The purpose of this study was to determine the effect of shopping enjoyment and sales promotion on impulsive buying of shopee marketplace on students of Riau University. The method used is a quantitative method with a data collection technique in the form of a questionnaire responded to by 384 respondents. The sampling technique uses a purposive sampling technique. This study uses analysis such as validity test, reliability test, normality test, multicollinearity test, heteroscedasticity test, multiple linear regression, t-test, F-test, and coefficient of determination (). Based on the analysis that has been done, the results show that shopping enjoyment and sales promotion have a positive and significant effect on impulsive buying of the Shopee marketplace for students at the University of Riau

Musyarakah Mutanaqisah (MMQ) is a form of sharia-based financing that offers a home ownership solution free from elements of interest (riba). This article aims to analyze the application of the MMQ contract in housing finance, compare it with the conventional mortgage scheme (Kredit Pemilikan Rumah/KPR), and evaluate its impact on community welfare. This study employs a descriptive qualitative approach through literature review of academic sources, regulations, and other relevant data. The analysis shows that MMQ is superior in terms of compliance with Islamic principles, fairness in risk sharing, and flexibility in payment structure. Furthermore, the MMQ scheme contributes to the improvement of social and economic well-being, particularly for the lower-middleincome segment of society. Nevertheless, its implementation still faces challenges such as the risk of default and the need for strengthened risk management systems. Therefore, collaboration between Islamic financial j, the government, and the public is essential to optimize the potential of MMQ as a fair and sustainable alternative for home financing

This research was conducted on students of the Faculty of Economics and Business at Slamet Riyadi University in Surakarta who use the e-commerce platform Tokopedia. The purpose of this study is to analyze the significance of the influence of brand ambassadors, electronic word of mouth (e-WOM), and price perception on purchasing decisions in Tokopedia's e-commerce. The type of data used is quantitative data, with primary data sources employed in this research. The data analysis techniques used in this study include multiple linear regression analysis, classical assumption tests, t-tests, F-tests, and the coefficient of determination (R²).
The results of the multiple linear regression analysis from this study are represented by the equation Y = 6.518 + 0.695 X1 + 0.168 X2 - 0.210 X3 + e. The t-test results indicate that the variables of brand ambassador, electronic word of mouth (e-WOM), and price perception have a significant effect on purchasing decisions in Tokopedia's e-commerce. The F-test results show that the model is appropriate for predicting the influence of brand ambassadors, electronic word of mouth (e-WOM), and price perception on purchasing decisions in Tokopedia's e-commerce. The coefficient of determination (R²) obtained is 0.594, meaning that the independent variables X1 (brand ambassador), X2 (electronic word of mouth (e-WOM)), and X3 (price perception) contribute 59.4% to Y (purchasing decisions). The remaining 40.6% (100% - 59.4%) is explained by other variables not included in this study.

Along with the increasing public interest in fun recreational activities such as water parks, the tourism sector has grown rapidly in recent years. This study examines the effect of physical evidence and perceived value on revisit intention through visitor satisfaction as an intervening variable for visitors to Labersa Waterpark Riau Fantasi. This research was conducted using quantitative research methods. The sampling technique used was Accidental Sampling with a sample size of 98 respondents. The data obtained is then processed using SmartPLS 4.0 software. The results of this research are : (1) physical evidence has a significant effect on revisit intention, (2) perceived value has a significant effect on revisit intention, (3) physical evidence has a significant effect on visitor satisfaction, (4) perceived value has a significant effect on visitor satisfaction, (5) visitor satisfaction has a significant effect on revisit intention, (6) visitor satisfaction is able to mediate the effect of physical evidence on revisit intention, (7) visitor satisfaction is able to mediate the effect of perceived value on revisit intention. 

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.

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.

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.

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 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.

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.