Light Spectrum Speckle Analysis in Roughness Material Identification by Using Naïve Bayes Classifier Based Equalization Histogram Adaptive

Muhammad Arief Bustomi; Edwin Widya Utama; Endah Purwanti

doi:10.12962/j24604682.v19i3.18482

Light Spectrum Speckle Analysis in Roughness Material Identification by Using Naïve Bayes Classifier Based Equalization Histogram Adaptive

Muhammad Arief Bustomi, Edwin Widya Utama, Endah Purwanti

Abstract

Speckle imaging is a method that has been used in various fields. This method can be used to analyze the surface roughness of an object. Speckle imaging uses laser light and observes speckle patterns formed from light interference on the surface. The speckle imaging method is very safe and does not require any contact so it is easy to detect the roughness of an object. In this research, two types of sandpaper were used as rough surface objects. Speckle images of the sandpaper surface were created using three laser diodes with different wavelengths, namely 405 nm, 550 nm, and 650 nm. Image processing in this research begins with pre-processing methods, image segmentation, feature extraction, and then the classification process. The feature extraction process uses an Adaptive Histogram. The classification process uses the Naïve Bayes classifier method. Based on the research results, it was found that variations in the wavelength of the light spectrum affect the results of the Adaptive Histogram image features. The accuracy of Naïve Bayes classification increases if the wavelength used in creating the speckle image is shorter. Identification accuracy increased from 92% to 96% due to the use of speckle images resulting from diode laser irradiation from 650 nm to 405 nm.

Keywords

Histogram; Image processing; Light Spectrum; Roughness

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References

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DOI: http://dx.doi.org/10.12962%2Fj24604682.v19i3.18482