Supply chain management using fp-growth algorithm for medicine distribution

Distribution of drugs evenly in accordance with the needs of Public Health Center (Puskesmas) become one of the responsibilities by the Health Office in Indonesia. This study aims to provide recommendations for distribution of drugs from the Department of Health according to the needs of each Puskesmas. Because often the distribution of drugs is not in accordance with the needs of medicines stock of each Puskesmas. This causes the possibility of drug stock void in Puskesmas in need, while there is excess stock of drugs in Puskesmas that do not need. Supply Chain Management (SCM) is applied as a controlling drug stock at Puskesmas in order to avoid drug vacuum or excess drug that eventually unused. In addition, the Frequent Pattern Growth (FP-Growth) algorithm that generates frequent item sets is used to provide drug distribution recommendations by looking at the highest frequency of drug occurrences and frequent drug frequencies. Based on testing black box system conducted in Health Office Purwakarta regency of Indonesia which oversees 20 Puskesmas with 100 data of drug distribution transactions, it can be concluded that system functionality is running well and SCM successfully implemented to arrange distribution process of medicine well. Furthermore FP Growth algorithm was able to provide recommendations for distribution of drugs with a high success rate. This is evidenced by the test results with various combinations of input parameters, FP-Growth is able to produce the right frequent item sets.