Analysis of Sales Pattern Determination System and Drug Stock Recommendation

Abstract

The tight competition in the pharmacy industry, requires pharmacy owners to develop strategies in increasing drug sales. One of the strategies carried out is to analyze patterns of drug sales and determine drug stock recommendations based on sales transaction data. Based on this, an application was built to determine the pattern of drug sales and drug stock recommendations by using a modified Apriori Algorithm and Triple Exponential Smoothing Method. Apriori algorithm modification is used to overcome the problem of large amounts of sales transaction data, thus minimizing the time in the database scan process. Triple Exponential Smoothing method is used in determining drug stock recommendations based on sales patterns that have been generated to prevent excess or lack of stock. Application testing techniques used are performance testing, lift ratio and accuracy testing. The research resulted in a sales pattern that has a strong association rule and time analysis using Apriori Algorithm modification that is faster than using a traditional Apriori Algorithm and the percentage error value of drug stock recommendations by 31.84%.

Keywords: Sales Pattern, Stock Recommendations, Apriori Algorithm Modification and Triple Exponential Smoothing