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Firliansyah Purba, Muhammad
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Mathematics

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PROFIT OPTIMIZATION IN WET CAKE SALES USING THE SIMPLEX METHOD AND ITS APPLICATION IN POM-QM Luthfiyah, Farica; Aulia, Riska; Irfan, Muhammad; Wahyu Nuraini, Adinda; Firliansyah Purba, Muhammad
Journal of Mathematics and Scientific Computing With Applications Vol. 4 No. 2 (2023)
Publisher : Pena Cendekia Insani

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Abstract

The development of population increases every year causing food needs to increase, to meet food needs by increasing food crop productivity so that food availability can be sufficient. Food crops consist of rice, corn, green beans, peanuts, cassava, and sweet potatoes. Productivity in each region has different characteristics and therefore it is necessary to group the regions so that solution can be implemented in accordance with each of the characteristics of the region. The purpose of this study is to group districts/cities in North Sumatera Province based on food crop productivity using the k-means clustering method. Clustering k-means is method of grouping non-hierarchical data that attempts to partition existing data into one or more cluster or groups so that data that has the same characteristics are grouped into one same characterstics are grouped into other groups. The result of this study are the formation of 3 city district clusters namely, cluster 1 amounting to 1 regency/city, cluster 2 totaling 7 districts/cities, and cluster 3 totaling 25 districts/cities.
PROFIT OPTIMIZATION IN WET CAKE SALES USING THE SIMPLEX METHOD AND ITS APPLICATION IN POM-QM Luthfiyah, Farica; Aulia, Riska; Irfan, Muhammad; Wahyu Nuraini, Adinda; Firliansyah Purba, Muhammad
Journal of Mathematics and Scientific Computing With Applications Vol. 4 No. 2 (2023)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v4i2.972

Abstract

The development of population increases every year causing food needs to increase, to meet food needs by increasing food crop productivity so that food availability can be sufficient. Food crops consist of rice, corn, green beans, peanuts, cassava, and sweet potatoes. Productivity in each region has different characteristics and therefore it is necessary to group the regions so that solution can be implemented in accordance with each of the characteristics of the region. The purpose of this study is to group districts/cities in North Sumatera Province based on food crop productivity using the k-means clustering method. Clustering k-means is method of grouping non-hierarchical data that attempts to partition existing data into one or more cluster or groups so that data that has the same characteristics are grouped into one same characterstics are grouped into other groups. The result of this study are the formation of 3 city district clusters namely, cluster 1 amounting to 1 regency/city, cluster 2 totaling 7 districts/cities, and cluster 3 totaling 25 districts/cities.
Co-Authors Aulia, Riska Luthfiyah, Farica Muhammad Irfan Wahyu Nuraini, Adinda