Proceedings of the 2023 Annual Technology, Applied Science and Engineering Conference (ATASEC 2023)

Image Processing in Automatic Locking System on SS2 TNI AD Weapon Rack Using Fingerprint Sensor with K-Nearest Neighbor (KNN) Algorithm Method

Authors
Rafi Maulana Al-farizi1, *, Indrazno Siradjuddin2, Sapto Wibowo3
1Electrical Engineering, State Polytechnic of Malang, Malang, Indonesia
2Electrical Engineering, State Polytechnic of Malang, Malang, Indonesia
3Electrical Engineering, State Polytechnic of Malang, Malang, Indonesia
*Corresponding author. Email: rafi.maulana0048@gmail.com
Corresponding Author
Rafi Maulana Al-farizi
Available Online 12 January 2024.
DOI
10.2991/978-94-6463-358-0_8How to use a DOI?
Keywords
Fingerprint Sensor; Image Processing; K-Nearest Neighbor (KNN)
Abstract

Weapon security is crucial in military environments. An automatic weapon rack locking system using fingerprint sensors can be an effective solution to prevent unauthorized access. However, in order to implement an automatic locking system an authorizing method using fingerprint is required. In the image processing stage, fingerprint images obtained from the sensor are processed to extract important features, such as core points and unique ridge orientations in the fingerprint. The K-Nearest Neighbor (KNN) algorithm compares the unknown fingerprint features with the fingerprints in the database to be classified. In the conducted tests, the image identification speed using 10 images was found to be 0.46 seconds, while it was 1.72 seconds for 50 images. Furthermore, the system achieved a False Acceptance Rate (FAR) of 0% and a False Rejection Rate (FRR) of 8% using an optimal Parameter K. The success rate reached 93%, with an accuracy level of 96%. Thus, optimal processing speed and accuracy were achieved in this automatic locking system. Consequently, this system can be effectively implemented to enhance weapon security in military environment.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 Annual Technology, Applied Science and Engineering Conference (ATASEC 2023)
Series
Advances in Engineering Research
Publication Date
12 January 2024
ISBN
978-94-6463-358-0
ISSN
2352-5401
DOI
10.2991/978-94-6463-358-0_8How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - Rafi Maulana Al-farizi
AU  - Indrazno Siradjuddin
AU  - Sapto Wibowo
PY  - 2024
DA  - 2024/01/12
TI  - Image Processing in Automatic Locking System on SS2 TNI AD Weapon Rack Using Fingerprint Sensor with K-Nearest Neighbor (KNN) Algorithm Method
BT  - Proceedings of the 2023 Annual Technology, Applied Science and Engineering Conference (ATASEC 2023)
PB  - Atlantis Press
SP  - 63
EP  - 74
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-358-0_8
DO  - 10.2991/978-94-6463-358-0_8
ID  - Al-farizi2024
ER  -