherland, C., Hare, D., Johnson, P.J., Linden, D.W., Montgomery, R.A. and Droge, E. (2023). Practical advice on variable selection and reporting using Akaike information criterion. Proceedings of The Royal Society B: Biological Sciences, 290(2007). https://doi.org/10.1098/rspb.2023.1261.
Werner, K. and Martin, A. (2023). Between precarity and professionalism: the effect of uncertainty on adult educators’ participation in continuing education. Zeitschrift für Weiterbildungsforschung. https://doi.org/10.1007/s40955-023-00255-9.
Winters, P.R. (1960). Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6(3), 324–342. https://doi.org/10.1287/mnsc.6.3.324.
Zwikael, O. and Gilchrist, A. (2021). Planning to Fail: When Is Project Planning Counterproductive? IEEE Transactions on Engineering Management, 70(1), 1–12. https://doi.org/10.1109/tem.2021.3053585.
