scholarly journals Aplikasi Analisis Multivariat Berdasarkan Warna untuk Memprediksi Brix dan pH pada Pisang

2017 ◽  
Vol 37 (1) ◽  
pp. 109 ◽  
Author(s):  
Yohanita Maulina Akbar ◽  
Dr. Rudiati Evi Masithoh ◽  
Nafis Khuriyati
Keyword(s):  
Multivariate Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Conventional Method ◽  
Calibration Model ◽  
Validation Data ◽  
Determination Coefficient ◽  
Mlr Model

In this research, Multiple Linear Regression (MLR) model was used to predict Brix and pH of banana based on RGB and Lab color values. Banana samples varied in color and ripening level from less ripen to ripen. RGB and Lab values were measured non-destructively using colormeter, while Brix and pH were determined using conventional method in laboratory. Multivariate analysis was done using the Unscrambler ® X 10.3 (CAMO, AS, OLSO, Norway, and trial version). Results showed that calibration model using MLR was able to predict Brix and pH of banana based on RGB and Lab color values. Furthermore, validation data were used to test the selected models. MLR model to predict Brix based on RGB and Lab validation resulted in 0.8 and 0.84 of determination coefficient between observation and prediction data. The model was also able to predict pH based on RGB and Lab values with 0.71 and 0.79 of determination coefficient between observation and prediction data. ABSTRAKPada penelitian ini, model Multiple Linear Regression (MLR) digunakan untuk memprediksi Brix dan pH pada buah pisang berdasarkan nilai warna Red Green Blue (RGB) dan Lab. Pisang yang dianalisis mempunyai variasi warna dari kurang masak sampai masak. Parameter warna RGB dan Lab dilakukan secara non-destruktif dengan menggunakan colormeter, sedangkan pengukuran kualitas internal yaitu Brix dan pH ditentukan secara destruktif atau dengan prosedur konvensional di laboratorium. Aplikasi analisis multivariat yang digunakan adalah Unscrambler ® X 10.3 (CAMO, AS, OLSO, Norway, versi trial). Analisis data menunjukkan bahwa model kalibrasi MLR dapat digunakan untuk memprediksi Brix dan pH berdasarkan parameter warna RGB dan Lab pada buah pisang. Selanjutnya, data validasi digunakan untuk menguji model MLR terpilih. Model kalibrasi MLR dapat memprediksi Brix berdasarkan nilai RGB dan Lab dengan nilai koefisien determinasi (R2) sebesar 0,8 dan 0,84, secara berurutan. Sedangkan koefisien determinasi (R2) untuk pH berdasarkan warna RGB dan Lab adalah 0,71 dan 0,79.

NLEAP Computer Model and Multiple Linear Regression Prediction of Nitrate Leaching in Vegetable Systems

2002 ◽  
Vol 12 (2) ◽  
pp. 250-256 ◽  
Author(s):  
Hudson Minshew ◽  
John Selker ◽  
Delbert Hemphill ◽  
Richard P. Dick
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Nitrate Leaching ◽  
Cover Crops ◽  
N Uptake ◽  
N Leaching ◽  
Residual Soil ◽  
Vegetable Crops ◽  
Crop N Uptake ◽  
Mlr Model

Predicting leaching of residual soil nitrate-nitrogen (NO3-N) in wet climates is important for reducing risks of groundwater contamination and conserving soil N. The goal of this research was to determine the potential to use easily measurable or readily available soilclimatic-plant data that could be put into simple computer models and used to predict NO3 leaching under various management systems. Two computer programs were compared for their potential to predict monthly NO3-N leaching losses in western Oregon vegetable systems with or without cover crops. The models were a statistical multiple linear regression (MLR) model and the commercially available Nitrate Leaching and Economical Analysis Package model (NLEAP 1.13). The best MLR model found using stepwise regression to predict annual leachate NO3-N had four independent variables (log transformed fall soil NO3-N, leachate volume, summer crop N uptake, and N fertilizer rate) (P < 0.001, R2 = 0.57). Comparisons were made between NLEAP and field data for mass of NO3-N leached between the months of September and May from 1992 to 1997. Predictions with NLEAP showed greater correlation to observed data during high-rainfall years compared to dry or averagerainfall years. The model was found to be sensitive to yield estimates, but vegetation management choices were limiting for vegetable crops and for systems that included a cover crop.

