This study aims to analyze the influence of regional financial management, technology utilization, and regional financial accounting systems on the quality of regional government financial reports. The method used in this study is quantitative. Data were obtained by distributing questionnaires to respondents who had been determined using purposive sampling techniques. Data analysis techniques used in this study include descriptive statistics, validity tests, reliability tests, classical assumption tests, and multiple linear regression analysis. The results showed that regional financial management had a significant effect on the quality of regional government financial reports with a t count of 6.333> t table 1.668 and a significance of 0.000 <0.05. The use of technology had a significant effect on the quality of regional government financial reports with a t count of 5.837> t table 1.668 and a significance of 0.000 <0.05. The Regional Financial Accounting System had a significant effect on the quality of regional government financial reports with a t count of 4.102> t table 1.668 and a significance of 0.000 <0.05.

This study aims to examine the role of innovation and business networks in the success of Mixue Pekalongan from the perspective of Islamic business ethics. As a global beverage franchise, Mixue has experienced rapid growth in Indonesia, including in Pekalongan, where the majority of the population is Muslim. The study employs a descriptive qualitative approach, with data collected through in-depth interviews, observations, and documentation. Key informants included the branch manager, employees, and consumers. The findings reveal that innovation includes regular product updates, digital marketing strategies via social media, and efficient, standardized operations. Meanwhile, the business network is supported through centralized supply distribution and cooperation among branches, which enhance operational efficiency and sustainability. The application of Islamic business ethics is reflected in honest transactions, fair treatment of employees, the use of halal-certified materials, and avoidance of usury and uncertainty in contracts. Mixue’s strength lies in its ability to integrate modern innovation with sharia principles, adding value in Muslim communities. The study concludes that innovation and business networks play a significant role in business success. It is recommended that branch offices be given more flexibility in local promotions and improved communication with headquarters. Future research may expand the scope to other branches to gain broader insight.

The “Aku Anak Jujur” campaign is an anti-corruption educational program aimed at elementary and middle school students to instill honesty and integrity from an early age. The program aims to shape young generations to be aware of the dangers of corruption and the importance of honesty in daily life. Conducted online via Zoom, the program involved 16 participants. Materials were delivered through presentations, animated educational videos, interactive discussions, and games such as word scramble and fill the blank. The results showed that the interactive approach effectively engaged participants, enhanced their understanding of anti-corruption values, and encouraged self-reflection on honest behavior. This initiative successfully increased students' awareness of honesty and contributed positively to character development. Similar programs are recommended to be expanded to strengthen an anti-corruption culture from a young age.
 

This study aims to develop an accurate predictive model for identifying students at risk of academic dropout using Decision Tree and Random Forest algorithms. The research utilizes a publicly available dataset sourced from Kaggle, which includes academic and demographic features such as GPA, attendance, credit load, financial aid status, and exam scores. The methodology involves several stages: data collection, preprocessing (handling missing values, encoding categorical variables, and feature scaling), model training, and evaluation using performance metrics such as Accuracy, Precision, Recall, F1-Score, and Confusion Matrix. Results show that the Random Forest algorithm outperforms Decision Tree in terms of accuracy and robustness, with notable feature importance on math, reading, and writing scores. The findings highlight the potential of machine learning in early detection of dropout risks and provide actionable insights for academic institutions to design timely interventions. This research contributes to the growing field of educational data mining and supports data-driven decision-making processes in higher education management.

This journal examines the factors that have the potential to cause delays in the implementation of the Dempsey Hill residential housing construction project, North Citraland, Surabaya. Projects managed by CV. Tirta Kusuma has a target of completion in 10 months, but there are indications of delays that affect the schedule and costs. This study aims to identify and analyze the dominant factors causing delays, such as project management, material procurement, and human resources. The methods used include observation, questionnaire distribution, and literature study. The results of this analysis are expected to provide recommendations to improve time management, resource efficiency, and control of future construction projects. The results of data analysis in this study indicate that project delays are primarily caused by material delays and errors in work execution, as concluded based on the results of factor analysis.

