Optimizing Sales Strategies to Address Excessive StockAccumulation: A Data Mining Approach

Abstract

The Two Pelita Weaving Business has recorded significant
sales in the weaving industry, despite facing challenges in
managing product stock due to the accumulation of excess
stock caused by a lack of customer interest. This study
employs data mining techniques, specifically the Association
Rule and Apriori algorithms, to analyze sales patterns. The
analysis results using Python and Orange Data Mining
showed consistency in the relationship between Siku
Keluang Weaving and Pucuk Rebung Weaving products,
with high occurrence rates of purchase patterns (11.74% and
10%, respectively). High confidence levels with Python at
96.36% and Orange Data Mining at 99.1% indicate that
customers who purchase Siku Keluang Weaving are also
likely to purchase Pucuk Rebung Weaving products.