scholarly journals UJI AKURASI KLASIFIKASI DAN VALIDASI DATA PADA PENGGUNAAN METODE MEMBERSHIP FUNCTION DAN ALGORITMA C4.5 DALAM PENILAIAN PENERIMA BEASISWA

2018 ◽  
Vol 9 (1) ◽  
pp. 565-578
Author(s):  
Ade Surya Budiman ◽  
Xanty Adhi Parandani
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Membership Function ◽  
Cross Validation ◽  
Mean Square ◽  
Fold Cross Validation

Data yang dikumpulkan dalam proses seleksi penerima beasiswa memiliki variabel berbentuk nominal dan numerik serta terdiri atas banyak item, sehingga diperlukan metode yang tepat untuk melakukan klasifikasi data secara akurat dan memastikan validitas data yang dipergunakan dalam penilaian penerima beasiswa. Dalam penelitian ini dilakukan pengujian terhadap data dengan dua metode, yaitu (i) Integrasi Membership Function dengan Algoritma C4.5, dan (ii) Penerapan Algoritma C4.5 secara langsung pada data. Pengujian dilakukan dengan jenis 10-fold cross validation dan pengujian kebenaran instances (correctly/incorrectly). Dari hasil perhitungan dan pengujian diperoleh nilai Mean Square Error (MAE) dan Root Mean Square Error (RMSE) yang lebih rendah pada metode Integrasi Membership Function dengan Algoritma C4.5, yaitu masing-masing 0,132 (MAE) dan 0,2714 (RMSE). Untuk pengujian Classified Instances, diperoleh persentase kebenaran (correctly) yang lebih tinggi pada metode Integrasi Membership Function dengan Algoritma C4.5, yaitu sebesar 92,8571%.Kata kunci: klasifikasi data, validitas data, membership function, algoritma C4.5, seleksi beasiswa.

Quantitative Analysis of Forsythiae fructus by Near Infrared Spectroscopy

2013 ◽  
Vol 807-809 ◽  
pp. 1967-1971
Author(s):  
Yan Bai ◽  
Xiao Yan Duan ◽  
Hai Yan Gong ◽  
Cai Xia Xie ◽  
Zhi Hong Chen ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Ethanol Extract ◽  
Mean Square ◽  
Validation Set

In this paper, the content of forsythoside A and ethanol-extract were rapidly determinated by near-infrared reflectance spectroscopy (NIRS). 85 samples of Forsythiae Fructus harvested in Luoyang from July to September in 2012 were divided into a calibration set (75 samples) and a validation set (10 samples). In combination with the partical least square (PLS), the quantitative calibration models of forsythoside A and ethanol-extract were established. The correlation coefficient of cross-validation (R2) was 0.98247 and 0.97214 for forsythoside A and ethanol-extract, the root-mean-square error of calibration (RMSEC) was 0.184 and 0.570, the root-mean-square error of cross-validation (RMSECV) was 0.81736 and 0.36656. The validation set were used to evaluate the performance of the models, the root-mean-square error of prediction (RMSEP) was 0.221 and 0.518. The results indicated that it was feasible to determine the content of forsythoside A and ethanol-extract in Forsythiae Fructus by near-infrared spectroscopy.

Multivariate analysis of three chemometric algorithms on rapid prediction of some important quality parameters of crude shea butter using Fourier transform-near infrared spectroscopy

2019 ◽  
Vol 27 (3) ◽  
pp. 220-231
Author(s):  
Emmanuel Amomba Seweh ◽  
Zou Xiaobo ◽  
Feng Tao ◽  
Shi Jiachen ◽  
Haroon Elrasheid Tahir ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Cross Validation ◽  
Partial Least Square ◽  
Least Square ◽  
Mean Square ◽  
Saponification Value ◽  
Peroxide Values