This study presents an evaluation of the cooling load capacity in the departure apron corridor area at Supadio International Airport using the Cooling Load Temperature Difference (CLTD) method. The objective of the research is to assess whether the current air conditioning (AC) is adequate to meet thermal comfort requirements in a tropical climate characterized by high humidity and strong solar radiation. A detailed field survey was conducted at Gate 3, a glass-dominated corridor that experiences significant heat gain from solar exposure. Empirical data including temperature profiles, material properties, occupancy levels, and equipment specifications were collected over a two-week period using digital instruments. Using the CLTD method, the heat contributions from walls, roof, glass surfaces, occupants, lighting, and air infiltration were quantified. The results revealed that the installed AC (2 PK) only delivers about  of the required cooling load, with the total load estimated at 66,448 BTU/hr. The dominant sources of thermal gain include the roof and east-facing glass panels. The study recommends AC resizing, glass shading implementation, and improved insulation to enhance energy efficiency. This research contributes practical insights for HVAC optimization in airport infrastructure within hot-humid climates.

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

Macroeconomic uncertainty poses a structural challenge for the sustainability of micro, small, and medium enterprises (MSMEs) in Indonesia. Fluctuations in inflation, benchmark interest rates, and exchange rates have a direct impact on liquidity, cost structures, and investment decisions. This study aims to explore the types of financial strategies adopted by MSMEs in response to macroeconomic pressures using a systematic literature review and secondary data analysis covering the period from 2019 to May 2024. Data were sourced from official national institutions, including the Central Bureau of Statistics, Bank Indonesia, the Ministry of Cooperatives and MSMEs, as well as relevant academic literature. The findings reveal that MSME financial strategies can be categorized into three key domains: cash management, cost control, and investment planning. MSMEs employing flexible strategies—such as increasing cash buffers, reducing operational costs, and diversifying products or financing sources—demonstrate higher resilience in the face of economic volatility. This study contributes theoretically by enriching the framework of financial adaptation for MSMEs and practically by informing financial literacy programs and policy design. The study also acknowledges limitations in the use of aggregate secondary data and recommends that future research adopt a mixed-method approach to capture undocumented informal financial practices at the grassroots level

In the era of data-driven digital commerce, Artificial Intelligence (AI)-based product personalization has become a key strategy to enhance user experience and foster customer loyalty. However, in the Indonesian e-commerce landscape, there remains a lack of empirical understanding of how personalization systems influence long-term user engagement. This study investigates the impact of AI-driven product personalization on customer loyalty among Indonesian e-commerce users. Employing a mixed-methods approach, quantitative data were collected through an online survey of 150 active users, and qualitative insights were obtained from in-depth interviews with six informants. Statistical analysis using simple linear regression revealed that personalization significantly influences customer loyalty, with a beta coefficient of 0.653 (t = 8.241, p < 0.001) and an R² value of 0.567. Qualitative findings highlight user concerns over recommendation accuracy, interface overload, and repetitive suggestions, which affect emotional satisfaction and platform attachment. This research contributes to the growing body of knowledge on AI adoption in e-commerce by integrating behavioral and technological dimensions of loyalty formation. It also offers practical implications for designing more context-sensitive personalization systems that prioritize not only algorithmic precision but also user control and experience quality.

This study aims to determine the effect of service quality, store atmosphere, and pricing on customer satisfaction at Senada Cafe in Kota Garo, Kampar Regency. The method used in this research is quantitative method. The sample in this study amounted to 99 respondents who were consumers of Senada Cafe, who were taken with non-probability sampling techniques including incidental sampling. Data collection in this study was carried out through distributing questionnaires that had been tested for validity and reliability. Data analysis was carried out using simple linear regression and multiple regression through the SPSS version 29 program. Validity test, reliability test, multicollinearity test, heteroscedasticity test, simple linear regression test, t test, multiple linear regression test, F test and coefficient of determination (R2) test, coefficient of determination (R2). The results showed that the variables of service quality (X1), store atmosphere (X2), and pricing (X3) had a positive and significant effect on customer satisfaction (Y).