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All Journal UNP Journal of Statistics and Data Science
Yan Aditya Pradana
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences

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Forecast Accuracy Comparison Between Holt’s Method and the Box-Jenkins Approach: The Case of Madiun City Labor Force Participation Rate Shobri, Muhammad Qolbi; Yan Aditya Pradana; Putri Balqis Al-Kubro; Nayla Desviona; Nila Destia Nasra
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss3/413

Abstract

Pacitan district recorded the highest Labor Force Participation Rate (LFPR) in Eastern Java Province. Meanwhile, Madiun city which is one of the largest cities in East Java Province, is ranked only 34 out of 39 cities in 2023. This condition raises concern for the local government, perticularly the Department of Manpower,in ensuring that the productive-age population can be optimally absorbed into the labor market. The LFPR is categorized as time series data, thus forecasting method are required to estimate its future trends.This Study compares the performance of the Double Exponential Smoothing Holt (DESH) method and the Autoregressive Integrated Moving Average (ARIMA) Box-Jenkins approach in forecasting the LFPR of Madiun City. The empirical result show that the ARIMA (1,0,1) model provides better accuracy compared to DESH. The forecasting result  indiacte that the LFPR of Madiun City is project to reach 67,19% in 2024, 67,20% in 2025, and 67,21% in 2026, with  Mean Squared Error (MSE) of 14,48; Root Mean Square Error (RMSE) of 3,80 and Mean Absolute Percentage Error (MAPE) of 4,75%. These finding are expected to serve as reference for future research and practical input for policymakers in formulating strategies to improve labor LFPR in Madiun City.
Co-Authors Muhammad Qolbi Shobri Nayla Desviona Nila Destia Nasra Putri Balqis Al-Kubro