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All Journal Sinergi Sunan Kalijaga Journal of Physics
Anggraini, Eca Indah
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Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Physics

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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. 
Estimating the weight of Ongole crossbreed cattle based on image data using CNN and linear regression methods Gumelar, Syahrul Fadholi; Anggraini, Eca Indah
Sunan Kalijaga Journal of Physics Vol. 5 No. 2 (2023): Sunan Kalijaga Journal of Physics
Publisher : Prodi Fisika Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/physics.v5i2.3717

Abstract

One of the contributors to the need for food, especially meat, is the Ongole breed of cattle or commonly known as PO cattle. In these livestock activities it is necessary to monitor the weight of the cattle with the aim of assessing the selling price of the cattle and knowing the health condition of the cattle. Currently breeders are still using traditional methods such as forecasts or scales in measuring the weight of cattle. Therefore, in this study using a camera sensor as an alternative instrument for measuring cattle weight. The stages of the research included image data acquisition, pre-processing, body segmentation of cattle, weight estimation and system evaluation. The process of acquiring image data is obtained with a DSLR camera device. Pre-processing is done using a kernel sharpening filter. Cattle body segmentation uses the Mask R-CNN method. The body image of the cow is then processed for weight estimation training using the CNN and Linear Regression methods. The system evaluation results at the segmentation stage succeeded in obtaining an Intersection over Union (IoU) metric value of 0.86. The weight estimation results managed to get a RMSE metric value of 1.10, MAE metric 0.24, MAPE metric 0.06%, and R2 metric 0.99.
Co-Authors Ardhana, Andini Aprilia Arief, Syachrul Dahsan, Sudarti Gumelar, Syahrul Fadholi Ikhsanudin, Agus Haryanto Nurdin, Fachdy Restianto, Mohammad Obie Supriyadi, Asep Adang Yahya Darmawan