scholarly journals Particulate matter (PM10) prediction based on multiple linear regression: a case study in Chiang Rai Province, Thailand

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wissanupong Kliengchuay ◽  
Rachodbun Srimanus ◽  
Wechapraan Srimanus ◽  
Sarima Niampradit ◽  
Nopadol Preecha ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Local Level ◽  
Winter Season ◽  
Summer Season ◽  
Weather Data ◽  
Good Prediction ◽  
Mlr Model

Abstract Background The northern regions of Thailand have been facing haze episodes and transboundary air pollution every year in which particulate matter, particularly PM10, accumulates in the air, detrimentally affecting human health. Chiang Rai province is one of the country’s most popular tourist destinations as well as an important economic hub. This study aims to develop and compare the best-fitted model for PM10 prediction for different seasons using meteorological factors. Method The air pollution and weather data acquired from the Pollution Control Department (PCD) spanned from the years 2011 until 2018 at two stations on an hourly basis. Four different stepwise Multiple Linear Regression (MLR) models for predicting the PM10 concentration were then developed, namely annual, summer, rainy, and winter seasons. Results The maximum daily PM10 concentration was observed in the summer season for both stations. The minimum daily concentration was detected in the rainy season. The seasonal variation of PM10 was significantly different for both stations. CO was moderately related to PM10 in the summer season. The PM10 summer model was the best MLR model to predict PM10 during haze episodes. In both stations, it revealed an R2 of 0.73 and 0.61 in stations 65 and 71, respectively. Relative humidity and atmospheric pressure display negative relationships, although temperature is positively correlated with PM10 concentrations in summer and rainy seasons. Whereas pressure plays a positive relationship with PM10 in the winter season. Conclusions In conclusion, the MLR models are effective at estimating PM10 concentrations at the local level for each seasonal. The annual MLR model at both stations indicates a good prediction with an R2 of 0.61 and 0.52 for stations 65 and 73, respectively.

Seasonal ground level ozone prediction using multiple linear regression (MLR) model

2020 ◽  
Vol 6 (4) ◽  
pp. 1981-1989
Author(s):  
Sarat Kumar Allu ◽  
Shailaja Srinivasan ◽  
Rama Krishna Maddala ◽  
Aparna Reddy ◽  
Gangagni Rao Anupoju
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Ground Level ◽  
Ground Level Ozone ◽  
Ozone Prediction ◽  
Mlr Model

scholarly journals Valoración de energía digestible de la glicerina cruda proveniente de aceite de palma -Elaeis guineensis- para cerdos en crecimiento en función del nivel de almidón de maíz en la dieta

2017 ◽  
Vol 64 (2) ◽  
pp. 52-69
Author(s):  
Ciro Alberto Ordoñez-Gomez ◽  
Gonzalo Mejia ◽  
German Afanador-Tellez ◽  
Claudia Ariza-Nieto
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Nitrogen Metabolism ◽  
Conventional Method ◽  
Palm Oil ◽  
Corn Starch ◽  
Elaeis Guineensis ◽  
Latin Square ◽  
Randomized Design ◽  
Completely Randomized Design

To determine the apparent digestible energy (EDA) of crude glycerin (GC) from palm oil (Elaeis guineensis) in pigs two experiments (E) were performed. In E1, EDA was determined to GC, with the technique of mobile nylon bag (TBMN) using eight barrows with duodenal cannula. In E2, EDA, EMA to GC was determined by the conventional method with indicator (MCI) with 10 barrows placed in metabolic cages. In E1 and E2 were evaluated in 2 x 5 factorial arrangement, two levels of corn starch (NA) in the diet, 10 (NA10) and 12% (NA12), and five levels of replacement by GC, 0; 2.5; 5; 7.5 and 10%, in E1 as a completely randomized design and in E2 as a Latin square design. For MCI and TBMN, data were analyzed using multiple linear regression and nitrogen metabolism in MCI as a Latin square design using the GLM and REG modules of SAS. By TBMN there was no effect (P> 0.05) in the NA on the EDA of GC. EDA of GC was calculated at 3251 kcal / kg DM. EDA of GC in the MCI depended NA (P <0.001), estimated at 4427.3 and 3769.3 Kcal / kg MS for NA10 and NA12, respectively. Interaction (P <0.001) between NA and GC for the amount of digestible nitrogen was observed, was reduced when GC increased by NA12 and increased by NA10. Other nitrogen metabolism parameters were not affected (P> 0.05). The results of EDA corrected by NA with MCI showed high correlation (R2 = 0.82) with TBMN. By MCI was established that increased in NA reduced the  EDA of GC.  

