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Muhammad Arief Bustomi Muhammad Arief Bustomi
Department of Physics, Institut Teknologi Sepuluh Nopember, Kampus ITS-Sukolilo Surabaya
Physics

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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 Muhammad Arief Bustomi Muhammad Arief Bustomi; Edwin Widya Utama Edwin Widya Utama; Endah Purwanti Endah Purwanti
Jurnal Fisika dan Aplikasinya Vol 19 No 3 (2023): October 2023 Edition
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat, LPPM-ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24604682.v19i3.3083

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.
Characteristics Analysis of the Archimedes Screw Turbine Micro-hydro Power Plant with Variation of Turbine Elevation Angle: Bachtera Indarto, Afif Mahrus Kurnia Putra, and Muhammad Arief Bustomi Bachtera Indarto Bachtera Indarto; Afif Mahrus Kurnia Putra Afif Mahrus Kurnia Putra; Muhammad Arief Bustomi Muhammad Arief Bustomi
Jurnal Fisika dan Aplikasinya Vol 17 No 2 (2021): June 2021 Edition
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat, LPPM-ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24604682.v17i2.8983

Abstract

Electrical energy has become a basic need of modern society, but the supply of electricity has not yet fully met the needs of the community. Many potential energy sources can be converted into electrical energy. One potential of energy sources is water flow which can be used for micro-hydro power plants in remote areas. This study aims to analyze the characteristics of the Archimedes screw turbine of micro-hydro power plant, in particular concerning on variations in the elevation angle of the turbine. Characteristics determination was carried out on a prototype of a micro-hydro power plant using a Brush Less Direct Current (BLDC) generator and two blades of Archimedes screw turbine. The varied turbine elevation-angle was applied, namely 20, 25, 30, 35, and 40o, with a water discharge of 2.64 L/s. The results show that an increase in the turbine elevation angle from 20 to 40o causes an enhancement in mechanical characteristics, i.e. torque and rpm, as well as electrical characteristics, i.e. voltage, current, and electric power. The mechanical and electrical characteristics reach a maximum value at the elevation angle of about 30o, which is 27.87 mW at 1 k O load. Furthermore, increasing the elevation angle to become more than 30o causes a decrease in mechanical and electrical characteristics.
Keunggulan Ekstraksi Fitur Ordo Kedua terhadap Ordo Pertama untuk Identifikasi Ciri Berbasis Tekstur Warna: Arief Bustomi Muhammad Arief Bustomi Muhammad Arief Bustomi
Jurnal Fisika dan Aplikasinya Vol 15 No 3 (2019): October 2019 Edition
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat, LPPM-ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24604682.v15i3.5311

Abstract

The feature extraction method is done by first converting RGB images into grayscale images. Based on the grayscale image, two different processing methods are carried out, namely first order feature extraction and second order feature extraction. The extraction features of the first order used 5 characteristic parameters, namely Mean, Variance, Skewness, Kurtosis, and Entropy, while the extraction features of the second order used 6 characteristic parameters, namely Angular Second Moment, Contrast, Correlation, Standard Deviation, Inverse Difference Moment and Homogenity. The 11 characteristic parameters will then be classified using the LVQ artificial neural network method to find the final weight used as the reference weight for characteristics identification based on color texture. In this research, 30 image samples were used (15 image samples in category A and 15 image samples in category B) which were divided into 16 image samples for training and 14 image samples for testing. The results of the research analysis show that the second order feature extraction method is more reliable than the first order feature extraction method.
Co-Authors Afif Mahrus Kurnia Putra Afif Mahrus Kurnia Putra Bachtera Indarto Bachtera Indarto Edwin Widya Utama Edwin Widya Utama Endah Purwanti Endah Purwanti