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

Breast cancer remains one of the leading causes of death among women worldwide, primarily due to late detection and diagnosis. Early and accurate prediction is essential to improve survival rates. Machine learning (ML) techniques have proven effective in supporting early diagnosis. This study aims to evaluate and compare the performance of three different approaches: traditional ML, ensemble ML, and deep learning (DL) for early prediction of breast cancer using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The methodology includes data collection, preprocessing, and the design of predictive models. Traditional ML algorithms used include Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), and Decision Tree (DT). Ensemble ML techniques comprise Random Forest (RF), XGBoost, and AdaBoost, while DL models include Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). The models were evaluated using precision, recall, F1-score, and accuracy. The results indicate that XGBoost achieved the highest accuracy (0.99), with strong recall (0.98) and F1-score (0.986), outperforming all other ensemble and traditional ML methods. CNN achieved 0.99 in all evaluation metrics, slightly outperforming RNN, which attained 0.98 accuracy and 0.985 F1-score. These findings confirm that ensemble ML techniques outperform traditional models, while CNN leads among DL models. Furthermore, the proposed models demonstrated superior prediction performance compared to existing studies, particularly in minimizing false negatives, which is critical for healthcare applications.

This study aims to describe the psychological elements of the character Kinan based on the id, ego and superego in the novel Layangan Putus by Mommy asf, to describe the form of psychological problems in the character Kinan in the novel Layangan Putus by Mommy Asf, and to describe the form of conflict defense mechanisms in the character Kinan in the novel Layangan Putus by Mommy Asf. The research used a qualitative approach. While the method used is the content analysis method. The source of funds in this study is the text of the novel Layangan Putus by Mommy Asf published by RDM Publisher in 2020, the first printing. The data collection technique is a written documentation technique. The data analysis technique is a qualitative descriptive technique. The findings of this study are as follows: (1) Psychological elements of the Kinan character were found based on the id, ego and superego, namely there is an uncomfortable id, a pain id and an unfulfilled need id, there is an angry ego in the Kinan character and there is that there a high personal standard superego, (2) It was found were 5 psychological problems in the Kinan character, including: mood disorders based on subjective feelings consisting of feelings of sadness, anger and disappointment, there were anxiety disorders, stress disorders, somatoform disorders and personality disorders including: adventitious, borderline, obsessive-compulsive, histrionic and narcissistic.

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.

Menstruation is one of the signs of the development of the reproductive system in women. Menstruation states that women have reached the point of reproductive maturity ready to be fertilized, this is inseparable from Premenstrual syndrome which is a sign before menstruation. This study aims to determine the relationship between physical activity and symptoms of premenstrual syndrome. In this study using quantitative methods. Through 2 questionnaires of physical activity and premenstrual syndrome, the data obtained correlation between the dependent variable and the independent variable lies in the significance of the results of statistical calculations, namely the significance obtained is the Sig value. 0.195. Which has a number smaller than 0.05, therefore it is stated that there is no correlation between the two variables studied or the correlation in the two variables is not too significant or can be called weak. The conclusion is in line with the data obtained by the researcher, there is an influence of physical activity on premenstrual syndrome symptoms in women with data that 80% of students have a high level of physical activity and 68% of respondents stated that they had quite high premenstrual syndrome symptoms. This states that physical activity has an influence on premenstrual syndrome in female students, but the level of influence is not very significant in each individual.

Education is a key factor in human resource development, especially in rural areas that often face challenges in accessing quality education. The Afternoon Tutoring Program (Bimbel Sore) conducted by KKN-MAS students from Universitas Muhammadiyah Prof. Dr. HAMKA in Sidorejo Village aims to enhance elementary school students' academic understanding and learning motivation. This program was attended by 30 students and focused on core subjects such as Mathematics, Indonesian Language, and Natural Sciences using an interactive approach, including group discussions, quizzes, and Q&A sessions. Evaluation results showed an increase in students' comprehension, with an average score improvement of 25%, as well as greater active participation in learning. Furthermore, the program contributed to boosting students' confidence in facing academic challenges. This initiative is expected to serve as a model for improving education quality in rural areas and inspire other community service activities. The sustainability of this program can be strengthened by involving more volunteers and expanding the range of taught materials to better align with students' needs in the future.

This study aims to analyze the potential of superior commodities in North Tapanuli Regency, especially pineapple commodities, using the Location Quotient (LQ) method. North Tapanuli Regency is known to have abundant agricultural resources, but not all of them have been optimized for export purposes. In this study, secondary data in the form of GRDP in 2024 from BPS was used as the basis for calculating LQ to identify the base sector. The results of the study showed that of several agricultural sub-sectors, pineapple fruit commodities had the highest LQ value of 7.89, which indicated that pineapple has a comparative advantage and is very potential to be developed as an export product. To support the development of this commodity, a SWOT analysis was conducted on two main processed products, namely pineapple dodol and pineapple chips. This analysis shows that pineapple is not only superior in terms of production quantity, but also has great potential to be developed into high value-added products. These findings provide an important contribution to the strategy for developing local commodities that are export-oriented and support sustainable regional economic development.
 

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.

This community service aims to provide an understanding of financial literacy and training and guidance related to the use of social media to disseminate their products online to students of Muhammadiyah Wanaraja Garut High School. The problem faced by partners in this programme is the lack of understanding of students of Muhammadiyah Wanaraja Garut High School regarding financial literacy. The method of implementing this activity using the Participatory Action Research (PAR) method is carried out starting from the socialisation stage at the beginning of the service activity, in the form of providing information to partners / communities about community service activities to be carried out, the objectives of the activity, the stages of implementing the activity, and the schedule for implementing the activity based on an agreement between the proposing team and partners, the next stage of training and mentoring and evaluation of activities. The results of the activity showed an increase in the literacy knowledge of students of Muhammadiyah Wanaraja Garut High School (SMA) related to financial literacy before and after training.

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.