Optimasi Prediksi Harga Ayam Boiler Berdasarkan Time Series Data dan Kondisi Eksisting Menggunakan Decision Tree
DOI:
https://doi.org/10.29407/noe.v8i02.24878Keywords:
optimation, Decision TreeAbstract
Predicting the price of boiler chicken is important in the chicken farming industry. Business actors need to have accurate price estimates as a reference in planning production and sales. However, precise and accurate price predictions can be challenging because they are influenced by various factors such as market conditions, demand, supply, and other factors. Therefore, this research was conducted to develop a boiler chicken price prediction method that can optimize prediction results by utilizing time series data and existing conditions using the Decision Tree C.45 algorithm. The aim of this research is to optimize boiler chicken price predictions based on time series data and existing conditions using the Decision Tree C.45 algorithm. By processing time series data and analyzing existing conditions, it is hoped that a more accurate prediction model can be obtained and can provide better results in predicting the price of boiler chicken. Apart from that, by implementing the Decision Tree C.45 algorithm, this research also aims to test the effectiveness of this algorithm in predicting the price of boiler chicken. The result of this research is a system that can accurately predict the price of boiler chicken, so that it can be used as an important basis for making decisions regarding determining the price of boiler chicken and inventory management.
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