A comparative study of three chemometric algorithms combined with NIR spectroscopy with the aim of determining the best performing algorithm for quantitative prediction of iodine value, saponification value, free fatty acids content, and peroxide values of unrefined shea butter. Multivariate calibrations were developed for each parameter using supervised partial least squares, interval partial least squares, and genetic-algorithm partial least square regression methods to establish a linear relationship between standard reference and the Fourier transformed-near infrared predicted. Results showed that genetic-algorithm partial least square models were superior in predicting iodine value and saponification value while partial least squares was excellent in predicting free fatty acids content and peroxide values. The nine-factor genetic-algorithm partial least square iodine value calibration model for predicting iodine value yielded excellent ( R2 cal = 0.97), ( R2 val = 0.97), low (root mean square error of cross-validation = 0.26), low (root mean square error of Prediction = 0.23), and (ratio of performance to deviation = 6.41); for saponification value, the nine-factor genetic-algorithm partial least square saponification value calibration model had excellent R2 cal (0.97), R2 val (0.99); low root mean square error of cross-validation (0.73), low root mean square error of Prediction (0.53), and (ratio of performance to deviation = 8.27); while for free fatty acids, the 11-factor partial least square free fatty acids produced very high R2 cal (0.97) and R2 val (0.97) with very low root mean square error of cross-validation (0.03), low root mean square error of Prediction (0.04) and (ratio of performance to deviation = 5.30) and finally for peroxide values, the 11-factor partial least square peroxide values calibration model obtained excellent R2 cal (0.96) and R2val (0.98) with low root mean square error of cross-validation (0.05), low root mean square error of Prediction (0.04), and (ratio of performance to deviation = 5.86). The built models were accurate and robust and can be reliably applied in developing a handheld quality detection device for screening, quality control checks, and prediction of shea butter quality on-site.

Rapid Determination of Docosahexaenoic Acid in Powdered Oil by Near-Infrared Spectroscopy

2010 ◽  
Vol 16 (2) ◽  
pp. 187-193 ◽  
Author(s):  
Yang Meiyan ◽  
Li Jing ◽  
Nie Shaoping ◽  
Hu Jielun ◽  
Yu Qiang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Docosahexaenoic Acid ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Mean Square ◽  
Validation Set

Near-infrared spectroscopy (NIRS) was used as a rapid and nondestructive method to determine the content of docosahexaenoic acid (DHA) in powdered oil samples. A total of 82 samples were scanned in the diffuse reflectance mode by Nicolet 5700 FTIR spectrometer and the reference values for DHA was measured by gas chromatography. Calibration equations were developed using partial least-squares regression (PLS) with internal cross-validation. Samples were split in two sets, one set used as calibration (n = 66) whereas the remaining samples (n=16) were used as validation set. Two mathematical treatments (first and second derivative), none (log(1/R)) and standard normal variate as scatter corrections and Savitzky—Golay smoothing were explored. To decide upon the number of PLS factors included in the PLS model, the model with the lowest root mean square error of cross-validation (RMSECV=0.44) for the validation set is chosen. The correlation coefficient (r) between the predicted and the reference results which used as an evaluation parameter for the models is 0.968. The root mean square error of prediction of the final model is 0.59. The results reported in this article demonstrate that FT-NIR measurements can serve as a rapid method to determine DHA in powdered oil.

scholarly journals CHEMOMETRIC ASSISTED QUANTITATIVE DETERMINATION OF VITEXIN IN ETHANOLIC EXTRACT OF BINAHONG (ANREDERA CORDIFOLIA (TEN.) STEENIS) LEAVES

2020 ◽  
pp. 69-75
Author(s):  
OCTAVIANUS BUDI SANTOSA ◽  
MICHAEL RAHARJA GANI ◽  
SRI HARTATI YULIANI
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Cross Validation ◽  
Multivariate Calibration ◽  
Data Sets ◽  
Calibration Data ◽  
Mean Square ◽  
Calibration Models ◽  
Pcr Method

Objective: The objective of this study was to develop a UV spectroscopy method in combination with multivariate analysis for determining vitexin in binahong (Anredera cordifolia (Ten.) Steenis) leaves extract. Methods: The partial least square (PLS) regression and the principal component regression (PCR) was performed in this study to evaluate several statistical performances such as coefficient of determination (R2), root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and relative error of prediction (REP). Cross-validation in this study was performed using leave one out technique. Results: The R2 values of calibration data sets resulted from PLS ​​and PCR method were 0.9675 and 0.9648, respectively. The low values of RMSEC and RMSECV both for PLS ​​and PCR method indicated the minimum error of the calibration models. The R2 values of validation data sets resulted from PLS ​​and PCR method were 0.9778 and 0.9820, respectively. The low values of RMSEP both for PLS ​​and PCR method indicated the minimum error of prediction generated from the calibration data sets. Multivariate calibration techniques were applied to determine the content of vitexin in binahong leaves extract. Predicted values from the multivariate calibration models were compared to the actual values determined from a validated HPLC method. It was found that PLS models resulted in the lowest REP values compared to the PCR models. Conclusion: The chemometrics technique can be applied as an alternative method for determining vitexin levels in the ethanol solution of binahong leaves extract.

