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All Journal Jurnal Tecnoscienza Priviet Social Sciences Journal Horizon: Indonesian Journal of Multidisciplinary
Dwi Junianto
Universitas Bhinneka PGRI
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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A comparative analysis of the accuracy of forecasting methods in predicting strategic food production in East Java Pangki Suseno; Dwi Junianto; Yeni Roha Mahariani
Priviet Social Sciences Journal Vol. 6 No. 2 (2026): February 2026
Publisher : Privietlab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55942/pssj.v6i2.1285

Abstract

Food security is one of the main pillars of sustainable national development, especially in East Java, a region that contributes significantly to the national rice production. However, data from 2016 to 2024 show a downward trend in rice production. This contrasts with the relatively stable consumption demand and poses a risk to future food stability. This study aims to predict future food needs and determine the most accurate forecasting method by comparing the naive method, moving average, single exponential smoothing (SES), and double exponential smoothing (DES) methods. The research data includes annual rice production and consumption volumes in East Java over a nine-year period. We evaluated the forecasting accuracy using the mean absolute deviation (MAD), mean squared error (MSE), and mean absolute percentage error (MAPE). The results of the analysis show that the double exponential smoothing method (with α = 0.9 and β = 0.1) provides the best performance, with the lowest error rate (MAPE) of 1.020%. This value is much more accurate than those of the naive method (6.397%), moving average method (6.359%), and single exponential smoothing method (6.530%), which are less responsive to downward trends in the data. Therefore, the DES method is recommended as the most appropriate forecasting model to assist the government of East Java with strategic planning and food security policies.
Pengaruh Luas Lahan dan Produktivitas Tebu terhadap Jumlah Tebu Metode Regresi Linier serta Perbandingan Akurasi Peramalan dengan Double Eksponential Smoothing Eka Putri Septiani; Dwi Junianto
HORIZON: Indonesian Journal of Multidisciplinary Vol. 4 No. 4 (2026): HORIZON: Indonesian Journal of Multidisciplinary (In-Press)
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/hijm.v4i4.6078

Abstract

The availability of sugarcane as the primary raw material for the sugar industry plays a vital role in supporting national food security. Variations in cultivated land area and sugarcane productivity are considered important factors affecting annual sugarcane production. This study aims to investigate the influence of land area and productivity on sugarcane production in Indonesia and to evaluate the performance of Multiple Linear Regression and Double Exponential Smoothing (DES) for forecasting purposes. The study employs secondary time-series data covering the period 1981-2024 obtained from the Central Bureau of Statistics (BPS). Multiple Linear Regression was applied to analyze the relationship among variables, while DES was utilized to forecast future production. Forecasting accuracy was assessed using Mean Absolute Percentage Error (MAPE). The findings indicate that both land area and productivity significantly affect sugarcane production, with productivity identified as the most influential factor. The coefficient of determination (R²) of 0.825 demonstrates that the model explains a substantial proportion of production variability. In addition, DES produced a lower forecasting error than Multiple Linear Regression, with MAPE values of 1.06% and 7.38%, respectively. These results suggest that DES is a more reliable approach for forecasting future sugarcane production. Keywords: sugarcane production, land area, productivity, multiple linear regression, double exponential smoothing.
Analisis Faktor-Faktor yang Memengaruhi Produksi Gula serta Perbandingan Akurasi Regresi Linear dan Single Exponential Smoothing Puspita Dea Anggraini; Dwi Junianto
JURNAL TECNOSCIENZA Vol. 10 No. 2 (2026): JURNAL TECNOSCIENZA
Publisher : JURNAL TECNOSCIENZA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51158/34bwyk52

Abstract

Sugar production in Indonesia is still fluctuating and has not been able to meet the increasing domestic demand, so a comprehensive analysis of factors related to sugar production and accurate forecasting methods is needed. This study aims to analyze the relationship between sugarcane production and yield to sugar production in Indonesia and compare the accuracy of multiple linear regression forecasting methods and Single Exponential Smoothing (SES). The data used is secondary data obtained from the Central Statistics Agency with a total of 44 observations. The analysis was carried out using linear regression to test the relationship between variables and the SES method to forecast until 2025. The results showed that sugarcane production and yield had a significant relationship with sugar production, both partially and simultaneously, with a determination coefficient value (R²) of 0.703. The accuracy test showed that the linear regression method had a MAPE value of 11.75%, while the SES method was 8.85%, so the SES was considered more accurate. The forecast results indicate a trend to increase sugar production until 2025. Thus, the SES method is more suitable for forecasting, while linear regression is more appropriate for analyzing the relationships between variables. 
Co-Authors Eka Putri Septiani Pangki Suseno Puspita Dea Anggraini Yeni Roha Mahariani