Coefficient Of Determination Values Research Articles

Estimation Chlorophyll-a Using Landsat-8 Imagery in Shallow Water: Effect of Atmospheric and Algorithm

Chlorophyll-a estimation using remote sensing technology is being challenged to improve its accuracy. Various algorithms and correction methods need to be studied, including the influence of the atmosphere. It can influence the passage of the electromagnetic wave from the sun to the object and from the object to the sensor that makes the difference on the image reflectance. There are two kinds of reflectance; which are ToA (Top of Atmosphere) reflectance and BoA (Bottom of Atmosphere) reflectance. ToA reflectance is the reflectance captured by the sensor yet BoA reflectance is the reflectance of the object corrected by the atmosphere. ToA reflectance is produced by radiometric calibration while BoA reflectance is made of atmospheric correction process. This research studies aims to compare those to reflectance to investigate the impact of atmospheric correction usage on chlorophyll-a case study. The waters of Wedung district is chosen as the research field because it is the largest area in Demak regency. This study used DOS (Dark Object Substractions), FLAASH (Fast Line of sight Atmospheric Analysis of Spectral Hypercubes), and 6SV (Second Simullations of a Satellite Signal in the Solar Spectrum) correction method. To compare between ToA and BoA reflectance in the calculation of chlorophyll-a, the writer used the algorithms of Wouthuyzen, Wibowo, Pentury, Much Jisin and also Lestari Laksmi. The result shown is that the usage of BoA image reflectance (atmospherically corrected) had a better model result than ToA image reflectance. This is indicated from the consecutive determination coefficient values of ToA reflectance which are 0,2292; 0,4562; 0,2292; 0,2252. Meanwhile the consecutive coefficient values of BoA reflectance by DOS correction method are 0,5251; 0,5575; 0,5251; 0,6939; by FLAASH correction method are 0,6168, 0,5041, 0,6168, 0,614; by 6SV method are 0,6436; 0,4033; 0,6436; 0,6365. The result of hypothesis and validation test is that atmospheric correction significantly influences on the calculation of chlorophyll-a in Wedung district except using Wibowo algorithm.

KINERJA PERUSAHAAN PENGARUH DARI STRUKTUR MODAL, PROFITABILITAS DAN UKURAN PERUSAHAAN

This study aims to determine the effect of Capital Structure, Profitability, Company Size on the Performance of the cigarette sub-sector companies listed on the IDX in 2014-2018. This research uses descriptive and verification methods with regression analysis tools and the coefficient of determination. The results of data processing analysis of the coefficient of determination obtained a fairly strong and unidirectional closeness of the relationship between the independent variable and the dependent variable and has a determination coefficient value of 47.91%. The results of testing the F test hypothesis indicate that the Capital Structure, Profitability and Company Size simultaneously have a significant effect on company Performance. Hypothesis testing using t test shows that Capital Structure has no significant effect on company Performance. Profitability has a significant effect on company Performance and Company Size has a significant effect on company Performance.
 Keywords: Capital Structure, Profitability, Company Size, Company Performance

Influence of effective texture depth on pavement friction based on 3D texture area

Pavement texture significantly affects the anti-skid performance. This study applied 3D laser technology to measure the texture of asphalt pavement surfaces with an objective to explain the variation in the friction measured using a portable pendulum tester. Pavement slab samples were prepared for six different types of asphalt mixtures (SMA-Top5, AC-10, AC-13, AC-16, SMA-13, and OGFC-13). High-precision surface texture data of the samples were then collected using a portable laser scanner (LS-40), and the friction values (BPN) were measured. The 3D image reconstruction and segmentation of the pavement texture were realized through a digital image processing technology, and the texture surface area at different depths was calculated. Finally, we performed a correlation analysis between the texture surface area at different depths and the pavement friction values. The results showed that the pavement anti-skid value and the texture surface area have a significant positive correlation in the texture depth range of 0.5–2 mm, with the lowest determination coefficient value of 0.6836. Our research confirms that the effective texture depth influencing pavement friction is 2 mm and that the texture surface area can be used as an indicator of pavement friction during its service life.

