Muzakkiy, Hamdi Ahmadi (2016) Deteksi Api Berbasis Sensor Visual Menggunakan Metode Support Vector Machines. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Kebakaran adalah salah satu bencana yang sering
terjadi. Penyebab sering terjadinya kebakaran yaitu karena
kelalaian manusia, dan hubungan arus pendek listrik. Bencana
kebakaran tidak hanya merusak bangunan bahkan
menimbulkan banyak korban. Saat ini banyak alat pendeteksi
api menggunakan sensor panas, ion, infrared. Namun
penggunaan sistem alarm ini tidak akan bekerja hingga partikel
mencapai sensor. Oleh karena itu diperlukan sistem deteksi api
yang dapat mendeteksi kebakaran dengan cepat.
Dalam Tugas Akhir ini diimplementasikan perangkat
lunak pendeteksi api menggunakan deteksi gerak, deteksi
warna menggunakan probabilitas warna, region growing,
ekstraksi fitur wavelet dan klasifikasi piksel menggunakan
support vector machines. Hasil dari deteksi bentuk akan
digunakan dalam proses penentuan api.
Dataset yang digunakan dalam proses uji coba berisi
enam puluh tujuh video dengan panjang video enam sampai
enambelas detik yang diambil dari berbagai sumber. Performa
terbaik yang dihasilkan adalah true positif sebesar 96.32%,
false positif sebesar 1.46% dan missing rate sebesar 2.23%
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Fire is one of the disasters that often occur. The cause
of frequent occurrence of fires are due to human negligence and
short-circuits. Fire does not only damaged buildings even cause
many victim. Currently many of fire detectors use heat sensors,
ion, and infrared. However, the use of this alarm system will not
work until the particles reach the sensor. Therefore, there has
to be a fire detection system that can detect fires quickly.
In this final project fire-detection software is
implemented using motion detection, color detection using
color probabilities, region growing, features extraction using
wavelet and pixel classification using support vector machines.
The Results from shape detection will be used in the process of
determining the fire.
The dataset used in the testing process contains sixtyseven
videos with a length of six to sixteen second video taken
from various sources. The resulting performance is the best at
96.32% true positive, 1.46% false positif and missing rate of
2.23%.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSIf 621.367 Muz d |
| Uncontrolled Keywords: | Deteksi Gerak, Deteksi Warna, Probabilitas, Wavelet, Support Vector Machines |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image processing--Digital techniques |
| Divisions: | Faculty of Information Technology > Informatics Engineering > 55201-(S1) Undergraduate Thesis |
| Depositing User: | Mr. Tondo Indra Nyata |
| Date Deposited: | 04 Oct 2017 08:41 |
| Last Modified: | 27 Dec 2018 03:03 |
| URI: | http://repository.its.ac.id/id/eprint/48918 |
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