Implementasi Metode Apriori Pada Sistem Persediaan Bahan Kimia Di Laboraturium Forensik Medan

Abstract

Data mining is a term used to describe the discovery of knowledge in databases. Data mining is a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and related knowledge from large databases. With data mining, a gem in the form of knowledge will be obtained in a large number of data sets. The Apriori (RF) method is a method that can improve accuracy results, because generating child nodes for each node is done randomly. This method is used to build a decision tree consisting of root nodes, internal nodes, and leaf nodes by taking attributes and data randomly according to the applicable provisions. The root node is the node located at the top, or commonly referred to as the root of the decision tree. The solution for determining chemical stock inventories at the Medan Branch Forensic Laboratory, by applying the Apriori method to determine the correlation coefficient level of frequently used products so that more frequently needed products can be provided to avoid chemical vacancies at the Medan Branch Forensic Laboratory