MEDICINE PRODUCT RECOMMENDATION SYSTEM USING APRIORI ALGORITHM AND FP-GROWTH ALGORITHM

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dc.contributor.authorSukenda
dc.contributor.authorWahyu, Ari Purno
dc.contributor.authorSunjana
dc.date.accessioned2021-02-10T03:36:16Z
dc.date.available2021-02-10T03:36:16Z
dc.date.issued2020
dc.description.abstractAs one of the pharmacy products is a company that sells Medicine and website-based health products, this pharmacy is able to produce sales data every day which continues to grow and is not considered to be able to maximize the utilization of the data. Sales data is only stored without further analysis, so an application is needed to analyze the market basket of transaction data on medicine product sales using data mining as a data analysis technique that can help pharmacies, so pharmacy owners obtain knowledge in the form of sales patterns in certain month period. Data mining applications are built using linear sequential processes with the PHP programming language and MySQL database. The algorithm used as the main process of market basket analysis to find out the stock of goods is a priori algorithm by using minimum support, minimum confidence, and month period of the sales transaction to find the association rules. Data mining applications produce association rules between items purchased items, namely consumers make transactions to purchase medicine products simultaneously with min support of 50% and confidence of 80%. Thus, if there is a consumer buying medicine product, then there is a possibility that 80% of consumers buy similar products.en_US
dc.identifier.issn1475-7192
dc.identifier.urihttp://repository.widyatama.ac.id/xmlui/handle/123456789/12307
dc.language.isoenen_US
dc.publisherInternational Journal of Psychosocial Rehabilitation, Vol.24, Issue 02en_US
dc.subjectapriori, medicine,FP-Growthen_US
dc.titleMEDICINE PRODUCT RECOMMENDATION SYSTEM USING APRIORI ALGORITHM AND FP-GROWTH ALGORITHMen_US
dc.typeArticleen_US
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