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This study aims to classify chicken eggs based on their physical size using the concept of computer vision and image segmentation techniques. Compared to the standard methods that have been used so far, this alternative technology is expected to help standardize measurements, cost efficiency, and work effectiveness. In this study, the classification of chicken eggs was carried out using image segmentation and regression analysis. Thus, it is expected that the classification of chicken eggs will have increasingly accurate values. After the image is taken using a webcam, the image segmentation process is used to divide the image into homogeneous areas based on the RGB (true color) color intensity similarity standard. Regression analysis is used to study and measure the relationship between the number of pixels and the weight of the object. The number of pixels indicating the area of ​​the object is the result of image segmentation, which will be entered into the regression equation to calculate the weight (grams). The results showed that the color characteristics of chicken eggs have a normalization of R at least 0.41 and a normalization of G at least 0.3. In addition, the classification test has an accuracy of 100% (36/36) and a weight estimation accuracy of 42 percent (15/36).

Russiva and Crookes proposed the widely used, highly efficient, robust Q-score parameter scale in their 1993 work, and they approximated it using "fast" Huber's M-scores. Shown What proposed us M-scores are highly efficient and robust on arbitrary nom distributions, thanks to correct choice parameter approximations. We pay particular attention to the Cauchy divisions  and cases of Gaussian distribution . Important terms: Cauchy's law, parameter scale, robustness, and Gaussian distribution With the Q-estimate as a foundation, the M-estimates of the scale parameter are very efficient and robust. Rousseeuw and Croux (1993) proposed the scale parameter of Q-estimate, which is often used with fast Huber M-estimates, and we were able to get rather close to it. By meticulously choosing the approximation parameters, we proved that the proposed M-estimates are efficient and resilient for any data distribution. In order to measure the robustness and efficiency of scale M estimates , we calculated their asymptotic variances; breakdown points , and influence functions. The Cauchy and Gaussian distributions were our main focus. Notably, the proposed robust estimate is consistent the Maximum likelihood estimation for the Cauchy distribution. Last but not least, these robust and efficient scale estimates need three to four times less computing time than their comparable Q-estimates. The research provides robust and efficient solutions for estimating distribution parameters more quickly and with less complexity, making them suitable for applications requiring fast and accurate computations.  

This think around focuses to essentially and comprehensively see at the concept of citizenship from the focuses of see of sacrosanct law and Islamic law. Citizenship may be a legal status that characterizes the rights and commitments of an individual interior a state. Inside the setting of sacrosanct law, citizenship is controlled through constitutions and citizenship laws that incorporate distinctive perspectives such as the benchmarks of citizenship (ius soli, ius sanguinis), human rights, and conscious commitments. Sacrosanct law besides considers the measures of vote based framework, value, and adjust in choosing citizenship status. On the other hand, from the point of see of Islamic law, the concept of citizenship isn't because it were seen from a legal-formal point but in addition incorporates moral and ethical estimations. Islamic law gives heading on the commitments and rights of individuals as parcel of the ummah (the Muslim community), based on Shariah measures such as value ('adl), open welfare (maslahah), and the confirmation of fundamental human rights. Besides, Islamic law considers the rights of non-Muslims interior an Islamic state setting through the concept of "ahl al-dhimmah" (guaranteed people) and the benchmarks of minority rights security. This examine grasps a essential and comparative examination approach to evaluate the resemblances and contrasts between these two perspectives. The revelations illustrate that in show disdain toward of foremost contrasts in their philosophical and methodological bases, both secured law and Islamic law share common goals, to be particular the confirmation of human rights and social value. In any case, contrasts inside the interpretation of these concepts can impact the execution of citizenship approaches in several countries. The think approximately additionally highlights the challenges and openings in coordination Islamic law benchmarks into the framework of display day sacrosanct law, particularly in Muslim-majority countries. At final, the examine proposes the require for more genuinely interest talk to realize a concordant agreeable vitality between secured law and Islamic law in controlling citizenship sensibly and comprehensively.

One of the most crucial subjects in the analysis of statistical models  is the identification of important variables. Therefore, the search for best variable selection methods is a good in obtaining best estimators. The Lasso method is considered the most effective approach for variable selection and parameter estimation in building statistical models with high explanatory power in representing the studied phenomenon. Therefore, using the Lasso method to estimate the parameters of a regression model that contains a dependent variable with data that is censored at zero can be achieved through the use of    Lasso tobit principal component regression, it has attractive properties in estimating the parameters of this model. The our proposed method is illustrated via simulation scenario and a new real data .

