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Deny Kurniawan
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Journal : Jurnal Mantik

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OPTIMIZATION OF THE NUMBER OF CLUSTERS ON K-MEDOIDS USING CHEBYCHEV AND MANHATTAN ON GOLD SELLING GROUPING Dedi Triyanto; Deny Kurniawan; Mochamad Wahyudi
Jurnal Mantik Vol. 5 No. 3 (2021): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Gold is a type of precious metal that can maintain value and can be used for exchange. Gold has attractive properties, so many people like to buy gold for jewelry and also for investments that can be resold when they need money quickly. During the COVID-19 pandemic, some sales sectors experienced a decline but gold was still selling well. M. Siregar Gold Shop serves gold jewelry sales. Gold jewelery sales transactions at the M. Siregar gold shop are stored in the database. Every day the transaction data is increasing, so the data is getting more and more. From the mountains of data we can dig up information or generate knowledge. M. Siregar's gold shop has difficulty in knowing the type of gold that is selling well, making it difficult for gold shop owners to determine the right gold supply. This study aims to classify gold sales at the M. Sisregar gold shop so that it is known which types of gold are selling well. This grouping uses the K-Medoids method with the calculation of the distance between the Chebychec distance and the Manhanttan distance. The data is taken from the sales of gold at the M. Siregar store from November 2021 to March 2022. To produce an optimal grouping, this grouping is tested with several number of clusters by calculating the distance between Chebycev distance and Manhattan distance by calculating the DBI value of each number of clusters. . The result of the optimal grouping of gold sales is the K-Medoids method with the calculation of the Chebycev distance with the number of clusters = 2 with DBI value = 0.024.ns.
COMPARISON OF EUCLIDEAN DISTANCE, CAMBERRA DISTANCE, AND CHEBYCHEV DISTANCE IN K-MEANS ALGORITHM BASED ON DBI EVALUATION Deny Kurniawan; Dedi Triyanto; Mochamad Wahyudi
Jurnal Mantik Vol. 5 No. 4 (2022): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

During the COVID-19 pandemic, almost all businesses experienced difficulties. But not all businesses experience difficulties. Cosmetics is a product category that still exists during the pandemic. Many customers buy cosmetics through online sales. Devi Cosmetics is a trading business which is engaged in selling cosmetics. Due to the large number of sales transactions recorded in the neglected database, it is difficult for business managers to find out which cosmetic products are in high demand by customers and make it difficult for business managers to determine the inventory of cosmetic goods correctly. Determination of the incorrect supply of cosmetics resulted in the loss of the store manager, namely many customers who canceled buying cosmetics due to empty supplies. This study uses the K-Means algorithm to classify sales of cosmetic goods. To find out the best grouping results, it is necessary to compare several distance calculation methods. The distance calculation method here uses three methods, namely Euclidean Distance, Camberra Distance, and Chebychev Distance by finding the DBI value of the three methods. The smallest DBI value is the chebychev distance calculation method with a DBI value = 0.254.
Co-Authors Adi Supriyatna Dedi Triyanto Dedi Triyanto Indra Chaidir, Indra Lise Pujiastuti Lise Pujiastuti Mochamad Wahyudi Sumanto, Sumanto