This study aims to analyze the effect of financial distress, capital size, and leverage on accounting conservatism in transportation sub-sector companies listed on the Indonesia Stock Exchange during the 2020–2023 period. This research uses a quantitative approach with multiple linear regression analysis. The data used are secondary data obtained from annual financial reports. The sample consists of 10 transportation companies selected using purposive sampling. The results show that financial distress has a significant negative effect on accounting conservatism, capital size has a significant positive effect, while leverage has no significant effect. Simultaneously, these variables have a significant effect on accounting conservatism with a contribution of 26.9%.

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 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.

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 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 research is a case study with the object of research on Tenun Ikat Ina Sabu located at Jalan Sukun I, RT 009 / RW 004, Oepura Village, Maulafa District, Kupang City, East Nusa Tenggara. The purpose of this study was to determine and explain the production planning of Tenun Ikat Ina Sabu in Kupang City. The types of data used are qualitative and quantitative. Data collection techniques in this study were interviews, observations, questionnaires and documentation. While the data analysis techniques used are Linear Trend and Break Even Point (BEP). The results of the Linear Trend calculation show that in 2024 Tenun Ikat Ina Sabu will produce 383 pieces of sarong, 358 pieces of blanket and 284 pieces of shawl, then the amount produced for 2025, 414 pieces of sarong, 332 pieces of blanket and 303 pieces of shawl, and the amount produced for 2026, 445 pieces of sarong, 402 pieces of blanket and 322 pieces of shawl. The results of the Break Even Point (BEP) analysis in 2023 show that if Tenun Ikat Ina Sabu produces 1,060 pieces of ikat weaving or around Rp.90,343,129, then the company does not make a profit or loss.

In the digital economy era, financial transparency has become a critical aspect for the sustainability and credibility of Micro, Small, and Medium Enterprises (MSMEs). Despite their substantial contribution to employment and economic growth, many MSMEs in Indonesia still rely on manual accounting systems, which limit the accuracy and traceability of financial reporting. This study aims to empirically examine the effect of cloud-based accounting automation on financial transparency among Indonesian MSMEs. Using a mixed-methods approach, the research collected quantitative data from 120 MSME respondents in Jakarta and West Java through structured questionnaires and qualitative data from six key informants through semi-structured interviews. The quantitative data were analyzed using simple linear regression, resulting in a coefficient of determination (R²) of 0.513 and a beta coefficient of 0.598 (p = 0.000), indicating a strong and significant influence of accounting automation on financial transparency. Qualitative findings also revealed improvements in real-time access, reporting accuracy, and transaction traceability. The study confirms that cloud-based digital accounting systems enhance financial transparency by enabling automated integration, audit trails, and timely reporting. These findings contribute to the literature on digital transformation in MSMEs and offer practical implications for policymakers, financial institutions, and software providers to accelerate technology adoption and improve financial governance in the MSME sector.

This study aims to analyze the effects of domestic investments, inflasi, and pengeluaran on Sumatra Utara's income between 2001 and 2020. This study uses the Error Correction Model (ECM) analysis method to determine the impact of independent variables on dependent variables in the pendek data and the Regression Linear Berganda analysis to determine the impact of independent variables on dependent variables in the panjang data. According to the results of the ECM analysis, the variable pengeluaran in the panjang has a significant and positive impact on the sumatera utara's income, while the variable PMDN in the panjang has a positive but not significant effect, and inflasi has a negative and not significant effect. Conversely, the pendek jangka indicates that the pengeluaran variable also has a significant and positive impact on the Sumatera Utara's income, whereas the PMDN and Inflasi variables do not have a significant impact.

The implementation of Supply Chain Management (SCM) makes many businesses manage information, goods, services and money as good as possible so they can provide fast and high-quality services to customers. Therefore, this study aims to implement SCM in Kopi Bintang Salatiga SMEs to decrease production costs, increase revenue, improve operational efficiency and establish good relationships with customers. This study discusses the SCM process and the added value in Kopi Bintang Salatiga SMEs so in the future Kopi Bintang Salatiga SMEs can have good risk management and increase added value. The data was collected through literature study and interviewing the owner of Kopi Bintang Salatiga SMEs. This study found that weather, natural disasters, fires and coffee pests are the factors with the highest risk in the SCM process of Kopi Bintang Salatiga SMEs. The added value of farmers & post-harvest coffee processors of Kopi Bintang Salatiga Kopi Bintang Salatiga SMEs is already quite high at 99%, while at grade I coffee sales is 71% and grade II is 47% it appears that coffee with grade II has lower added value, so it is needing accuracy of coffee processing at farmers & post-harvest coffee processors so the grade I coffee product is increased.

Digital transformation in the Islamic banking sector requires innovation that is responsive to user needs. The BYOND by BSI application is a form of digital transformation by Bank Syariah Indonesia in response to these dynamics. This study aims to examine customer acceptance and satisfaction with the use of the BYOND application based on five recent national studies. The research findings indicate that factors such as perceived usefulness, ease of use, trust, and Islamic banking features play a significant role in driving customer usage and loyalty. Challenges include low digital literacy and concerns about security. With a sustainable approach to feature development and digital education, BYOND has great potential to strengthen the digital Islamic banking ecosystem in Indonesia.