Implementation of The Apriori Algorithm for Spare Parts Data Sales


Siti Nur Khasanah
Levi Juniarti
Sandra Jamu Kuryanti
Eko Yulianto


Pakubumi piling spare parts are very much needed when there is a lot of infrastructure development in the capital city and in small cities. Spare parts that are used at the time of construction and for stocks carried out by project workers that will be prepared by suppliers as well as in CV. Abadi Perkasa. With a lot of available sales data, an algorithm is needed to manage the sales data. Data processing sales data mining with a priori algorithm technique can look for the highest frequency pattern where the highest frequency value is above a certain threshold which is called minimum support. After getting the highest support value, then the confidence value is sought to determine the association rules for one product with another. Looking for a minimum of support and confidence can also be found with the help of rapidminer tools. From this research, it can be seen that the a priori algorithm can find out which products are often purchased as well as determine the patterns of relationships between products and help to develop future marketing strategies.


How to Cite
Khasanah, S. N., Juniarti, L., Kuryanti, S. J. and Yulianto, E. (2020) “Implementation of The Apriori Algorithm for Spare Parts Data Sales”, Jurnal Mantik, 4(3), pp. 1568-1572. doi: 10.35335/mantik.Vol4.2020.892.pp1568-1572.


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