DETEKSI SEBARAN TITIK API PADA KEBAKARAN HUTAN GAMBUT MENGGUNAKAN GELOMBANG-SINGKAT DAN BACKPROPAGATION (STUDI KASUS KOTA DUMAI PROVINSI RIAU)

HANDAYANI, TRI (2014) DETEKSI SEBARAN TITIK API PADA KEBAKARAN HUTAN GAMBUT MENGGUNAKAN GELOMBANG-SINGKAT DAN BACKPROPAGATION (STUDI KASUS KOTA DUMAI PROVINSI RIAU). S2 thesis, UAJY.

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Abstract

Peat moss forest fire is one of damaging disasters. It disturbs people’s activity and health and also reduces ecological livings; thus, this disaster becomes attention of large society either nationally or internationally. Peat moss forest fire can be identified by remote sensing technology. The development of remote sensing technology by using MODIS imaging satellite so far has been used in many fields and one of them is in controlling forest fire multitemporally. Hotspots of information obtained from the MODIS image processing can be obtained by image feature extraction using a wavelet , so that the results of this extraction can be input in the process of detection of hotspots using Artificial Neural Networks . In this research data for surface temperature used Terra MODIS satellite by taking an advantage of canal 31 and 32 and using Coll’s, et.al (1994) algorithm, The data of forest fire were from forest fire service of Dumai City, for feature extraction using a wavelet Haar , Coiflet1 and Symlet5, and backpropagation neural networks to recognize patterns hotspot. This research was conducted in Dumai City Province of Riau. Result of this research shows that the use of Canal 31 and 32 through Terra MODIS imaging satellite can be used for detecting points of fire which is found that there are more then 17 points of fire within temperature ranges of 270C – 320C, While the results of the Pattern Recognition hotspots using short - wave and Backpropagation Network with image input in the form of satellite images with 8 bit and size 512 x 512 derived from satellite data turned out to give good results with a performance of 100 % on the image of the wavelet decomposition Haar and Coiflet 1 , while for wavelet Symlet5 gives performance by 40 % .

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