PERBANDINGAN JARAK POTRET DAN RESOLUSI KAMERA PADA TINGKAT AKURASI PENGENALAN ANGKA KWH METERMENGGUNAKAN SVM

Dini Amputri, Siti Nadra, Gasim Gasim, M. Ezar Al Rivan

Abstract


Electricity meter is a tool used to measure the electricity power consumption. Electricity meter has the numeral part known as electricity meter number which load the electricity power consumption.Usually, the recording is done once a month, by recording the number on the electric meter into the book, and then the recording officer take pictures the power meter. Electricity meter number can be analized through an image using the knowledge of pattern recognition in image processing. In analyzing picture of electicity meter number, we used Histogram Of Oriented Gradients (HOG) as the feature extraction and Supply Vector Machine (SVM) as the classification method. The result using 100 train-set and 30 test-set for each combination of category shows that the best resolution is 10 MP and 14 MP and the picture capturing distance is at 30 cm byand 10 cm by 73,33%  accuracy for each image and 86,67% for each number and confusion matrix shows that presentation of all number is 75,48%.

 Key words:Number Recognition, Histogram Of Oriented Gradients (HOG) , Support Vector Machine (SVM)


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References


Pujiharsono, H 2016, Sistem Otomasi Robust Berbasis Citra untuk Mendeteksi dan Mengenali Angka Pemakaian Energi Listrik pada kWh Meter Pascabayar, Universitas Gajah Mada, Yogyakarta.

Gunawan, R.,dkk 2014, Penerapan Optical Character Recognition (OCR) untuk Pembacaan Meteran Listrik PLN , INFORMATIKA, Vol.10, h. 127-134.

Ning, G 2013, Vehicle License Plate Detection and Recognition, University of Missouri, Columbia.

Banjare, K& Massey S 2016, Numeric Digit Classification Using HOG Feature Space and Multiclass Support Vectore Machine Classifier, International Journal of Scientific Research and Education, Vol.4, h. 5339-5345.

Lawgali, A 2016, Recognition of Handwritten Digits using Histogram of Oriented Gradients, International Journal of Advances Research in Science, Engineering and Technology, Vol. 3, h. 2359-2363.

Kamble, P & Hegadi R 2015, Handwrittem Marathi Character Recognition Using R-HOG Feature, International Conference on Advanced Computing Technologies and Applications, Procedia Computer Science 45, h. 266-274.

Ebrahimzadeh, R2014, Efficient Handwritten Digit Recognition Based on Histogram of Oriented Gradients and SVM, International Journal of Computer Applications, Vol.104, h. 10-13.




DOI: http://dx.doi.org/10.36982/jig.v8i1.218

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