| dc.description.abstract | The advancement of technology has led to the onset of the Fourth Industrial
Revolution, where the physical, digital, and biological worlds are integrated.
Trends such as automation, the Internet of Things (IoT), big data, and cloud
computing technologies dominate this era. Data science, in particular, has become
crucial, helping to transform data into meaningful information. Data mining has
also rapidly developed, driven by advancements in information technology and the
internet. Currently, the furniture industry is one of the sectors influencing
Indonesia's economy. The demand for furniture continues to increase over time due
to various innovations in the furniture being produced. Therefore, PT Starindo
Prima, one of the industries in the furniture sector, must always provide its products
to meet customer demands. However, in 2023, PT Starindo Prima faced several
challenges, such as the unavailability or shortage of stock when customer demand
occurred. This issue necessitated PT Starindo Prima to produce the products first,
resulting in delivery times of 1-2 months. The improper stock provision has been a
major issue causing these problems. Data mining processes on transaction data
become a solution to identify customer purchase patterns to determine products for
which safety stock and reorder points will be calculated, ensuring product stock is
always maintained. This study uses one of the data mining techniques, namely
association techniques, to analyze customer purchase patterns. By using the FPGrowth
algorithm, customer purchase patterns will be generated and analyzed to
obtain product recommendations for production. The results include 11 association
rules formed with a minimum support of 0.03 or 3% and a minimum confidence of
0.5 or 50%. The products generated based on the association rules amount to 12
products with an estimated safety stock ranging from 33 to 66 units and reorder
points ranging from 53 to 106 units | en_US |
