Evaluasi Metode Inisialisasi pada Model Pemulusan Eksponensial melalui Data IPM Banyumas Raya

Authors

  • Melda Juliza Unsoed
  • Novita Eka Chandra Universitas Jenderal Soedirman
  • Felinda Arumningtyas Universitas Jenderal Soedirman
  • Puce Angreni Universitas Jenderal Soedirman

DOI:

https://doi.org/10.52166/ujmc.v12i1.13519

Keywords:

Double Exponential Smoothing, Initialization Methods, Simple Initialization, Optimal Initialization, Human Development Index (HDI), Time Series, Model Evaluation

Abstract

The forecasting accuracy of exponential smoothing models is significantly influenced by the determination of initial values (initialization). This study aims to evaluate the performance of initialization methods for Brown’s Double Exponential Smoothing model using Human Development Index (HDI) data from the Banyumas Raya region for the period 2010-2025. The research stages included identifying data patterns, constructing models using both simple initialization and optimal initialization with numerical optimization, performing the Ljung-Box test for residual diagnostics, and comparing model accuracy. Evaluation results indicate that the Brown model using the optimal initialization method effectively captures trend patterns. The application of optimal initialization consistently improved model accuracy across all regencies. The highest error improvement was observed in Banyumas Regency (28.65%), followed by Cilacap (28.29%), Banjarnegara (24.77%), and Purbalingga (22.89%). Based on these results, the optimal initialization model was used to project HDI values for the next three periods, revealing a sustained upward trend. In conclusion, determining initial values is a crucial component that alongside smoothing parameter optimization must be seriously considered when developing forecasting models.

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Published

2026-06-30

How to Cite

Juliza, M., Novita Eka Chandra, Felinda Arumningtyas, & Puce Angreni. (2026). Evaluasi Metode Inisialisasi pada Model Pemulusan Eksponensial melalui Data IPM Banyumas Raya. UJMC (Unisda Journal of Mathematics and Computer Science), 12(1), 59–69. https://doi.org/10.52166/ujmc.v12i1.13519

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