FITRIYANI, AIDA Implementation of Apriori Algorithm for Determining Spare Parts Product Recommendation Packages. Implementation of Apriori Algorithm for Determining Spare Parts Product Recommendation Packages.
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Abstract
The aim of this research is to determine recommended product packages for spare parts from an automotive parts supplier. Shop owners have faced challenges in meeting customer demands over the past few months, experiencing frequent stockouts of spare parts due to a manual transaction recording system and a manual checking system for spare parts storage. This inefficiency and lack of accuracy in managing in-demand spare parts prompted the application of the apriori algorithm, a data mining method. Data was collected from the total sales over the past three months, subsequently cleaned and transformed for manual and Python-based apriori calculations. The results, obtained through both manual and Python implementations of apriori, indicate that the two frequently occurring item sets are oil filters with a confidence value of 68% and air filters with a confidence value of 63%. Based on these findings, the study recommends spare parts stores to maintain higher stock levels of oil filters and air filters compared to other spare parts. Keyword: Spare Parts Product, Apriori Algorithm, Crisp-DM, Association Rules.
| Item Type: | Article |
|---|---|
| Subjects: | Teknologi dan Ilmu Terapan > Komputer > Data File-file dan Database, Pangkalan Data, Pusat Data Teknologi dan Ilmu Terapan > Komputer |
| Depositing User: | Aida Fitriyani |
| Date Deposited: | 16 Sep 2024 23:44 |
| Last Modified: | 16 Sep 2024 23:44 |
| URI: | http://repository.ubharajaya.ac.id/id/eprint/31784 |
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Implementation of Apriori Algorithm for Determining Spare Parts Product Recommendation Packages. (deposited 13 Sep 2024 06:50)
- Implementation of Apriori Algorithm for Determining Spare Parts Product Recommendation Packages. (deposited 16 Sep 2024 23:44) [Currently Displayed]
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