Application of Lagrange Interpolation Method in Predicting the Number of HVS Orders in Printing

The research entitled “Application of the Lagrange Interpolation Method in Predicting the Number of HVS Orders in Printing” aims to develop an accurate forecasting model for the number of HVS orders in the printing industry. In this study, the Lagrange interpolation method was chosen because it can provide estimates by considering limited historical data. The analyzed data ranges from January 2021 to November 2022 and is processed to build a forecasting model. The results showed that the interpolation model built was able to capture well the fluctuation pattern of the order quantity. Model evaluation is carried out using Mean Absolute Percentage Error, which reads <=10%, indicating high prediction accuracy. In addition, sensitivity analysis to data variations showed consistent results of the prediction, thus strengthening the validity of the method. These findings suggest that Lagrange interpolation is an effective tool in forecasting HVS demand, providing advantages in inventory management and production planning in printing houses.

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Publication Details

Journal
Holistic Science
Published
2024-12-10
DOI
https://doi.org/10.56495/hs.v4i3.791
Citations
1
Primary Topic
Advanced Numerical Analysis Techniques
Type
article
Field-Weighted Citation Impact
0.37
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article

Application of Lagrange Interpolation Method in Predicting the Number of HVS Orders in Printing

Amelia Putri, D. A. Triana, Anatasia Faradhilah, Yolanda Angelina Sitorus
1 citations
Holistic Science
Advanced Numerical Analysis Techniques
0.37
article

Application of Lagrange Interpolation Method in Predicting the Number of HVS Orders in Printing

Amelia Putri, D. A. Triana, Anatasia Faradhilah, Yolanda Angelina Sitorus
article en
1 citations

Abstract

The research entitled “Application of the Lagrange Interpolation Method in Predicting the Number of HVS Orders in Printing” aims to develop an accurate forecasting model for the number of HVS orders in the printing industry. In this study, the Lagrange interpolation method was chosen because it can provide estimates by considering limited historical data. The analyzed data ranges from January 2021 to November 2022 and is processed to build a forecasting model. The results showed that the interpolation model built was able to capture well the fluctuation pattern of the order quantity. Model evaluation is carried out using Mean Absolute Percentage Error, which reads <=10%, indicating high prediction accuracy. In addition, sensitivity analysis to data variations showed consistent results of the prediction, thus strengthening the validity of the method. These findings suggest that Lagrange interpolation is an effective tool in forecasting HVS demand, providing advantages in inventory management and production planning in printing houses.

Holistic ScienceVol. 4(3)
State University of Medan (ID)
Industry, innovation and infrastructure
Openalex Percentile: Top 40%
Advanced Numerical Analysis Techniques
0.37
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