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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika
MUHAMMAD HENDRI
Universitas Islam Indonesia
Electrical & Electronics Engineering Engineering

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Smoke and Fire Detection Base on Convolutional Neural Network ELVIRA SUKMA WAHYUNI; MUHAMMAD HENDRI
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 7, No 3 (2019): ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v7i3.455

Abstract

ABSTRAKDeteksi api dan asap adalah langkah pertama sebagai deteksi dini kebakaran. Deteksi dini kebakaran berdasarkan pemrosesan gambar dianggap mampu memberikan hasil yang efektif. Pilihan metode deteksi adalah kunci penting. Metode ekstraksi fitur berdasarkan analisis statistik dan analisis dinamis kadang-kadang memberikan akurasi kurang akurat dalam mendeteksi asap dan api, terutama pada deteksi asap, hal ini disebabkan oleh karakteristik objek asap yang transparan dan bergerak. Dalam penelitian ini, metode Convolutional Neural Network (CNN) diterapkan untuk deteksi asap dan api. Dari penelitian ini, diketahui bahwa CNN memberikan kinerja yang baik dalam deteksi kebakaran dan asap. Akurasi deteksi tertinggi diperoleh dengan menggunakan 144 data pelatihan, 20.000 iterasi dengan dropout.Kata kunci: Deteksi asap, deteksi kebakaran, Jaringan Syaraf Konvolusional ABSTRACTFire and smoke detection is the first step as early detection of fires. Early detection of fire based on image processing is considered capable of providing effective results. The choice of detection method is an important key. Feature extraction methods based on statistical analysis and dynamic analysis sometimes provide less accurate accuracy in detecting smoke and fire, especially on smoke detection, this is due to the characteristics of transparent and moving smoke objects. In this study, the Convolutional Neural Network (CNN) method was applied for smoke and fire detection. From this study, it is known that CNN provides good performance in fire and smoke detection. The highest detection accuracy is obtained by using 144 training data, 20,000 iterations and dropout is true.Keywords: Smoke detection, Fire detection, Convolutional Neural Network
Co-Authors Elvira Sukma Wahyuni