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All Journal Jurnal Mantik Penusa JIPN (Journal of Informatics Pelita Nusantara) Journal of Intelligent Decision Support System (IDSS) Jurnal Ilmiah Multidisiplin Indonesia Multidisiplin Pengabdian Kepada Masyarakat (M-PKM)
Penda Sudarto Hasugian
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Health Professions Nursing Physics

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Journal : Journal of Intelligent Decision Support System (IDSS)

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Implementation Of Data Mining In Determining Sales Pattern Of Snack Products Using Apriori Algorithm (Case Study: PT Siantar Top Tbk) Siti Patimah; Penda Sudarto Hasugian
Journal of Intelligent Decision Support System (IDSS) Vol 3 No 4 (2020): December: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

PT Siantar Top.Tbk is a company engaged in snack manufacturing which is located at Jl Raya Medan Tebing Tinggi, Ujung Serdang, Tanjung Morawa, Deli Serdang Regency, North Sumatra. In the sales transaction data processing process at PT Siantar Top Tbk, it has not been able to provide accurate information about the pattern or relationship of a set of items purchased by customers. So that the company has difficulty knowing every product that is sold, because the sales data is always increasing, but the company does not understand how to manage the sales data of these snack products. Because the snack product sales data is only archived and not managed by the company to get new results. The purpose of this research is to design and build a priori algorithm in determining sales patterns. This system is designed using UML and is built with the programming languages ​​PHP, HTML, CSS, Javascript and Mysql as the database. Then the determination of the sales pattern of snack products that are successful every month at PT Siantar Top Tbk using the Apriori algorithm. A priori algorithm is a data mining technique to find associative rules between a combination of items.
Co-Authors Asay Kumar Efrika Manurung Erwin Panggabean Hasugian , Paska Marto Jijon R. Sagala Kelvin Kalsel Nuraisana, Nuraisana Relima Mahdalena Simanjorang Rohit Siti Patimah Wira Apriani