Implementasi Data Mining Untuk Menganalisis Pola Penimbangan Sampah Menggunakan Algoritma Apriori

Abstract

Pandan Wangi Waste Bank's weighing transaction data has not been maximized and is not used for further purposes. Pandan Wangi Waste Bank receives 42 types of waste from the community, but has no information about the weighing pattern of the waste deposited by the community. Therefore, managers sometimes have difficulty in planning better storage and management. This study aims to analyze waste weighing patterns based on weighing transaction data to identify customer weighing behavior, find the types of waste that are often weighed together, and determine the support, confidence, and lift ratio values of each association rule generated. The technique used is a quantitative method and to process the weighing transaction data into information using the apriori data mining algorithm.  From 866 weighing data for two years from May 2022 to March 2024, this research produces four rules that have a good lift ratio value with a minimum support value of 0.1 and a minimum confidence of 0.8. The most frequently weighed type of waste is the mixed bucket type with a support value of 69.9%. Then for the type of waste that is most often weighed simultaneously is if weighing boncos, and clean mineral bottles, then also weighing mixed buckets with a support value of 0.11 and confidence of 0.87.