Penerapan Supervised Emerging Patterns Untuk Multi Atribut Pada Data Online Izin Usaha Pertambangan di Indonesia (Studi Kasus: EITI Indonesia) - utami, yohana tri; Harco Leslie Hendric, Spit Warnars (Jurnal Sistem Komputer, 2016)
AbstractIndonesian EITI (Extractive Industries Transparency Initiative) is an organization under Ministry of Economic Coordination which used to increase the transparency of extractive industry in Indonesia. EITI Indonesia manage a lot of data about mining, one of the managed data is data Mining Business License in Indonesia. The data has many records that require large storage allocation and difficult process data that is used by the EITI for decision making. .This data Mining Business License will be used for the processing of data mining that aims to help look for interesting patterns to determine a learning and two itemsets (attributes) that exist. Application Data Mining with Emerging Patterns Supervised methods will be used as a solution for managing data such large, so it is easy to produce a decision in the form of an interesting pattern information to determine the transparency of mining license in Indonesia. System development methods using CRISP-DM. The design of data mining applications using the programming language Java, NetBeans and MySQL database tools used to build a technology Supervised Emerging Patterns in multi-attribute decision making.