Algoritma SARIMA sebagai Pendukung Strategi Peramalan HPS dalam Persaingan Tender di LPSE Indonesia

Authors

DOI:

https://doi.org/10.29408/edumatic.v8i2.28009

Keywords:

forecasting, hps value prediction, sarima algorithm, tender, web scraping

Abstract

Tender in Indonesia's Electronic Procurement Service (LPSE) is the procurement of goods/services in the form of public facilities and managed by the provider with the lowest estimated price (HPS) value during the reverse auction process. The fluctuating value of HPS and the tight competition of competitors make winning for providers increasingly difficult and competitive. The purpose of this research is to create a forecasting model of the HPS value of tenders in LPSE Indonesia using the Seasonal Autoregressive Integrated Moving Average (SARIMA) algorithm. This type of research is experimental research to determine the order of the best SARIMA model. The research variables used are tender publication date and HPS value as much as 747,098 tender data from historical data from web scraping of the LPSE website with a withdrawal date range of January 7, 2013 to November 30, 2022. The data analysis technique uses data exploration analysis to determine the characteristics of the data distribution and then the implementation of the SARIMA forecasting algorithm. The results of this study show that the SARIMA((5,1,1),(4,1,1,7)) model is the optimal model with an evaluation value of mean absolute percentage error (MAPE) percentage error value of 33.56% which in relation to LPSE can provide a reasonable forecasting value. The results of forecasting for the next 30 days show that the distribution of HPS values is in the range of 680 million - 700 million rupiah in the period December 2022.

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2024-12-19

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Vol. 8 No. 2 (2024): Edumatic: Jurnal Pendidikan Informatika

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