Chronic Kidney Disease (CKD) is a major global health issue that can lead to serious complications and long-term medical care. This study aims to identify key clinical factors associated with CKD status using binary logistic regression analysis. The dataset, obtained from Kaggle, contains 400 patient records with various clinical and demographic attributes. The dependent variable is CKD status (positive or negative), while the independent variables include age, blood pressure, hemoglobin level, urine albumin level, and serum creatinine. Initial analysis involved descriptive statistics and multicollinearity checks, followed by model estimation and evaluation using likelihood ratio and Wald tests. The final model identified four significant predictors: blood pressure, hemoglobin, urine albumin, and serum creatinine. The model achieved a high classification accuracy of 95.50% and an Area Under the ROC Curve (AUC) of 98.78%, indicating excellent predictive performance. These results highlight the importance of these clinical indicators in early CKD detection and support their use in risk assessment models for kidney disease screening
Keywords: Chronic Kidney Disease, Binary Logistic Regression, Likelihood Ratio Test, Wald Test, Classification Accuracy

This study aims to determine the level, attribute, and stimulus most considered by respondents in choosing expedition services. The method used is conjoint analysis by applying the conjoint analysis full-profile method to design the stimulus. The attributes and levels used are delivery type (ECO, REG, express, and same day), payment methods (cash and online transfers), service promotions (cost discounts and goods pick-up), and service responsiveness (responses to damage or loss of goods and responses to late delivery). The results of research conducted on 150 respondents showed that delivery type is the most preferred attribute with a value of 0,324. The levels with the largest part-worth of each attribute are REG (0.335), online transfers (0.210), cost discounts (0.270), and response to damage or loss of goods (0.250). The most popular stimulus is expedition services with standard shipping (REG), online transfers, cost discounts, and responses to damage or loss of goods.

Diabetes is one of the non-communicable diseases that is considered dangerous due to its susceptibility to complications. This disease is caused by high blood sugar levels in a person's body, which makes the blood more alkaline and slows down the metabolic process. In this study, we observed 8 variables that are considered influential in diabetes and will build a regression model that can predict the response variable (y) through Logistic Regression Analysis. Logistic Regression Analysis is a statistical analysis method used to describe the relationship between a dependent variable with two or more categories and one or more independent variables that are categorical or continuous. Based on the results, the logistic regression model for factors influencing diabetes in the Indian Pima tribe includes variables such as number of pregnancies, glucose level, blood pressure, body mass index, and diabetes pedigree function

Determining the price of option contracts is a crucial aspect of financial markets, particularly for investors aiming to manage risk and make informed investment decisions. In this study, the price of an Asian call option is calculated using the Monte Carlo Stratified Sampling method based on the stock price data of Tesla, Inc. (TSLA) from January 2021 to December 2023. This method has been proven to reduce variance compared to the Standard Monte Carlo simulation, leading to faster price convergence and more efficient results. The parameters used in the simulation include the initial stock price  (S_0), number of simulations (N), maturity time  (T)dividend = 0, risk-free rate (r), strike price ( K), and volatility

This study compares different machine learning models for time series forecasting in financial data analysis. Models including ARIMA, LSTM, and GRU are applied to predict stock price movements. We measure the accuracy and computational efficiency of each model on various datasets and discuss their strengths and weaknesses in financial forecasting contexts. The findings suggest that deep learning models show significant improvement in capturing complex temporal patterns over traditional methods.

This paper investigates the stability characteristics of fractional order differential equations (FODEs) incorporating time delays. Using the Lyapunov-Krasovskii method, we derive sufficient conditions for the stability of solutions to these delayed fractional systems. The theoretical findings are applied to several examples, including models of population dynamics and engineering systems. Numerical simulations validate the theoretical results, demonstrating the role of time delays in system behavior and stability.

The student study period is one of the important aspects to measure the quality of a higher education institution. The length of the student study period can be assumed to come from internal factors and external factors, so it is necessary to conduct research that aims to identify and model the factors that affect the student study period. The method used in this research is logistic regression and the data used is primary data obtained from distributing questionnaires. The target of this research is aimed at alumni students from the Mathematics Study Programme of Udayana University from 2011 to 2019. In this study, the best model produced has a classification accuracy of 98.17% and the independent variables that have a significant effect on the study period are gender, tuition fees and interest in majors.

When making investment decisions, it is crucial for investors to consider various risks that may arise, both in the short and long term. One method to measure risk is through volatility. Volatility represents a statistical measurement of the degree of price variation over a specific period, expressed as volatility (?) (Aklimawati & Wahyudi, 2013). This study aims to discuss the pricing of European option contracts using Conditional Monte Carlo simulation and the Black-Scholes method. The data used in this study is secondary data obtained from Yahoo Finance. The data consists of quantitative information, namely the monthly closing prices of Toyota Motor Corporation (TM) stock, spanning 5 years from July 1, 2019, to July 1, 2024, yielding 60 data points. In this research, the pricing of European call option contracts was calculated using Conditional Monte Carlo simulation and the Black-Scholes method. The study concludes that European option contract pricing can be determined using two methods: Conditional Monte Carlo simulation and the Black-Scholes method. Conditional Monte Carlo simulation can be employed to calculate European option prices in a structured manner, utilizing stochastic volatility estimated through the Ordinary Least Squares (OLS) method. The two methods yield differing option prices; Conditional Monte Carlo simulation produces lower option price estimates with relatively lower error values compared to the Black-Scholes method at every strike price. The lower estimates from Conditional Monte Carlo simulation are due to its consideration of stochastic changes in volatility, whereas the Black-Scholes method results in higher prices due to its assumption of constant volatility. The comparison demonstrates that Conditional Monte Carlo simulation provides cheaper price estimates under market conditions with non-constant volatility, despite requiring higher computational time compared to the Black-Scholes method.
 
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This paper explores the use of genetic algorithms (GAs) for optimizing nonlinear systems in resource allocation. By simulating various allocation scenarios, we demonstrate the efficiency of GAs in finding near-optimal solutions in complex environments. The study provides a comparison of GA performance against traditional optimization methods and identifies scenarios where GAs outperform. The results emphasize the utility of GAs in real-world applications, especially when conventional approaches struggle with large solution spaces.

This research presents a numerical investigation into heat transfer in nanofluid flow using an advanced lattice Boltzmann method (LBM). The study modifies the standard LBM to incorporate the unique properties of nanofluids, such as enhanced thermal conductivity. We simulate convective heat transfer in a pipe with varying nanoparticle concentrations, assessing the effects on heat transfer rates. Results show that nanofluids significantly improve heat transfer efficiency, offering valuable insights for engineering applications in cooling systems.