Predicting Resilient Modulus of Cementitiously Stabilized Subgrade Soils Using Neural Network, Support Vector Machine, and Gaussian Process Regression

Artificial neural network (ANN), support vector machine (SVM), and Gaussian process regression (GPR) were developed in this study for predicting resilient modulus (Mr) values of cementitously stabilized subgrade soils. A database was developed using laboratory test results on 160 specimens prepared by using four soils stabilized with three cementitious additives, namely, lime (3%, 6%, 9%), CFA (5%, 10%, 15%), and CKD (5%, 10%, 15%). Of these, 120 specimens (development dataset) prepared using three types of soils were used in development, and the remaining 40 specimens (validation dataset) were used in the validation of the developed models. A commercial software, MATLAB, was leveraged to develop three ANN models (radial basis function network/RBFN; a multilayer perceptrons network/MLPN with one hidden layer; and MLPN with two hidden layers); four SVM models (linear, quadratic, cubic, and radial basis function kernels); and three GPR models (rational quadratic, Matérn, and exponential kernels) by using codes written in MATLAB language. The strengths and weaknesses of the developed models were examined by comparing the predicted Mr values with the observed/experimental values with respect to the mean squared error (MSE) values and determination coefficient (R2) values. Through comprehensive comparison among these three types of models, an MLPN model with one hidden layer was determined as the best performing model developed in this study. It can be used to predict Mr of cementitiously stabilized subgrade soils for Level 2 pavement design applications. This model as well as the other models could be refined using an enriched database.

Chlorophyll Content in Leaves of Wheat as Influenced by Inorganic, Organic and Integrated Sources of Nutrient Application

Field experiments were conducted during winter season of 2018-19 and 2019-20 with three sources of nutrient viz., inorganic, organics (FYM, VC and biofertilizers) and their integration as main treatments and five levels [S1-0 (0-0-0 kg NPK ha-1), S2-100% (120-60-40 kg NPK ha-1), S3-150% (180-90-60 kg NPK ha-1), S4-200% (240-120-80 kg NPK ha-1) and S5-Soil Test based (STV) NPK i.e. 149-176-33 kg ha-1 in split plot design with three replications. The chlorophyll content (‘a’, ‘b’ and total) in leaves and Soil Plant Analyzer Development (SPAD) value were recorded at crown root initiation (CRI), tillering, jointing and milking stage of wheat. The pooled data of findings revealed that the treatment with inorganic sources showed significant increase in the SPAD readings (9.62, 15.54, 23.77 and 29.83), chlorophyll ‘a’ (0.76, 0.83, 1.47 and 0.63 mg g-1 leaf tissue), ‘b’ (0.44, 0.78, 0.87 and 0.57 mg g-1 leaf tissue) and total (1.19, 1.64, 2.25 and 1.14 mg g-1 leaf tissue) chlorophyll content in leaves over organic source at all the growth stages.
 All the levels of nutrient were significantly increased the chlorophyll content and SPAD value over control at all the stages except chlorophyll ‘a’ at jointing and milking stage. However, amongst the levels 150% and 200% NPK were found significantly superior to 100% NPK for SPAD value (8.32 and 8.71 at CRI and 12.56 and 12.19 at tillering), chlorophyll ‘a’ (0.73 and 0.70 mg g-1 leaf tissue at CRI), chlorophyll ‘b’ (0.46 and 0.45 mg g-1 leaf tissue at CRI, 0.68 and 0.71 mg g-1 leaf tissue at tillering and 0.53 and 0.59 mg g-1 leaf tissue at milking), respectively.
 The interaction results suggested that the 200% NPK with inorganic and integrated sources significantly superior to 100% NPK for chlorophyll ‘a’ content at jointing and milking stage. The application of 150% and 200% NPK with inorganic source were found significantly higher over the same level of NPK with integrated source of nutrient for total chlorophyll content and SPAD value at all the growth stages except 150% NPK for total chlorophyll at jointing and milking stage and SPAD value at milking stage. The correlation between SPAD value and chlorophyll ’a’, ‘b’, total were found significantly and positively at all growth stages. Coefficient of determination values between SPAD and chlorophyll content showed linear relationship at all the growth stages.