A Quantitative Approach to Measure Webpage Aesthetics

2020 ◽  
Vol 16 (2) ◽  
pp. 53-68
Author(s):  
Ranjan Maity ◽  
Samit Bhattacharya
Keyword(s):  
Root Mean Square Error ◽  
Prediction Model ◽  
Computational Model ◽  
Support Vector Regression ◽  
Mean Square Error ◽  
Cross Validation ◽  
Support Vector ◽  
Mean Square ◽  
Validation Technique ◽  
Fold Cross Validation

Aesthetics measurement is important in determining and improving the usability of a webpage. Wireframe models, the collection of the rectangular objects, can approximate the size and positions of the different webpage elements. The positional geometry of these objects is primarily responsible for determining aesthetics as shown in studies. In this work, the authors propose a computational model for predicting webpage aesthetics based on the positional geometry features. In this study, the authors found that ten out of the thirteen reported features are statistically significant for webpage aesthetics. Using these ten features, the authors developed a computational model for webpage aesthetics prediction. The model works on the basis of support vector regression. The authors rated the wireframe models of 209 webpages by 150 participants. The average users' ratings and the ten significant features' values were used to train and test the aesthetics prediction model. Five-fold cross-validation technique shows the model can predict aesthetics with a Root Mean Square Error (RMSE) of only 0.42.

scholarly journals NIR assignment of isopsoralen by 2D-COS technology and model application in Yunkang Oral Liquid

2015 ◽  
Vol 08 (06) ◽  
pp. 1550023 ◽  
Author(s):  
Yanling Pei ◽  
Zhisheng Wu ◽  
Xinyuan Shi ◽  
Xiaoning Pan ◽  
Yanfang Peng ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Least Squares ◽  
Partial Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Cross Validation ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Oral Liquid ◽  
Spectral Assignment

Near infrared (NIR) assignment of Isopsoralen was performed using deuterated chloroform solvent and two-dimensional correlation spectroscopy (2D-COS) technology. Yunkang Oral Liquid was applied to study Isopsoralen, the characteristic bands by spectral assignment as well as the bands by interval partial least squares (iPLS) and synergy interval partial least squares (siPLS) were used to establish partial least squares (PLS) model. The coefficient of determination in calibration [Formula: see text] were 0.9987, 0.9970 and 0.9982. The coefficient of determination in cross validation [Formula: see text] were 0.9985, 0.9921 and 0.9982. The coefficient of determination in prediction [Formula: see text] were 0.9987, 0.9955 and 0.9988. The root mean square error of calibration (RMSEC) were 0.27, 0.40 and 0.31 ppm. The root mean square error of cross validation (RMSECV) were 0.30, 0.67 and 0.32 ppm. The root mean square error of prediction (RMSEP) were 0.23, 0.43 and 0.22 ppm. The residual predictive deviation (RPD) were 31.00, 16.58 and 32.41. It turned out that the characteristic bands by spectral assignment had the same results with the chemometrics methods in PLS model. It provided guidance for NIR spectral assignment of chemical compositions in Chinese Materia Medica (CMM).

scholarly journals An Innovative Liquid Biosensor for the Detection of Lipid Molecules Involved in Diseases of the Nervous System

Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 760 ◽  
Author(s):  
Simone Grasso ◽  
Marco Santonico ◽  
Tiziana Bisogno ◽  
Giorgio Pennazza ◽  
Alessandro Zompanti ◽  
...  
Keyword(s):  
Nervous System ◽  
Root Mean Square Error ◽  
Peripheral Nervous System ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Cross Validation ◽  
Mean Square ◽  
Pathological Conditions ◽  
Novel Approach

Growing evidence suggests that endocannabinoid levels are modulated during pathological conditions affecting both central and peripheral nervous system. In the present study, a novel approach (patent pending) based on an innovative liquid biosensor has been used to analyze two relevant endocannabinoid molecules with calibration purposes: N-arachidonoylethanolamine and 2-arachidonoylglycerol. The system was able to predict both compound concentrations with a Root Mean Square Error in Cross Validation (RMSECV) of 6.61 nM and 23.50 nM, respectively.

scholarly journals Carotenoid determination in tomato juice using near infrared spectroscopy

2015 ◽  
Vol 29 (3) ◽  
pp. 275-282 ◽  
Author(s):  
Konrád Deák ◽  
Tamás Szigedi ◽  
Zoltán Pék ◽  
Piotr Baranowski ◽  
Lajos Helyes
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Cross Validation ◽  
Diode Array ◽  
Mean Square ◽  
Least Squares Regression ◽  
Carotene Content