Organizational achievement is influenced by the work environment where employees carry out activities also have an important role in improving their performance. The work environment refers to the material and psychological conditions of employees in the organization. This study aims to determine the effect of work motivation and work environment on employee performance at the Kulon Progo Regency Small and Medium Enterprises Cooperative Office. In this study, the data collection techniques used were questionnaires, observation, documentation and literature study. The analysis used is instrument test, classical assumptions, multiple regression, hypothesis testing, and R2 estimation. Sampling by distributing questionnaires directly to 37 samples. The results of data processing from motivation (X1) have a positive and significant effect on employee performance, namely tcount 2.573> ttable 2.032 with a significance of 0.015 <0.05 and the work environment (X2) has a positive and significant effect on employee performance, namely tcount 2.903> ttable 2.036 with a significance of 0.006 <0.05. Fcount value 7.803> Ftable 3.28, with a significance value of 0.002 <0.05. This means that motivation and work environment simultaneously affect employee performance. The results of the coefficient of determination test obtained an R-square value of 0.315 or 31.5%, which means that the influence of the independent variables on the dependent variable and the rest is influenced by other factors outside the research variables.

Epilepsy, or apoplexy, is a chronic disease characterized by recurrent seizures and impaired consciousness due to disorders of the central nervous system. In developing countries, including in RSU Putri Bidadari, epilepsy management is often hampered by high consultation costs, resulting in suboptimal quality of treatment and patient recovery. To overcome this challenge, a system is needed that can facilitate the diagnosis and treatment of epilepsy more efficiently. By using this method, RSU Putri Bidadari can improve the precision of epilepsy diagnosis and determine more appropriate treatment steps, despite limited resources. The Bayes method, as a statistical approach, offers a potential solution to improve the accuracy of diagnosis through data-based probability estimation of diseases and symptoms reported by patients such as frequent hunger, thirst, urination, weight loss, vaginal infections, easy fatigue, tingling legs, and blurred vision. The analysis results of the system show an estimated probability of 73% for patients suffering from generalized epilepsy. The Bayes method-based system is expected to help RSU Putri Bidadari in providing more effective treatment and improving the overall quality of life of epilepsy patients.

This study aims to determine the effect of monetary policy on economic growth. This study uses the Vector Autoregression method by completing the assumption test and estimation on the research variables of inflation, investment, credit, interest rates and gross regional domestic product. Time series research data from 2003-2022 sourced from the Central Bureau of Statistics of North Sumatra (BPS) SUMUT with the results obtained are inflation and interest rates give the results that inflation is influenced by investment and credit, until investment affects gross regional domestic product. Then credit also affects inflation and interest rates and gross regional domestic product is influenced by investment and credit. Thus, in determining policies that encourage economic growth the government must consider a more coordinated monetary policy strategy to deal with the dynamics of strengthening interest rates, controlling inflation, can be measures that support economic stability.

The research comprised multiple simulated tests to determine the relationship between (sample size, distribution parameter value, estimation method, and pollution indivuduales). The experimental findings indicate that the estimator is influenced by sample size, the value of distribution parameter, estimation method, and pollution indivuduales. The results of the mean square error analysis indicate that (robust estimation method) produces the best results with the lowest mean square error, and the best estimation method was (191) of (243) simulation experiments. Additional statistical distributions with additional factors can be performed to demonstrate additional results.

This research aims to analyze the influence of e-commerce and financial technology on income inequality in Indonesia. This research uses panel data from 10 provinces in Indonesia in the 2018-2022 period. This research uses a panel random effect model (REM) regression approach with a GLS (generalized least square) estimation tool. The estimation results from this research show that e-commerce has a negative and significant effect on income inequality in Indonesia. Meanwhile, financial technology has a positive and significant effect on income inequality in Indonesia. It is hoped that the results of this research can be used as consideration for the government in making policies in an effort to reduce income inequality in Indonesia.  

Increasingly advanced technology currently encourages people to make transactions using electronic money (e-money). Currently, more and more Indonesia people are using electronic money, this can be seen from the volume of electronic money transactions which is increasing from year to year. People prefer to make transactions using electronic money because it is easier to transact with traders. This research aims to analyse the variables of interest rates, inflation, money supply, e-money and electronic money transactions in Indonesia. The type of research is quantitative analysis using secondary data from 2011 to 2021 with quarterly annual data take from Bank Indonesia (BI), World Bank and BPS (Central Statistics Agency). The analysis method used is VAR (Vector Autoregressive) and refined with Forecast Error Variance Decomposition (FEVD) analysis. The results of the VAR analysis show that there is a contribution to the variable itself and other variable. From the estimation results, it turns out that there is a reciprocal relationship between one variable and another variable, or in other words, all variable, namely the variable Inflation, JUB, E-Money, Interest Rate and Elecronic Money Transaction contribute to each other. The results of the FEVD analysis show that not all variables contribute to the variable itself.

