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Abstract

This study aims to provide recommendation from the result of the comparison artificial neural network between backpropagation and learning vector quantization (LVQ) methods in pattern recognition. These methods are often used for pattern recognition applications, because these two methods are able to grouping patterns into pattern classes and included in supervised learning methods. In this study will be proven backpropagation and LVQ are able to recognize the pattern of two-dimentional figure geometry shapes and show which method better in pattern recognition. Implementation of backpropagation and learning vector quantization (LVQ) is using Matlab v8.5. The first thing is doing image processing process there is grayscalling and thresholding process to get the bineryzation value that will be used as the input value on ANN. After that the input value will be processed in ANN method backpropagation and LVQ. From the result of the implementation of the testing these methods, obtained accuracy is 91.43% for backpropagation and 65.71% for learning vector quantization

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How to Cite
Hendriyani, Y. (2020). Perbandingan Algoritma Backpropagation Dan Learning Vector Quantization (LVQ) dalam Pengenalan Pola Bangun Ruang Geometri. INVOTEK: Jurnal Inovasi Vokasional Dan Teknologi, 20(2). https://doi.org/https://doi.org/10.24036/invotek.v20i2.746

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