Judul: Lokalisasi Plat Nomor pada Kendaraan di Indonesia dengan Metode Dasar Vertical Egde Analysis.
Abstrak: Localization of license plate is a first stage in vehicle license plate recognition system. License plate localization affects the results of license plate recognition. In this research, the vertical edge analysis became the basic method of the license plate localization. First, pre-processing on the image is performed to obtain a better image quality. Secondly, the vertical edge detection on the image performed using the Sobel operator. Thirdly, candidate number plate detection is done by using a sliding window. This phase is divided into two processes: coarse detection and fine detection. At this stage the edge density will be calculated by using the concept of integral edge image. To separate the actual plate with objects that resemble plates, edge density calculation is performed in certain parts of the license plate candidates. Finally, the candidates are verified by counting the number of vertical pixels that exist on the license plate candidates. Of testing 70 cars image data, a recall value of 86.17% and a precision value of 60.75% are obtained.
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Judul: Identifikasi Plat Nomor Kendaraan Dengan Zone Based Feature Extraction Menggunakan Metode Klasifikasi Bacpropagation
Abstrak: Automatic license plate identification is one of the important features required for vehicle data recording system to be use in applications such as parking system, automatic highway gate system, etc. Several research has been conducted to devise a reliable method to identify vehicle license plate. In this research, we aim to implement an automatic license plate identification system using the zone based feature extraction method and artificial neural network for classification. The data is obtained from 100 units of vehicle using a 5 MP mobile phone camera. The preprocessing step consists of converting the images to grayscale, followed by noise reduction using median filter, edge detection using Canny with a threshold of 0,2 and 0,5. Afterwards, we perform segmentation using 8-connected labelling component to obtain the characters. The zone based feature extraction used is image centroid and zone using the most efficient zone. The fastest and the highest accuracy will be choosen as the most efficient zone. In this research 14 zone extraction have an efficient result. We used the backpropagation neural network with 25 input neurons, 30 hidden neurons, and 36 output neurons representing each characthers (alphabet and numerals). The best result for indvidual character recognation is 85,32% while the best recognition rate for the whole plate is 40,61%
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Judul: Nutritional Properties of Cocoa Pods as Ruminant Feedstuff
Abstrak: An attempt to increase cocoa pods quality feed for ruminant have been done using fermentation techniques with or without urea addition. Five treatments (Fresh cocoa pod = C; fermentation with addition urea 0 g/kg = U0 or ensilage; 10 g/kg = U1; 20 g/kg = U2 and 30 g/kg fresh substances of cocoa pods) have been examined for their effect on pH, proximate composition, N-fractions, amino acid compositions, cell wall constituents, anti nutritive compounds, in vitro gas production (Gb), estimate organic matter digestibility (OMD) and metabolizable energy (ME) of cocoa pods. Ensilage (U0) improve feed values of cocoa pods by weakening the lingo-cellulose or –hemicellulose bonds, resulting in higher in vitro gas production as well as higher OMD and ME contents compare to fresh cocoa pods. The protein value of cocoa pods, expressed as true protein (TP) or non ammonia nitrogen (NAN) minus urea nitrogen (UR) was also improved. Addition of urea affected the composition of the fibre fraction as to the proportion of NDF, ADF and ADL and the content of TP was slightly increased. Urea treatment increased Gb, OMD and ME compared to ensiled or fresh cocoa pods. The optimum level of urea for maximum Gb was 6.7 g/100 g DM cocoa pods. The optimum level to reach maximum OMD and ME were also reached at urea level 6.5 – 6.7 g/100 g DM cocoa pods.
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