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Sajaratud Dur
Department of Mathematics, Universitas Islam Negeri Sumatera Utara, Medan, Indonesia
Mathematics

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PREDICT THE PRICE OF CURLY RED CHILI IN NORTH SUMATRA USING THE HOLT WINTERS ADDITIVE METHOD Umi Sarah Nurainun; Sajaratud Dur; Rina Widyasari
Journal of Mathematics and Scientific Computing With Applications Vol. 2 No. 1 (2021)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (361.045 KB)

Abstract

Curly red chilies are one of the vegetable commodities that have an effect on national economic growth. North Sumatra is one of the largest red chilli have a problem with price fluctuations which will result in inflanation. Erratic chili prices will have an impact on society and the country. The right policy to avoid negative impact on price fluctuations of North Sumatra’s curly red chilies is to predict it in the future. The purpose of this study was to obtain the result of the prediction of the price of North Sumatra curly red chilies. The results of this analysis can be used in determining the right policy. The method used in this study is the Holt Winters Additive Method, because the Holt Winters Additive Method is a method that can be used for forecasting data that has elements of trend and seasonality. The data used in this study is the average price of North Sumatra curly red chilies per week from January 2020 to February 2021 which is obtained from the National Strategic Food Price Information Center. After testing the price of curly red chilies in North Sumatra, a forecast data plot is obtained which tends to follow the actual data. Then the error rate is measured using MAPE (Mean Absolute Percentage Error). The MAPE results obtained were 10.15% with the best parameters ? = 0.84, ? = 0.09 and ? = 0.83. this means that the Holt Winters Additive method has a good level of accuracy used to predict the price of curly red chilies in North Sumatra Province.
OPTIMIZATION OF SYAHFIRA BAKERY PRODUCTION USING THE TAGUCHI-PRINCIPAL COMPONENT ANALYSIS (PCA) METHOD Rodiani Dongoran; Sajaratud Dur; Rina Widyasari
Journal of Mathematics and Scientific Computing With Applications Vol. 2 No. 2 (2021)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (808.29 KB)

Abstract

The bread-making business is part of the finished food industry which uses wheat flour as the main raw material for its production process. Bread production has quality characteristics, namely bread surface roughness (Smaller is better) and material processing rate (Larger is better). The combination of the Taguchi-Principal Component Analysis method is used to optimize bread products. The experimental design used is the L9 orthogonal matrix. These quality characteristics are influenced by factors such as the length of time for mixing and kneading, yeast fermentation, roasting time and the dose of water with 3 levels each. Principal Component Analysis (PCA) is used to eliminate correlated correlated responses to an uncorrelated quality index. The results showed that this method can improve the quality of bread production in influencing the surface roughness of the bread and the significant speed of processing the ingredients is the dough time, yeast fermentation, and baking time.
Co-Authors Rina Widyasari Rodiani Dongoran Umi Sarah Nurainun