The Level of Consumer Confidence in the Purchase Decision of Hotel and Restaurant Tourism Products

The existence of the COVID-19 pandemic has affected the tourism industry, including the purchase of hotel and restaurant services. This study aims to determine the level of consumer confidence in purchasing decisions for hotel and restaurant tourism products. The research method used is descriptive quantitative with a questionnaire instrument and data collection techniques using questionnaires, interviews. The population used in this study is respondents who use hotel and restaurant services in Jabodetabek with various demographics of the work of trainers, consultants and practitioners, but preferably 90 people who are accustomed to using these services. The sample used is saturated sample, so that the entire population becomes a sample of 90 respondents. The results showed that the coefficient of determination or R Square was 0.766. that is, the variable under study explains 76.6% and the remaining 23.4% is explained by other variables not examined in this study. The results of the research on the level of consumer confidence in the decision to purchase hotel and restaurant products during the Covid 19 pandemic, obtained a determination coefficient value of 76.6%. It can be concluded that the level of consumer confidence in purchasing decisions during the Covid 19 period. Consumers who decide to purchase hotel and restaurant tourism products for the purpose of work, travel, and refreshing or traveling. The purpose of purchasing hotel tourism products is dominant with the aim of West Java as much as 27%. Meanwhile, the destination for purchasing restaurant tourism products is dominant in the Jakarta area as much as 33%. Hotel and restaurant service providers continue to use the application of health protocols.

Salt content in saline-alkali soil detection using visible-near infrared spectroscopy and a 2D deep learning

Quickly measuring the salt content in saline-alkali soil (SAS) is an important task. This study proposes a method for rapid detection of salt content. First, we collected the SAS samples and measured the spectral data of these samples with a visible-near infrared spectrometer. Second, a method of converting one-dimensional into two-dimensional spectral data is proposed. Finally, based on convolutional neural network, gravitational search algorithm and reservoir computing extreme learning machine, a salt detection model is constructed. The experimental results show that our proposed method can effectively detect the salt content of SAS with the coefficient of determination value is 0.9 and the root-mean-square error value is 1.55. This method can achieve online rapid detection of salt content. Compared with chemical analysis method, the proposed method saves time and cost.

AUTHENTICITY ANALYSIS OF BEEF MEATBALL ADULTERATION WITH WILD BOAR USING FTIR SPECTROSCOPY COMBINED WITH CHEMOMETRICS

The aim of this research to detect and analyze the wild boar in beef meatball using FTIR combined with chemometric. Then, the specific of FTIR region for detecting wild boar in beef meatball in the commercial product. The wild boar meatball was made with different a concentration range between 0-100%. The wild boar meatball was analyzed using FTIR. The FTIR spectrum was processed using chemometrics method like Part Least Square (PLS) and Principal Component Analysis (PCA). The PLS look for the highest of determination coefficient and smaller of root mean square error of calibration (RMSEC) or Root Mean Square Error of Cross-Validation (RMSECV). PCA analysis will distinguish the wild boar meatball and beef meatball product. The result of this research showed that the FTIR spectrum of wild boar and beef in meatball have different characteristics. The result from FTIR spectrum processing showed that specific spectrum of wild boar at wavenumber 999 - 1481 cm-1 combined with 1650 - 1793 cm-1 which is processed at 1st derivative. The PLS resulted from the calibration method has the determination coefficient (R2) value is 0.9991 and RMSEC of 1.028%. While the validation methods resulted are R2 and RMSECV which are 0.9999 and 0.300% respectively. This research showed the meatball from wild boar can be distinguished from beef and the commercial meatballs are not made from them.

