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All Journal Sinergi Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA)
Dahsan, Sudarti
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Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Medicine & Pharmacology

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DIAGONALISASI DUA MATRIKS HERMITE SECARA SIMULTAN Nurman, Try Azisah; Aeni, Nur; Dahsan, Sudarti
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 7 No 1 (2019)
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (292.721 KB) | DOI: 10.24252/msa.v7i1.9880

Abstract

Matriks Kompleks merupakan matriks yang entri-entrinya bilangan kompleks. Matriks kompleks terdiri dari matriks hermite, matriks satuan (uniter) dan matriks normal. Tujuan dalam penelitian ini adalah untuk mendiagonalisasi dari dua matriks hermite secara simultan. Suatu matriks hermite  dan  terdiagonalisasi secara simultan jika . Langkah pertama mendiagonalisasi matriks hermite  adalah menentukan basis untuk masing-masing ruang eigen. Selanjutnya, menormalisasikan masing-masing basis bagi masing-masing ruang eigen, kemudian membentuk matriks  yang kolom-kolomnya adalah vektor-vektor basis. Untuk mendiagonalisasi matriks  dan  secara simultan menggunakan persamaan D1 = P*AP dan D2 = P*BP. Untuk matriks A dan B ordo 2 x 2 diperolehD1 dan D2. Untuk matriks A dan B ordo 3 x 3 diperoleh D1 dan D2.
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 Anggraini, Eca Indah Ardhana, Andini Aprilia Arief, Syachrul Ikhsanudin, Agus Haryanto NUR AENI Nurdin, Fachdy Restianto, Mohammad Obie Supriyadi, Asep Adang Yahya Darmawan