DESIGN AND IMPLEMENTATION OF RFID BASED ATTENDANCE ARCHITECTURE USING BIG DATA CONCEPT CASE STUDY : ATTENDANCE SYSTEM IN BANDUNG JUNIOR HIGH SCHOOL

• Main Author: RIDHO SAPUTRA (NIM : 23215388), ACHMAD
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/24942
• Institution: Institut Teknologi Bandung

In recent years, the rapid development of the Internet, the Internet of Things, and Cloud Computing has led to explosive data growth in almost every area of industry and business. Then emerging big data technology, big data is a new technology in the world of information technology that enables the processing, storage, and data analysis in various forms / formats, with large amounts, and very quick data increase. The most interesting thing to do with the data is an analysis that can create new information to help governments, companies or stakeholders in making policies or decisions. Of course, data analysis has long been done before the Big Data, but the new benefits brought by big data analytics, is the speed and efficiency. The purpose of this research is to build IOT-based attendance system architecture using big data concept for SMP attendance system in bandung, data digitization of attendance is one way to simplify the storage, tracking and analysis of attendance data. Attendance system as part of the IOT will provide much potential for developing a manual attendance system into a system of attendance that can be part of the Big Data. The architecture is built using cluster message broker middleware, NoSql Database and FTP Server for media storage. From the result of data fusion, data mining will be obtained and analyzed to get new data about behavior of students discipline, learning motivation, honesty and correlation between student attendance level and academic achievement. Architecture has been successfully implemented and tested at several junior high schools in Bandung for 2 weeks, using 16 attendance machines. The frequency of data entered each time the absence of incoming and returning ranges from 900 to 1002 data. Testing the architecture using simulation data capable of displaying the optimal results is evidenced by processing 10000 data within 3.493 seconds