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PENGEMBANGAN SISTEM INFORMASI ABSENSI MAHASISWA BERBASIS RFID DAN WEB DENGAN MENGGUNAKAN TEKNIK MULTILATERATION

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Penilaian

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In general, the attendance system at the institution is currently still using the manual method using the attendance list in checking student attendance. This creates an opening for fraud. In this study, an indoor mapping system was created using RFID media. Where each student position represented by the RFID tag was tracked further and mapped its whereabouts. So that in the end the attendance status of each student is updated automatically. The concept of this attendance system has been proposed before. unfortunately, the accuracy of the system in making predictions is still not accurate. In this study, the location of the RFID tag was measured using four RFID antennas by applying the Multilateration technique. Multilateration itself is a location predicting technique based on the distance of the object to the reference point. In the application of Multilateration distance is needed. To convert the RSSI which is obtained from the RFID device into the distance, machine learning algorithms are used. Machine Learning which is compared in this research is Linear Regression and Support Vector Regression. This mapping system is eventually loaded into a web form that can be accessed by learning devices so that they can be used. Based on the results of this study, it was found that an indoor mapping system could be built using the Multilateration technique. Where this technique can map the presence of RFID tags with an average error of 39.4401 cm. In converting distance, it is known that SVR is better with an average error of 19.594227 cm while Linear Regression has an error of 35.255 cm. In this study, the mapping system was also successfully loaded into the attendance information system in a web form using the help of Flask as a framework and Jinja as a web template engine for the Python programming language.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207002203	T74451	T744512022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T744512022

Publisher

Inderalaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2022

Collation

x, 80 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.678 07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Website
Jurusan Sistem Komputer

Specific Detail Info

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Statement of Responsibility

SEPTA

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