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All Journal Teknika: Jurnal Sains dan Teknologi JURNAL NASIONAL TEKNIK ELEKTRO
Muchtar, Kahlil
Universitas Syiah Kuala
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Perbandingan Kinerja Support Vector Machine (SVM) Dalam Mengenali Wajah Menggunakan SURF DAN GLCM Bahri, Syamsul; Saddami, Khairun; Arnia, Fitri; Muchtar, Kahlil
JURNAL NASIONAL TEKNIK ELEKTRO Vol 8, No 2: July 2019
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (524.644 KB) | DOI: 10.25077/jnte.v8n2.620.2019

Abstract

Face recognition is one part of the biometrics research. Face recognition is widely used in identification and recognition process. Speed-up Robust Feature (SURF) is one of feature extraction method used in face recognition system. This research aims to compare face recognition performance between SURF and Gray Level Co-occurence Matrix (GLCM) methods for perspective rotation. In this study, the image features were extracted using SURF and GLCM. Each feature was used on classification stage using Support Vector Machine (SVM). The dataset was obtained from National Cheng Kung University (NCKU). The NCKU dataset has more variation of rotation angle. The dataset used in this study consists of 10 classes that showed 10 of the subject. The results show that SURF method obtained 85% of accuracy and GLCM method reached 50% of accuracy. Therefore, we concluded that SURF method has better performance on implementing on face recognition system.Keywords : SURF, GLCM, Face Recognition, SVM Abstrak Pengenalan wajah merupakan salah satu bagian dari penelitian biometrika. Pengenalan wajah banyak digunakan dalam proses identifikasi manusia. Metode ekstraksi fitur Speed-Up Robust Feature (SURF) merupakan salah satu metode yang digunakan untuk mengenali wajah. Penelitian ini bertujuan untuk membandingkan kinerja sistem pengenalan wajah dengan menggunakan metode ekstraksi fitur SURF dan Gray Level Co-occurence Matrix (GLCM). Pada penelitian ini, data input wajah akan diekstraksi fiturnya menggunakan SURF dan GLCM. Setiap fitur digunakan pada tahapan klasifikasi menggunakan Support Vector Machine (SVM). Data yang digunakan merupakan data yang didapatkan dari National Cheng Kung University (NCKU). Data wajah NCKU mempunyai sudut rotasi yang lebih banyak. Dataset yang digunakan pada penelitian ini terdiri dari 10 kelas yang menunjukkan 10 subjek penelitian. Pengenalan wajah menggunakan metode SURF dan SVM mempunyai akurasi 85%, sedangkan menggunakan metode GLCM mempunyai akurasi 50%. Hasil menunjukkan bahwa metode SURF mempunyai kinerja yang lebih baik dari metode GLCM.Kata Kunci : SURF, GLCM, pengenalan wajah, SVM
Design and development of aircraft cargo fire early detection simulation system using arduino nano microcontroller Chitraningrum, Nidya; Siswanti, Sri Dessy; Herdiana, Dina; Prasetiawan, Anton; Banowati, Lies; Muchtar, Kahlil; Sakti, Indra
Jurnal Teknika Vol 20, No 1 (2024): Available Online in June 2024
Publisher : Faculty of Engineering, Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62870/tjst.v20i1.22544

Abstract

Fire in the aircraft cargo can cause dangerous damage to aircraft systems during flight. To prevent aircraft cargo fires, the fire early detection system must be built. In this work, we design and develop the fire early detection simulation system for aircraft cargo using Arduino nano microcontroller. The aircraft cargo is prone to fire due to the load of any type of goods such as dangerous goods, flammable stuff, or liquids, etc. This paper simulates the fire that occurs in artificial aircraft cargo and designs the detection and extinguisher prototypes using three kind sensors: flame, smoke, and temperature sensors combined with SMS gateway for user notification.  We used SIM800L GSM module as communication tools to send and achieve data through short messages service (SMS) between security system and cellphones. Three sensors including flame, smoke, and temperature sensors were used as the warning indication to the hardware. When the sensors detect the fire and smoke, the red LED, buzzer, and vacuum pump will be on active mode, and SMS notification will be delivered immediately to the user’s cell phone. As the fire has been extinguished by the vacuum pump, the red LED, buzzer, and vacuum pump return to standby mode and the fire warning alarm system will turn off. This research is successfully developing the fire early detection simulation systems that can be applied in real aircraft cargo.
Co-Authors Arnia, Fitri Bahri, Syamsul Banowati, Lies Chitraningrum, Nidya Herdiana, Dina Prasetiawan, Anton Saddami, Khairun Sakti, Indra Siswanti, Sri Dessy