Investigations on quality characteristics in gas tungsten arc welding process using artificial neural network integrated with genetic algorithm

Gas tungsten arc welding (GTAW) technology is widely used in industry and has advantages, including high precision, excellent welding quality, and low equipment cost. However, the inclusion of a large number of process parameters hinders its application on a wider scale. Therefore, there is a need to implement the prediction and optimization models that effectively enhance the process performance of the GTAW process in different applications. In this study, a five-factor five-level central composite design (CCD) matrix was used to conduct GTAW experiments. AISI 1020 steel blank was used as a substrate; UTP AF Ledurit 60 and UTP AF Ledurit 68 were used as the materials of two tubular wires. Further, an artificial neural network (ANN) was used to simulate the GTAW process and then combined with a genetic algorithm (GA) to determine welding parameters that can provide an optimal weld. In welding experiments, five different welding current levels, welding speed, distance to the nozzle, angle of movement, and frequency of the wire feed pulses were used. Using GA, optimal welding parameters were determined: welding current = 222 A, welding speed = 25 cm/min, nozzle deflection distance = 8 mm, travel angle = 25°, wire feed pulse frequency = 8 Hz. The determination coefficient (R2) and RMSE value of all response parameters are satisfactory, and the R2 of all the data remained higher than 0.65.

The relationship between critical thinking skills and students learning motivation with students’learning achievement about buffer solution in eleventh grade science program

This research was aimed to find out: (1) the relationship between students’ critical thinking skills with students’ achievement; (2) the relationship between students’ learning motivation with students’ achievement; and (3) the relationship between students’ critical thinking skills and learning motivation with learning achievement in learning about buffer solution. This was a quantitative research with a correlational approach. The populations were 178 students at eleventh grade science program from SMA Negeri 6 (senior high school) in Surakarta, Central Java, Indonesia, academic year of 2019/2020.. One class with 36 students was selected sample by using cluster random sampling. The data were obtained through a multiple-choice test about critical thinking skills, a multiple-choice test about the buffer solution topic, and a questionnaire about students’ learning motivation. The data were analyzed by using Pearson Product Moment correlation, multiple correlations, and multiple regression analysis. The results showed that: (1) A positive and significant relationship between students’ critical thinking skills with students’ achievement was found, as shown by the correlation coefficient value of 0.871; (2) A positive and significant relationship between students’ learning motivation with their achievement was found, as shown by the correlation coefficient value of 0.884; and (3) A positive relationship between students’ critical thinking skills and learning motivation with their learning achievement, as shown by correlation coefficient value of 0.931. Regression analysis obtained the equation of Y = 10,416 + 0.423 X1 + 0.487 X2, and the value of coefficient determination was 0.845 or 84.5%.

Semiparametric regression based on fourier series for longitudinal data with Weighted Lest Square (WLS) optimization

Regression modelling is one Statistical methods that be used to investigate the relationship between predictor variable and response variable. In regression modelling can be estimated with three approaches, such as parametric, nonparametric and semiparametric regression. In this case, we concentrated to elaborate semiparametric regression. Semiparametric regression consists of parametric and nonparametric component. This research examined semiparametric regression model with Fourier series estimator for longitudinal data. By minimizing Weighted Least Square (WLS), the Fourier series estimator depends on the oscillation parameter. The result is the estimator for parameter and curve regression, that be used to model with real data. The optimal model is selected based on minimum Generalized Cross Validation (GCV) which affects the small value of Mean Square Error (MSE) and high determination coefficient so that the model can be used further as estimation and prediction.

Correlation between Body Mass Index and Radiation Dose in Patients Undergoing Coronary Computed Tomography Angiography

ABSTRACT Background: Radiation dose is an important factor in performing a coronary Computed Tomography Angiography (CTA). Objective: To assess the correlation between Body Mass Index (BMI) and radiation dose in patients receiving a Coronary CTA. Methods: All retrospectively EKG gated coronary CTA studies using a 64 slice CT in patients above 18 years old, during a one-year time period were included in the study. The data was obtained by a query of the PACS system. Sixteen of the total 75 subjects were excluded due to lack of BMI or Dose length product (DLP) data or the CTA deviated from standard protocol. Seven of these patients were specifically excluded by using an x-ray tube voltage other than the standard 120 kVp. For the remaining 52 subjects, correlation and linear regression statistical analysis was used to analyze the relationship between BMI and DLP. Pearson correlation coefficient was calculated using Microsoft Excel. Results: There was a weak correlation between the BMI and DLP with a coefficient of determination value of 0.0217. Conclusion: When appropriate criteria exist for obtaining a coronary CTA, a high BMI should not deter ordering based only on high radiation dose concerns since correlation between BMI and DLP is weak.