scholarly journals Modeling of soil shear strength using multiple linear regression (MLR) at Penang, Malaysia

2021 ◽  
Vol 9 (3A) ◽  
Author(s):  
Bala Balarabe ◽  
◽  
Andy Anderson Bery ◽  
Keyword(s):  
Shear Strength ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Friction Angle ◽  
Accurate Estimation ◽  
Near Surface ◽  
Independent Variables ◽  
Refraction Tomography ◽  
Soil Shear Strength ◽  
Mlr Model

This paper presents multiple linear regression (MLR) soil shear strength models developed from electrical resistivity and seismic refraction tomography data. The MLR technique is used to estimate the value of dependent variables of soil shear strength based on the value of two independent variables, namely, resistivity and velocity. These parameters were regressed using regression statistics technique for generating MLR model. The results of MLR model, which is based on the estimation of model dependent parameters (Log10 resistivity and Log10 velocity), calculated for p-value, are less than 0.05 and VIF value less than 10 for cohesion and friction angle models. This result shows that there is a statistically significant relationship between cohesion and friction angle with geophysical parameters (independent variables). The estimation accuracy of the MLR models is also conducted for verification, and the result shows that RMSE value for predicted cohesion and predicted friction angle is 0.77 kN/m2 and 1.73° which is close to zero. Meanwhile, MAPE value was found to be 4.57 % and 7.61 %, indicating highly accurate estimation for the MLR models of predicted cohesion and predicted friction angle. Based on the application of near surface, the study area was successfully classified into two regions, namely, medium and hard clayey sand. Thus, it is concluded that MLR method is suitable in estimating the subsurface characterization that covered more regions compared to the traditional method (laboratory test).

scholarly journals Pengaruh Daya Tarik Wisata dan Word Of Mouth terhadap Keputusan Wisata Berkunjung ke Taman Hutan Kota di Kota Langsa

2019 ◽  
Vol 10 (2) ◽  
pp. 146-155
Author(s):  
Erni Junaida
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Word Of Mouth ◽  
T Test ◽  
Linear Regression Method ◽  
Tourist Attraction ◽  
F Test ◽  
Forest Park ◽  
Determination Coefficient ◽  
Test Result

This research intends to know the influence of tourist attraction and word of mouth to tourists' visiting decision in city forest park kota langsa. Samples of this research are 96 respondents. Data analysis of this research is using multiple linear regression method and hypothesis testing is using t test, f test, and determination coefficient test (R2). The result of multiple linear regression is Y = 1,200 + 0,341 X1 + 0.137 X2. T test result shows that tourist attraction variable influenced significantly to tourist's visiting decision in city forest park kota langsa and word of mouth variable also influenced significantly to tourist's visiting decision in city forest park kota langsa. F test result shows that tourist attraction variable and word of mouth variable influenced significantly to tourist's visiting decision in city forest park kota langsa. Determination coefficient test shows that tourist attraction and word of mouth influenced tourist's visiting decision in city forest park kota langsa for 0,301 or 30,1 %.

scholarly journals FAKTOR-FAKTOR YANG MEMPENGARUHI KEMAMPUAN MICROTEACHING MAHASISWA

2015 ◽  
pp. 227-238
Author(s):  
Siti Rohmah Rohimah ◽  
Ismah Ismah
Keyword(s):  
Instructional Technology ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Test Performance ◽  
Learning Strategy ◽  
Linear Regression Analysis ◽  
Math Learning ◽  
Determination Coefficient ◽  
Learning Plan ◽  
Linearity Test

