IMPLEMENTATION OF APRIORI ALGORITHM IN PREDICTING CAR PARTS

Syahman Putra Pratama Nst (Universitas Labuhan Batu)
Ibnu Rasyid Muthe (Universitas Labuhan Batu)
Rahma Mutiah (Universitas Labuhan Batu)

INFOKUM
Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence

In the procurement of spare parts for the car production process, information or knowledge related to auto parts is needed so that the company's production processes and results are effective and efficient. The purpose of this study is to apply an a priori algorithm for prediction of auto parts that often appear. The research subject used is the BMW car production data of PT Gaya Motor. The research model used is market basket analysis. The stages of the research carried out include: (1) Data Collection; (2) Training Data; (3) Formation of Association Rule, (4) Lift Ratio Test, and (5) Drawing Conclusion. The research results obtained are the most widely produced BMW car types in 2018 are the BMW 320 and BMW 7 Series. So the company can use these results to determine strategies related to the procurement of spare parts for that type of car. Based on the Lift Ratio Test that has been carried out, there are two very strong and valid rules to be used in the prediction of BMW auto parts, namely the BMW 320 and BMW 7 SERIES.