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Klasifikasi Jenis Kendaraan Menggunakan Metode Extreme Learning Machine

Rispani Himilda, Ragil Andika Johan
JTIM : Jurnal Teknologi Informasi dan Multimedia , Volume 2, pp 237-243; doi:10.35746/jtim.v2i4.118

Abstract: The number of vehicles in Indonesia has increased each year, both two-wheeled and four-wheeled vehicles; this is inversely proportional to the development of road infrastructure in Indonesia, which has not experienced much change or improvement. Supposedly, with the increase in the number of vehicles, road infrastructure must also keep pace so that things such as the accumulation of cars on the road do not occur, traffic accidents and congestion become obstacles to carrying out activities. Therefore, it is necessary to make a system to detect and classify vehicles' types in this study using two types of vehicles, namely cars and motorbikes. According to the Indonesian Central Statistics Agency (BPS), it is the highest number. The classification system uses digital image processing techniques, a science to study how an image is formed, processed, and analyzed by a computer to produce information that humans can understand. The method used in this research is the Extreme Learning Machine (ELM), a part of artificial intelligence in feedforward neural networks, where this method can solve regression and classification problems. The data used in this study are 25 images of cars and motorbikes as training data and 15 photos of cars and motorbikes as test data, respectively. The results obtained from this study are a system for classifying two types of vehicles, namely cars and motorbikes, with an accuracy rate of 86.6%.
Keywords: neural / Indonesia / extreme learning machine / road / Motorbikes / Wheeled

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