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.

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

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

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.

The COVID-19 pandemic severely impacted Indonesia, leading to substantial socio-economic disruptions and widespread social anxiety. In response, the government implemented a range of measures, including restrictions and mandatory health protocols, to control the virus. Although the pandemic is officially declared over, the risk of infection remains. Therefore, continued adherence to health protocols is essential. This study aimed to identify the key factors driving public compliance with health protocols in the post-pandemic period within Indonesia, providing valuable insights for policymakers to prevent future outbreaks. The findings revealed that knowledge emerged as the most significant determinant of community adherence to ongoing health protocol implementation. Consequently, the government must consistently emphasize the importance of adhering to health protocols whenever individuals are outside their homes.

Wakatobi is one of the tourist destinations renowned for its underwater beauty, holding great potential to be developed as a sustainable tourism destination. This research aims to determine the priorities for sustainable tourism development in Wakatobi using the Analytical Hierarchy Process (AHP) method. The AHP method is employed to identify and prioritize various criteria and sub-criteria contributing to sustainable tourism development. Based on the research findings, infrastructure and facilities were found to be the highest priorities, with a focus on improving transportation infrastructure and public facilities. These findings indicate that the development of transportation infrastructure and public facilities is crucial to support the sustainability of tourism in Wakatobi.

Bacteria are microorganisms that require adequate media and nutrients to grow and develop. The media commonly used to grow bacteria is Nutrient agar media, but the price of Nutrient agar media is quite expensive. Corn cobs are lignocellulosic wastes that contain nutrients that can meet the nutritional requirements of bacterial growth media. This study aims to investigate the viability of corn cobs as a cost-effective alternative to Nutrient agar media. Specifically, we explore the variance in bacterial colony growth when utilizing corn cobs as a growth medium for Escherichia coli and Staphylococcus aureus bacteria. Employing a true experimental design with a posttest-only control structure, we analyze the treatment's effects using the One Way ANOVA test and Independent Sample T-test. These statistical methods enable us to discern differences between groups utilizing corncob media with and without added sugar.Our findings reveal noteworthy insights into bacterial colony growth. For Escherichia coli, the average number of colonies on corncob media with sugar was 222 CFU/ml, on corncob media without sugar, it was 164 CFU/ml, and on Nutrient agar media, it reached 361 CFU/ml. In the case of Staphylococcus aureus, colonies on corncob media with sugar averaged 871 CFU/ml, on corncob media without sugar, they averaged 657 CFU/ml, and on Nutrient agar media, they reached 942 CFU/ml.Statistical analysis using the One Way ANOVA test for both Escherichia coli and Staphylococcus aureus demonstrated p-values <0.05, signifying significant differences in their growth on corncob media with sugar, corncob media without sugar, and Nutrient agar media. Furthermore, the Independent Sample T-test results for both bacterial strains yielded p-values <0.05, indicating dissimilar growth patterns on corncob media with sugar compared to corncob media without sugar.In conclusion, our study underscores significant disparities in the growth of Escherichia coli and Staphylococcus aureus bacteria among Nutrient agar media, corncob media with sugar, and corncob media without sugar. Notably, we observed a marked increase in bacterial colony counts on corncob media with sugar and corncob media without sugar.