Abstrak:Tujuan penelitian ini adalah untuk mengetahui faktor-faktor yang mempengaruhi kemampuan microteaching mahasiswa. Penelitian ini merupakan penelitian kuantitatif yang menggunakan analisis regresi linear berganda. Populasi penelitian ini adalah seluruh mahasiswa Program Studi Pendidikan Matematika semester 6 yang sedang mengambil mata kuliah Pembinaan Kompetensi Mengajar (Microteaching) tahun ajaran 2012/2013 pada Fakultas Ilmu Pendidikan Universitas Muhammadiyah Jakarta yang berlokasi di Cirendeu. Data dikumpulkan dari nilai hasil akhir setiap mata kuliah Media dan Teknologi Pembelajaran, Strategi Pembelajaran Matematika, dan Perencanaan Pembelajaran Matematika. Nilai kemampuan mengajar dikumpulkan menggunakan Microteaching Test Performance setiap mahasiswa yang terintegrasi dalam nilai akhir dari mata kuliah Pembinaan Kompetensi Mengajar. Kemudian dilakukan uji asumsi yang harus dipenuhi dalam regresi linear berganda yaitu uji multikolinearitas, uji autokorelasi, uji heteroskedastisitas, dan uji linearitas. Hasil penelitian menunjukkan bahwa variabel nilai mata kuliah Media dan Teknologi Pembelajaran (X1), Strategi Pembelajaran Matematika (X2), dan Perencanaan Pembelajaran Matematika (X3) secara bersamaan mempengaruhi nilai mata kuliah Pembinaan Kompetensi Mengajar (Y) secara signifikan. Koefisien determinasi dari model regresi sebesar 0.37. Hal ini berarti bahwa varian nilai mata kuliah Pembinaan Kompetensi Mengajar (Y) mampu dijelaskan sebesar 37% oleh variabel nilai mata kuliah X1, X2, dan X3. Sedangkan 63% sisanya oleh faktor lainnya. Semoga hasil penelitian ini bisa menjadi bahan literatur untuk penelitian berikutnya dengan menentukan lebih banyak lagi faktor yang mempengaruhi kemampuan microteaching mahasiswa.Kata Kunci: kemampuan microteaching, regresi linear berganda, koefisien determinasiAbstract:The aim of this research is to determine the factors affecting the students’ microteaching ability. This research is a quantitative research which applies multiple linear regression analysis. The population is all 6th semester students of Mathematic Education Program at Education Faculty of Universitas Muhammadiyah Jakarta who are taking microteaching subject in 2012/2013 academic year. Data are from their final scores of three subjects: Media and Instructional Technology, Math Learning Strategy, and Math Learning Plan. Microteaching capacity scores are based on their Microteaching Test Performance integrated in their final Microteaching subject score. Then, assumption test that must be met in multiple linear regression is carried out which are multicolinearity test, autocorrelation test, heteroscedacticity test, and linearity test. The result shows that the score variables of Media and Instructional Technology (X1), Math Learning Strategy (X2), and Math Learning Plan (X3) collectively effect the score of Microteaching (Y) significantly. Determination coefficient of regression model is 0.37, meaning that the variable of Microteaching (Y) subject score can be explained by variables X1, X2, and X3 of 37%. The rest of 63% is explained by other variables. Hopefully, this researchcan be reference for continuous research on students’ microteaching capacity with more affecting factors.Keywords: microteaching capacity, multiple linear regression, determination coefficient

Estimating and Modeling Uncertainties Affecting Production Throughput Using ARIMA-Multiple Linear Regression

2012 ◽  
Vol 488-489 ◽  
pp. 1263-1267
Author(s):  
Amir Azizi ◽  
Amir Yazid B. Ali ◽  
Loh Wei Ping ◽  
Mohsen Mohammadzadeh
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Production Line ◽  
Moving Average ◽  
Random Variables ◽  
Modeling Uncertainties ◽  
Break Time ◽  
Demand Changes ◽  
Changes Over Time ◽  
Mlr Model

Throughput of each production stage cannot meet the demand in the real production system because of the disruptions and interruptions of the production line for example break time and scrap. On the other hand, demand changes over time due to volume variation and product redesign as the customers’ needs are changing. This situation leads to planning and controlling under uncertain condition. This paper proposes a hybrid model of autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR) for estimating and modeling the random variables of production line in order to forecast the throughput in presence of production variations and demand fluctuation. The random variables under consideration of this study are demand, break-time, scrap, and lead-time. The random variables are formulated in the MLR model, where the mean absolute percentage of error (MAPE) was 2.53%. Further, nine ARIMA models with different parameters in MLR model are fitted to the data and compared by their MAPE. The best model with the lowest MAPE was when the ARIMA parameters set for p=1, d=0, and q=3. Finally the proposed model using ARIMA-MLR is formulated by MAPE of 1.55%.

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