Deteksi Kecacatan Permukaan Rel Menggunakan Metode Deep Learning Neural Network

Teguh Arifianto, Sunaryo Sunaryo, Sunardi Sunardi, Akhwan Akhwan

Abstract


Rail is a construction in one unit made of steel, concrete, and other construction materials above or below the ground depending on the direction and area. The condition of the rail surface must not have any defects so that train travel is safe and comfortable. This research method discusses the research design. There are four processes in this research design, namely the data acquisition process, the RGB image color conversion process to HSI, the filtering process using the gabor filter, and the classification process using deep learning neural networks. The purpose of this study is to build a system to visually detect defects in the surface of the railroad tracks, namely image processing techniques. This activity was carried out at the Madiun Indonesian Railways Polytechnic Station Laboratory. Based on the research that has been done, it can be concluded that an image with a size of 32x32 pixels produces the highest accuracy value at epoch 90 using the gabor filter image type. The more epochs used, the better the results will be and the better the model can be made. The accuracy results obtained were 0.8041 or 80.41% for training accuracy and testing accuracy of 0.79 or 79%

Keywords


Disability Detection; Railways; Deep Learning Neural Networks

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References


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DOI: https://doi.org/10.29408/jit.v6i1.7415

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