Development and validation of a gas chromatography with a flame ionization detector method for quantitative analysis of four major fatty acids extracted shark liver oil

Context: The shark liver of the species Ginglimostoma cirratun, Carcharhinus longimanus, and Carcharhinus falciformis, captured in the north-central coast of Cuba are a source of oil, whose content of major fatty acids could be used in its quality control. Aims: To develop a simple and robust gas chromatography with a flame ionization detector (GC-FID) method that is suitable for routine analysis of four major fatty acids extracted shark liver oil. Methods: Four major fatty acid content in shark liver oil pool of species Ginglimostoma cirratun, Carcharhinus longimanus, and Carcharhinus falciformis, was analyzed through the gas chromatography with a GC-FID. The fatty acids were analyzed as methyl esters derivatives, using 5% aqueous sulfuric acid in methanol. The method was validated in terms of linearity, precision, accuracy, specificity and limit of detection and quantitation. Results: Under the optimum analytical conditions, the analysis revealed that each target component was well separated with satisfactory recoveries and reproducibility. The method linearity was found to be high with good determination coefficient values for all target components. The evaluation of the matrix effect, demonstrated, that there is not interference from substances other than analysis. The method was also found to be accurate, precise and reproducible and it was applied to the quantitative determination of the fatty acid content in shark liver oil pool; oleic acid was the most abundant fatty acid (22.69%), followed by palmitic (18.85%), stearic (6.01 %) and myristic acid (0.40 %). Conclusions: The GC-FID developed method is reliable and suitable for determination of four major fatty acids in shark liver oil pool.

The Effect of Developing Process Standards and Content Standard Development on Improving The Performance of Chemical Teachers of Negeri High Schools in Bandung District

This study examines the problem of developing process standards and content standards which are important variables in improving teacher performance. Teacher performance is increasingly important to improve in line with the increasingly complex demands of teachers as professionals. The purpose of this study was to describe and analyze the influence and comparison of the development of process standards and content standards on improving teacher performance which focused on the main problems, namely: (1) the magnitude of the influence of developing process standards on teacher performance, (2) the magnitude of the influence of developing content standards on performance. teachers, and (3) the magnitude of the influence and comparison of the development of process standards and content standards simultaneously on improving the performance of state high school chemistry teachers in Bandung Regency. In this study, the Embedded Concurent model of the Mix Methods approach was used. Data were analyzed using descriptive analysis techniques and inferential analysis techniques. The results of the analysis show that the development of process standards and content standards together contributes positively and significantly to improving teacher performance, which is stated based on the positive correlation coefficient and determination coefficient values ​​with an effect of 76%, meaning that the effect is good, this proves that the better the development of standard processes and the development of content standards are carried out, the better the increase in teacher performance. Meanwhile, the remaining 24% is influenced by other variables

The Effect of Developing Process Standards and Content Standard Development on Improving The Performance of Chemical Teachers of Negeri High Schools in Bandung District

This study examines the problem of developing process standards and content standards which are important variables in improving teacher performance. Teacher performance is increasingly important to improve in line with the increasingly complex demands of teachers as professionals. The purpose of this study was to describe and analyze the influence and comparison of the development of process standards and content standards on improving teacher performance which focused on the main problems, namely: (1) the magnitude of the influence of developing process standards on teacher performance, (2) the magnitude of the influence of developing content standards on performance. teachers, and (3) the magnitude of the influence and comparison of the development of process standards and content standards simultaneously on improving the performance of state high school chemistry teachers in Bandung Regency. In this study, the Embedded Concurent model of the Mix Methods approach was used. Data were analyzed using descriptive analysis techniques and inferential analysis techniques. The results of the analysis show that the development of process standards and content standards together contributes positively and significantly to improving teacher performance, which is stated based on the positive correlation coefficient and determination coefficient values ​​with an effect of 76%, meaning that the effect is good, this proves that the better the development of standard processes and the development of content standards are carried out, the better the increase in teacher performance. Meanwhile, the remaining 24% is influenced by other variables

