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All Journal Sinergi Specta Journal of Technology
Ardhana, Andini Aprilia
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering

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The Impact of Increasing Sigma Value on the Performance of the Weapon Production Line at PT. X Fatmawati, Fatmawati; Sudiarso, Aries; Juprianto; Nugroh, Vicky Aditiyo; Ardhana, Andini Aprilia; Gultom, Rudy AG
SPECTA Journal of Technology Vol. 7 No. 3 (2023): SPECTA Journal of Technology
Publisher : LPPM ITK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35718/specta.v7i3.907

Abstract

PT X is one of the companies that produces the Main Armament System (Alutsista) used by the Indonesian National Army. In an effort to meet the production needs of Assault rifle weapons, PT. X must meet the quality standards set by the Ministry of Defense to support the operational tasks of the Indonesian National Army. This study aims to improve the performance of the weapon production line by measuring and evaluating the amount of sigma value in long-barreled weapons. The method used in this research is DMAIC (Define, Measure, Analyze, Improve, and Control), which is a structured approach to quality improvement. This method is used to address problems that occur on the weapon production line and analyze improvements that can be made to improve production performance. The steps recommended in this study include using the Critical to Quality method to identify the causes of problems that occur in weapon production. Furthermore, the Cause and Effect method is used to rank the most significant causes of problems based on the scores obtained. The 5W+1H method is also used to formulate appropriate improvement proposals based on the ranking results from the Cause and Effect Matrix. Thus, it is expected that an effective improvement proposal can be found to improve the performance of PT. X weapon production line.
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 Arief, Syachrul Dahsan, Sudarti Fatmawati Fatmawati Gultom, Rudy AG Ikhsanudin, Agus Haryanto Juprianto Nugroh, Vicky Aditiyo Nurdin, Fachdy Restianto, Mohammad Obie Sudiarso, Aries Supriyadi, Asep Adang Yahya Darmawan