This study aims to determine the effect of profitability, company size and capital structure on price to book value in banking companies. The research method used is quantitative research. The sampling technique used is using purpose sampling technique. Data analysis using Eviews 12 software and testing is carried out, namely data analysis methods (descriptive), regression model estimation methods, panel data selection, Multiple Linear Regression Analysis, and hypothesis testing. The results show that 1) there is no effect of profitability on price to book value. 2) there is an influence and significant company size on price to book value. 3) there is an influence and significant capital structure on price to book value. 4) the existence of independent variables simultaneously influence and significant to the dependent variable.    

PT Swadaya Graha is a company that works in the field of construction, fabrication and steel manufacturing. One of the machines used in carrying out its operational activities is the Baoji 1660A lathe. The lathe often fails during the production process. This study aims to determine critical components, critical component reliability values, maintenance scheduling intervals and maintenance cost estimates of the Baoji 1660A lathe using the Reliability Centered Maintenance (RCM) method. The results of this study show that the electrical system with an RPN value of 240, v-belt with an RPN value of 168, and gearbox with an RPN value of 60 as critical components. The reliability value of the electrical system approaches 0.5 at a time interval of 640 hours or 80 working days, the reliability value of the v-belt approaches 0.5 at a time interval of 208 hours or 26 working days, and the reliability value of the gearbox approaches 0.5 at a time interval of 384 hours or 48 working days. From the scheduling interval, the estimated total maintenance cost in 2025 is IDR 7,019,092, in 2026 is IDR 3,615,386, in 2027 is IDR 4,187,484, in 2028 is IDR 4,276,752, and in 2029 is IDR 3,615,386.

This study aims to analyze the effect ofproduct innovation and product quality partially and simultaneously oncompetitive advantage at Batak Restaurant inAlam Barajo District, Jambi City. Thisresearch uses a descriptive quantitative approach. The sampling method uses the Structural Equation Model method and uses Maximum Likehood Estimation (MLE). The sample inthis study were consumers at Batak Restaurant in Alam Barajo District, Jambi City, totaling 98 respondents. The data collected using a questionnaire withan ordinal scale. The data analysis method in this study usedSPSS version 25.0 software. The results ofthe study prove that Product Innovation and Product Quality have a significant effect simultaneously on Competitive Advantage, Product Innovation has a significant effect partially on competitive advantage and Product Quality has a partial effect on Competitive Advantage at Batak Restaurant in Alam Barajo District, Jambi City. The product quality variable is the most dominant variable affecting competitive advantage in Batak Restaurant in Alam Barajo District, Jambi City.    

In order to better understand the complex links between infrastructure development, education, tourism, agriculture, and regional economic growth in Indonesia, this study uses structural equation modeling with partial least squares, or SEM-PLS. The research makes use of a 287-observation dataset and applies rigorous measurement model evaluation, bootstrapping analysis, structural model estimation, and model validation against multiple fit indices. The results show a strong positive correlation between each sector—agriculture, tourism, education, and infrastructure development—and regional economic expansion. The necessity for comprehensive, integrated development strategies is highlighted by the interconnectedness of these interactions. In order to promote balanced and resilient regional economic growth, the findings provide policymakers with useful insights and highlight the significance of ongoing investments in infrastructure, education, and sustainable tourism and agricultural practices.    

Predicting sales is an important aspect in sales development. Sales prediction simulation is an estimated calculation of the level of product sales in a certain period. Sales of pempers tend to fluctuate due to the choice of many brands available, resulting in sales having a little difficulty in estimating sales of MAKUKU diaper products at Greens Mart stores. This research aims to predict sales Pempers products. This research uses several stages of identifying problems, determining research objectives, collecting data, data collected or obtained from interviews with salespeople, managing data using Monte Carlo stages, implementing/testing data and testing results to see the accuracy of the method used. The analysis results show that Comfit M and Comfit L have almost the same level of accuracy, namely Comfit M is around 90.63% and sales of Comfit L are 90.48%. These values ​​provide an indication of the level of accuracy of the sales predictions made.      