Resource recovery in potato chips processing industry: green bioelectricity production using continuous mediatorless microbial fuel cell

ABSTRACT Potato chips processing industry generally discharge large volumes of organic loaded-wastewater and significant amounts of peels as solid wastes. In this study, a dual chamber microbial fuel cell (MFC) was setup, and continuously operated for 120 days for combined biotreatment of potato chips processing wastewater (PCW) and waste potato peels (PP) associated with electricity generation. The discarded PP were dried, grinded, and added to the PCW as a powder at concentrations of 0, 2.5, and 5 g PP/L resulted in three different organic loadings, denoted as OL0, OL1, and OL2, respectively. The results demonstrated significant removal efficiency of COD up to 99% with maximum power generation of 612.5, 800, and 1012.5 mW/m3 for OL0, OL1, and OL2, respectively. Butler–Volmer–Monod model was proposed to describe the overpotential-polarization curve for the MFC. Significant agreement was observed between the predicated and experimental results with determination coefficient (R2) values > 0.91.

Stochastic optimization and modeling of high-velocity impact tests on high-temperature carbon\u2013carbon composites

In this study, it is intended to optimize a high-velocity impact case of a composite plate. The case selected from literature focused on the failure response of advanced carbon–carbon (C/C) composites under high-velocity impacts. Based on the stochastic optimization method, three unique models are introduced within the present study's scope as dimensionless damage areas of front and back sides and the composite impact energy response. The difference between the equations found in the present study and the base study is the number of variables. Obtained prediction models consist of only the tests' input variables; thus, these models can be considered the essential prediction functions of high-velocity impact response of C/C composites under high temperatures. Multiple nonlinear regression method is used for objective functions of the optimization problem. Since the determination coefficient values have been found quite similar to the ones in the literature, the presented models can be considered successful in predicting the results. By utilizing the novel regression functions presented in this study, the damaged areas are minimized. Without the necessity of experimental research, further predictions can be made by operating the models found in the present study.

Energy consumption prediction of cement production based on chaotic neural network-Markov chain

In order to improve the accuracy of energy consumption modeling and prediction in the cement production process, a cement production energy consumption prediction model based on chaotic neural network is proposed: 1. In the energy consumption modeling stage, chaotic neural network is used to reconstruct the phase of chaotic time series. Space, the chaotic neural network can still make high-precision predictions of the system even when the network input is incomplete or mutated, and the determination coefficient value is 0.019 higher than that of the RBF neural network. 2. In the energy consumption prediction stage, the introduction of Marko The residual error correction method is to correct the current forecast value based on the residual error between the historical predicted energy consumption value and the actual energy consumption value. The result shows that the relative residual error of the predicted value corrected by the Markov correction method decreases from -0.6% to -0.25 %, the predicted value of energy consumption is closer to the actual value. Based on the above description of the two stages of energy consumption modeling, the proposed cement production energy consumption prediction model has better prediction effects and higher prediction accuracy than traditional mechanism modeling in cement production energy consumption prediction.

Multivariate linear regression models for predicting metal content and sources in leafy vegetables and human health risk assessment in metal mining areas of Southern Jharkhand, India.