AbstractA rapid non-destructive method for profiling tomato carotenoids was developed using NIR spectrometry. One hundred and twenty tomato samples were produced at the Experimental Farm of Szent István University in Gödöllő (Hungary). The sample preparation was based on homogenization. The mixed samples were scanned with a diode array Perten DA7200 NIR Analyzer (950-1650 nm) and analyzed by high performance liquid chromatography. The calibration was based on partial least squares regression with cross-validation. The performance of the final model was evaluated according to root mean square error of cross-validation. The results indicate that the main carotenoid components were accurately predicted. The correlation between the NIR measurement and the β-carotene content of tomatoes was adequately high [R2CV = 0.89; root mean square error of cross-validation = 0.174 μg g−1]. The NIR method was also performed for the determination of the all-trans lycopene content (R2CV = 0.75; root mean square error of cross-validation = 6.88 μg g−1). It can be concluded that the diode array NIR spectrometer has the potential to be used for the determination of the main carotenoids of tomatoes.

scholarly journals Rapid Analysis of Adulterated Dexamethasone in Joint-Pain Killer Traditional Herbal Medicine (THM) Using Infrared Spectroscopy

2018 ◽  
Vol 18 (2) ◽  
pp. 137-145
Author(s):  
Ardi Nugroho ◽  
Fany Devita Ritonga
Keyword(s):  
Infrared Spectroscopy ◽  
Herbal Medicine ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Joint Pain ◽  
Cross Validation ◽  
Partial Least Square ◽  
Mean Square ◽  
Traditional Herbal Medicine

A rapid, non-destructive and reagent-free infrared spectroscopy combined with Partial Least Square (PLS) has been developed for the dexamethasone quantification in joint-pain killer traditional herbal medicine (THM). The main aim of this study is to select the best wavenumbers that are capable of providing the high coefficient of determination (R2), low values of Root Mean Square Error of Calibration (RMSEC), Root Mean Square Error of Cross Validation (RMSECV)  and predictive residual error sum of squares (PRESS). Finally, wavenumbers 3646, 3642, 2461, 2453, 2432, 2406, 2229, 2209, 2197, 2097, 2092, 2064, 2059, 2047, 2026, 2009, 1969, and 1513 cm-1 were selected for the prediction of dexamethasone in the joint-pain killer traditional herbal medicine. The correlation between the actual values of dexamethasone determined in joint-pain killer traditional herbal medicine using infrared spectroscopy combined with PLS revealed the R2 values of 0.9988. The RMSEC values obtained 0,009455. The PRESS and RMSECV value obtained as the results of cross-validation model selection for dexamethasone in joint-pain killer traditional herbal medicine were 0,0022721.00 and 0,02902, respectively. The high value of R2 and low value of RMSEC, RMSECV and PRESS indicated that this method had high accuracy and precision in a validated condition for the dexamethasone quantification in the joint-pain killer traditional herbal medicine. These results indicated that infrared spectroscopy combined with PLS can be an alternative method for the dexamethasone determination in joint-pain killer traditional herbal medicine.his part contains English version of the abstract. The abstract presents background, method of the research/ literary study and discussion. The abstract consist of maximum 300 words. All sentence must represent the core of research presented in good structure of sentences.

Near infrared spectroscopy as an alternative method for rapid evaluation of toluene swell of natural rubber latex and its products

2018 ◽  
Vol 26 (3) ◽  
pp. 159-168 ◽  
Author(s):  
Chin Hock Lim ◽  
Panmanas Sirisomboon
Keyword(s):  
Thin Film ◽  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Thick Film ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Mean Square

Toluene swell or equilibrium swelling is universally used by rubber factories to measure the degree of crosslink of their compounded or prevulcanized latices at different stages of production. To apply near infrared spectroscopy for rapid and accurate quality control, spectral acquisition of prevulcanized latex, thin film and thick film was performed using a Fourier transform near infrared spectrometer in diffuse reflection mode across the wavenumber range of 12,500–3600 cm−1. For prevulcanized latex an effective model was developed using partial least squares regression with preprocessing (first derivative + straight line subtraction method). The coefficient of determination (r2), root mean square error of cross validation and bias of the validation set were 0.71, 3.93% and −0.005%, respectively. For the thin film model the r2, root mean square error of cross validation and bias were 0.65, 4.01% and −0.028%, respectively. Whereas for the thick film model the r2, root mean square error of cross validation and bias were 0.70, 4.00% and −0.006%, respectively. Three models including prevulcanized latex, thin film and thick film were validated by 23 unknown samples, providing standard error of prediction and bias of 5.357 and 2.494, 4.565 and 1.001 and 3.641 and −0.961%, respectively, for prevulcanized latex, thin film and thick film. The model developed for the thick film spectra gave the best results.

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