This research aims to analyze the influence of the labor force, inflation, unemployment and poverty on economic growth in Indonesia for the period (2014-2022). The variables used are Inflation as the dependent variable, Money Supply (JUB) and Interest Rates as the independent variables. The data used in this research is secondary data obtained from publications by the Indonesian Central Bureau of Statistics. The analytical method used is a multiple linear regression model or Ordinary Least Square (OLS). In processing the data, the author used the help of Eviews 10 software. Based on the estimation results, it was found that there is a relationship between Labor Force, Inflation, Unemployment and Poverty on economic growth in Indonesia and the variables Labor Force, Inflation, Unemployment and Poverty have a positive and significant effect on Economic Growth in Indonesia .      

Universities' development hinges significantly on student admissions, necessitating accurate predictions for effective planning. This study applies the Monte Carlo simulation method to forecast new student arrivals at the Faculty of Mathematics and Natural Sciences (FMIPA) at Universitas Negeri Medan (UNIMED). Utilizing data from 2021 to 2023 sourced from the PDDikti website, the research employs PHP programming for implementation. The Monte Carlo algorithm's numerical prowess ensures precise statistical data simulation, comprising data collection, probabilistic distribution computations, cumulative distribution determinations, random number generation, and simulation analyses. Simulation results for 2022, 2023, and 2024 exhibit consistent trends, projecting an average of 860 to 930 new students per program. This methodology surpasses manual estimations, offering robust insights for university resource allocation and strategic management. Despite its effectiveness, study limitations, such as model assumptions, warrant continuous validation with actual data. This research advances predictive modeling in higher education, providing a foundation for future enhancements and comprehensive prediction integrations.

This research applies the Monte Carlo simulation method to predict the movement of Apple Inc.'s stock price over a long period of time. Using historical data of Apple's stock price from 12 December 1980 to 24 March 2022, this study aims to generate a probability distribution of the future stock price. The method involves several steps, including data collection, log return calculation, parameter estimation, and simulation of the stock price path through random iterations based on the log return distribution. The simulation results show that the closing price of Apple stock can be predicted by following the historical trend, although there are differences with the real data due to the stochastic nature of the Monte Carlo technique. This research also applies a variance reduction method to improve simulation efficiency. The findings provide a valuable perspective for investors and financial analysts in identifying investment risks and opportunities through an in-depth understanding of the dynamics of stock price movements using Monte Carlo simulation. Suggestions for future research include the use of VaR methods with historical variance and covariance approaches, as well as considering longer data periods and more stock indices for more comprehensive results.

This research aims to analyze the influence of the Money Supply (JUB) and interest rates on inflation in Indonesia in the 2014-2023 period. The variables used are Inflation as the dependent variable, Money Supply (JUB) and Interest Rates as the independent variables. The data used in this research is secondary data obtained from publications by the Indonesian Central Bureau of Statistics. The analytical method used is a multiple linear regression model or Ordinary Least Square (OLS). In processing the data, the author used the help of Eviews 10 software. Based on the estimation results, it was found that there is a relationship between the Money Supply and Inflation in Indonesia and the Interest Rate variable has a positive and significant effect on Inflation in Indonesia.

A new, economical, sensitive, simple, rapid UV spectrophotometric method has been developed for the estimation of Acyclovir in pure form and pharmaceutical formulation. This UV method was developed using NaOH (0.1N) as a solvent. In the present method, the wavelength selected for analysis was 253nm. Using 1cm match quartz cells, all of the spectrum and absorbance measurements were performed using an UV-VIS spectrophotometer (Shimadzu 1800) was used to carry out the spectral analysis. The ICH guidelines were used to validate the method. Throughout the study, we compared the purity of the drugs, with standard drugs (HPLC grade purity >99%) purchased from SIGMA ALDRICH Ltd. The test drugs from pharmaceutical companies (CIPLA) in its dosage form taken from the market.The method was validated for linearity, range, accuracy, precision, drug content , LOD and LOQ. Linearity was found in the range of 2.5-40μg/ml. Accuracy was performed by using a recovery study. The percentage recovery for all the methods performed was in the range of 99-100% indicating of zero interference of the excipients (in the formulation).   LOD and LOQ were found to be 1.5766434 and 4.7777075 respectively [SD: 0.474474135; SE: 0.212191284]. Relative error (<2) values showed that the proposed procedure exhibited excellent Interday and intraday precision.The proposed UV spectroscopic method was found to be accurate, precise, stable, linear, specific, and simple for quantitative estimation of acyclovir in bulk and pharmaceutical dosage form. Hence the present UV spectroscopic method is suitable for the routine assay of acyclovir in bulk and pharmaceutical formulations.