The present study was intended to investigate the metal concentrations in the leafy vegetables, irrigation water, soil, and atmospheric dust deposition in the iron and copper mining areas of Southern Jharkhand, India. The study aimed to develop a multivariate linear regression (MVLR) model to predict the concentration of metals in leafy vegetables from the metals in associated environmental factors and assessment of the risk to the local population through the consumption of leafy vegetables and other allied pathways. The developed species-specific MVLR models were well fitted to predict the concentration of metals in the leafy vegetables. The coefficient of determination values (R2) was greater than 0.8 for all the species-specific models. Risk assessment was carried out considering multiple pathways of ingestion, inhalation, and dermal contact of vegetables, soil, water, and free-fall dust. Consumption of leafy vegetables was the major route of metal exposure to the local population in both the metal mining areas. The average hazard index (HI) value considering all the metals and pathways was calculated to be 5.13 and 12.1, respectively for iron and copper mining areas suggesting considerable risk to the local residents. Fe, As, and Cu were the major contributors to non-carcinogenic risk in the Iron mining areas while in the case of copper mining areas, the main contributors were Co, As, and Cu.

Pengaruh Sistem Informasi Manajemen Terhadap Kinerja Karyawan Pada PT. Arta Boga Cemerlang Cabang Cirebon

Performance as an ability that a person has includes knowledge and skills. In the world of work, performance is the capacity that exists in a person to fulfill his work responsibilities so as to produce good output. The employee's performance will be said to be successful and optimal if it is in accordance with the company's desired results. The need for information systems to produce optimal employee performance is needed by computers based on Information Technology (computerization), so that it is not only able to optimize employee performance but also facilitate decision making for management because the data and information produced are fast and accurate. To find out the influence of management information system on employee performance, research was made. Thepurpose of this study is to analyze and test the influence of management information system on employee performance. In this study the population is PT Arta Boga Cemerlang Cirebon Branch,Jln. Nyi Gede Cangkring No. 99. To make it easier, save time, effort and cost, the researchers determined the size of the sample based on the Slovin formula. The sample obtained was 55 employees. Data analysis tools used in this study are validity and reliability test, significance test (t test), product moment correlation test (r test), determination coefficient (R2),and linear regression. From this research, it can be concluded that there is a positive and significantinfluence between management information system and employee performance, this can be seen from the results of the study showed a correlation of 0.68 and t calculate = 6,75. From the calculation result t calculate greater than t table which is 6.75 > 2.021; thus Ho hypothesis is rejected and ha alternative hypothesis accepted, i.e. there is a significant influence between management information system on employee performance. The coefficient value of determination of 0.46 or 46%,means that the variation in employee performance change(Y) is influenced by management information system (X) by 46%and the remaining 54%isinfluenced by other factors that are not calculated. Regression test results showed that management information system variables had a positive effect on employee performance with a coefficient of0.96.
 Performance as an ability that a person has includes knowledge and skills. In the world of work, performance is the capacity that exists in a person to fulfill his work responsibilities so as to produce good output. The employee's performance will be said to be successful and optimal if it is in accordance with the company's desired results. The need for information systems to produce optimal employee performance is needed by computers based on Information Technology (computerization), so that it is not only able to optimize employee performance but also facilitate decision making for management because the data and information produced are fast and accurate. To find out the influence of management information system on employee performance, research was made. Thepurpose of this study is to analyze and test the influence of management information system on employee performance. In this study the population is PT Arta Boga Cemerlang Cirebon Branch,Jln. Nyi Gede Cangkring No. 99. To make it easier, save time, effort and cost, the researchers determined the size of the sample based on the Slovin formula. The sample obtained was 55 employees. Data analysis tools used in this study are validity and reliability test, significance test (t test), product moment correlation test (r test), determination coefficient (R2),and linear regression. From this research, it can be concluded that there is a positive and significantinfluence between management information system and employee performance, this can be seen from the results of the study showed a correlation of 0.68 and t calculate = 6,75. From the calculation result t calculate greater than t table which is 6.75 > 2.021; thus Ho hypothesis is rejected and ha alternative hypothesis accepted, i.e. there is a significant influence between management information system on employee performance. The coefficient value of determination of 0.46 or 46%,means that the variation in employee performance change(Y) is influenced by management information system (X) by 46%and the remaining 54%isinfluenced by other factors that are not calculated. Regression test results showed that management information system variables had a positive effect on employee performance with a coefficient of0.96.