Studi Perbandingan Konsentrasi Klorofil-a Di Semenanjung Blambangan Kabupaten Banyuwangi Menggunakan Data Citra Satelit Aqua Modis

Zainab, Siti; Wibisana, Hendrata; Casita, Cintantya Budi

doi:10.24895/jig.2019.25-1.889

Cited by 1 publication

(1 citation statement)

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“…It is found that the cubic polynomial regression model is the best for predicting texture values, water content, total dissolved solids, and total acid with correlation coefficients of 0.92, 0.85, 0.99, and 0.32, respectively. In Zainab et al [9], the relationship between RGB color and chlorophyll-a content was found; it is the best equation in the form of an exponential equation with a correlation coefficient of 0.767. In addition, in other studies [10], [11] studied the relationship between RGB colors for the distribution of solid materials, and the best equation was obtained, i.e., the exponential equation with a correlation coefficient of 0.9336 and 0.9075, respectively.…”

Section: Introductionmentioning

confidence: 99%

Digital Image Processing for Height Measurement Application Based on Python OpenCV and Regression Analysis

Abadi

Tahcfulloh²

2022

JOIV : Int. J. Inform. Visualization

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Pixel is the smallest element given by the image from a digital camera and is used as a data source in the digital image processing process. In this paper, two data collection processes are carried out, i.e. taking actual height data using a standard stature meter and taking sample photos using a camera placed from the sample with a distance of 160 cm and a height of 100 cm. The sample photos obtained are then processed for segmentation of the sample body against the surrounding environment using several digital image-processing techniques such as grayscale, blur, edge detection, and bounding box in order to obtain a pixel value that represents the height of the sample. The next stage is the regression analysis process by correlating actual height with pixel height using five regression equation analysis methods such as least squares, logarithmic powers, exponentials, quadratic polynomials, and cubic polynomials. This study analyzes the differences between these methods in terms of correlation coefficient, Root Mean Squared Error (RMSE), average error, and accuracy between height calculation data based on digital image processing and actual height measurement data. From the results obtained, the logarithmic power method produces the best analytical value compared to other methods with the correlation coefficient, RMSE, average error percentage, and percentage accuracy of 0.976, 1.3, 0.58%, and 99.42%, respectively. While the cubic polynomial is in the last position, the correlation coefficient, RMSE, average error percentage, and accuracy percentage are 0.978, 1.41, 0.64%, and 99.36%, respectively.

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Section: Introductionmentioning

confidence: 99%