Data Science Insights
Vol. 2 No. 1 (2024): Journal of Data Science Insights

Application of Geolocation Methods in Student Attendance System Design

Rizya Pratama , Yoga (Unknown)
Siswanto, Apri (Unknown)



Article Info

Publish Date
29 Feb 2024

Abstract

Universitas Islam Riau is one of the universities in Riau province that is of interest to high school graduate students as a place to continue their studies at a higher level. Implementing the student attendance process at the Universitas Islam Riau is still done manually; this causes less efficiency and effectiveness of attendance activities, starting from data collection, processing presence data, and storing and searching processes, which take time. In some cases, fraud may occur, such as falsifying the presence of someone represented by another party. Then, we need a system that can record the attendance of students whose positions are within the scope of the class radius. Geolocation can capture device coordinates by utilizing latitude and longitude, which will be used to measure the distance between classes and students. If the student's position is outside the class radius determined by each lecturer, then the student cannot fill in attendance. If the student's position is within the scope of the class radius that has been determined, students can fill in attendance. In the research, we succeeded in designing a student attendance system based on the geolocation method. Security to overcome fake GPS managed to function properly, and fingerprints to take attendance can work properly. From the results of Black box testing, the system can run well and is free from syntax and functional errors.

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Journal Info

Abbrev

jdsi

Publisher

Subject

Computer Science & IT Engineering

Description

Data Science Insights, with ISSN 3031-1268 (Online) published by PT Visi Media Network is a journal that publishes Focus & Scope research articles, which include Data Science and Machine Learning; Data Science and AI; Blockchain and Advance Data Science; Cloud computing and Big Data; Business ...