Robustness Analysis of QR – Code Based and Geolocation Based Attendance System
Abstract
The use of QR-code and GPS technology in the attendance system also has several advantages, such as making the attendance process easier for students, monitoring employee attendance accurately and efficiently, and improving the quality of student work. Therefore, it is important to compare the efficiency of the QR-code attendance method with Geolocation to find out which technology is more effective and efficient in improving the attendance system. In this research the author used the K-means Clustering method. The K-means method is a non-hierarchical data grouping method that attempts to partition existing data into two or more 13 groups. This method partitions data into groups so that data with the same characteristics is included in other groups. The research results show that Cluster 1 has a centeroid that is close to the QR Code features (3,4,4), namely moderate efficiency in application implementation, relatively high ease of use, and system robustness with high data accuracy. Cluster 2 has a centeroid with Geolocation features (3,3,3), namely moderate efficiency and flexibility, moderate ease of use, and system robustness with moderate data accuracy. Thus, after obtaining comparison results between QR code and Geolocation in the lecture attendance process, researchers can recommend the best system to use in terms of user aspects and needs. If the user needs a presence system that prioritizes ease of use and robustness of the system, the user is suited to using the QR-Code system because the usability and durability aspects are relatively high. Meanwhile, if the user prioritizes efficiency and flexibility in the process, it is best to use a presence system in the form of Geolocation, because the results of this research show that the efficient aspect of Geolocation is higher
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