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Yahya Darmawan
Indonesian Agency For Meteorological, Climatological And Geophys-ics (BMKG) Region I
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Journal : Sinergi

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Development of face image recognition algorithm using CNN in airport security checkpoints for terrorist early detection Anggraini, Eca Indah; Nurdin, Fachdy; Restianto, Mohammad Obie; Dahsan, Sudarti; Ardhana, Andini Aprilia; Supriyadi, Asep Adang; Darmawan, Yahya; Arief, Syachrul; Ikhsanudin, Agus Haryanto
SINERGI Vol 29, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.1.004

Abstract

Ensuring airport security is of paramount importance to safeguard the lives of passengers and prevent acts of terrorism. In this context, developing advanced technology for early terrorist detection is crucial. This paper presents a novel approach to enhancing security measures at airport checkpoints by applying Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) algorithms in face image recognition. Our system utilizes state-of-the-art artificial intelligence techniques to analyze facial features. Our research uses VGG architecture and pre-trained with face data as a CNN model. This model is used to extract face embedding features from the dataset. These embedding features are then compressed with Principal Component Analysis (PCA) to obtain the meaningful feature as training data for the ANN algorithm. We trained our system using data from 500 identities data with 60 data for each identity.  This training enables our system to recognize known terrorists and individuals on watchlists by comparing the facial features of individuals passing through security checkpoints with those in the database. The proposed CNN-ANN-based face recognition system not only enhances airport security but also significantly reduces the processing time for security checks. It can quickly identify potential threats, allowing security personnel to take appropriate actions in real time ensuring a rapid response to security concerns. We present the architecture, training methodology, and evaluation of the CNN-ANN model, achieving a high accuracy of 91.16% and precision of 91.36%. Through this research, we aim to increase airport security and strengthen efforts to combat terrorism, making air travel safer and more secure for all passengers. 
Co-Authors Afriyanti Agung Perdian Sulistio Akbar, Ahmad Aldizar Al Badri, Abdul Aziz Amanu, Rendy Syahril Amri, Sayful Anggraini, Eca Indah Aplena Elen S. Bless Ardhana, Andini Aprilia Arief Wibowo Arief, Syachrul Arifianto, Fendy Arkananta, Muhammad Zaky Armadyaputri, Aludra Nadia Bambang Giyanto Benyamin Heryanto Rusanto Carundyatama, Daniar Ihza Dahsan, Sudarti Deni Septiadi Dhaifullah Rafif Aslam Erna Frida Fadhli Aslama Afghani Fadllillah, Ahmad Arif Zulfan Ferdiansyah, Ervan Ferdiyansyah, Ervan Franchitika, Rizky Giananti, Attiya Shakila Habibi, Naufal Ilham Hayatul Khairul Rahmat Hibatullah, Khindi Aufa Ikhsanudin, Agus Haryanto Imawan Mashuri Karyono Karyono Kusumayanti, Diah Lumbantoruan, Alva Josia Manik, Willy Bonanja Manullang, Safri Emanuel Manurung, Ellya Veronika Iriani Manurung, Royston Marhaposan Situmorang, Marhaposan Muhamad Arif Jumansa Muhammad Labieb Muzakkie Muhammad Zaky Arkananta Mulya, Aditya Munawar Munawar Nardi, Nardi Nisa, Ania Maulidiah Nurdin, Fachdy Nuzula Elfa Rahma Oktabrian, Krisna Dwi Parwati Sofan Rahma, Nuzula Elfa Ramadoni Khirtin Restianto, Mohammad Obie Rista Hernandi Virgianto Rizki Ramadani Sabrina, Purwanti Lelly Samen Baan Sanjaya, Kadek Valerina Kitana Saputra, Ahmad Irsyad Saragih, Immanuel Jhonson A. Sudarisman, Maman Supriyadi, Asep Adang Syahrul Humaidi, Syahrul Tambunan, Nensy Nindy Tanggahma, Yuan Zalfa Trianasari, Maria Evy Tulus Ikhsan Nasution Veanti, Desak Putu Okta Vidia, Trimawarti Esti Wandono, Wandono Weman Suardy Wibowo, Shindyko Widodo , Anton Widodo Widodo