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Student dropout remains a significant challenge for higher education institutions as it impacts academic quality, educational management efficiency, and students' success in completing their studies. Therefore, an approach that can identify students at risk of dropping out is necessary so that timely academic interventions can be made. This study aims to develop a dropout detection model using an Artificial Neural Network (ANN). The data used come from a publicly available higher education dataset, ensuring research reproducibility. Data preprocessing steps were carried out to improve data quality before modeling, and the Synthetic Minority Over-Sampling Technique combined with Edited Nearest Neighbors (SMOTE-ENN) was applied to address class imbalance issues. The ANN model's performance was evaluated using accuracy, precision, recall, F1-score, and area under the ROC curve (ROC-AUC). The test results show that the ANN model can provide excellent predictive performance in detecting at-risk students. The application of SMOTE-ENN also proved to enhance the model’s sensitivity toward the minority class, as indicated by improvements in recall and F1-score. These findings indicate that the developed ANN model has the potential to be used as a student dropout detection system to support data-driven decision-making and strategy development within higher education institutions.

This study aims to analyze the influence of promotion, shopping convenience, advertising, recommendation, comment, and credibility factors on purchase intensity in the TikTok Shop platform among Indonesian households. The rapid development of social commerce services requires a deep understanding of factors driving online purchasing decisions, especially among families as primary users. A quantitative approach was employed, utilizing a Likert-scale questionnaire distributed online. The sample consisted of 150 active TikTok Shop users from various household backgrounds. Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS was used to analyze the direct effects of each factor on purchase intensity. The results indicate that promotion, shopping convenience, and credibility significantly and positively influence purchase intensity, while advertising, recommendation, and comment have relatively weaker yet still relevant effects. These findings underscore the importance of effective promotional strategies, ease-of-shopping enhancements, and building platform credibility to boost purchase intensity within the household consumer segment. The practical implications can guide e-commerce practitioners and digital marketers in formulating adaptive marketing strategies in the era of social commerce.

In the contemporary digital marketplace, consumers increasingly face diverse product choices and brand communications. Understanding the mechanisms through which product quality and brand perception influence customer loyalty remains critical for competitive advantage. The mediating role of customer trust in this relationship has received limited empirical attention within Indonesian market contexts. This research analyzes the direct and indirect effects of product quality and brand perception on customer loyalty, with customer trust as a mediating variable, using Partial Least Squares Structural Equation Modeling (PLS-SEM) methodology on 103 respondents. A quantitative cross-sectional survey design was employed, collecting data via Likert-scale questionnaires (1-5) with 15 measurement items across four latent constructs: Product Quality (5 items), Brand Perception (4 items), Customer Trust (3 items), and Customer Loyalty (3 items). Data analysis utilized PLS-SEM via SmartPLS 3.0, including assessment of measurement model validity (outer model), structural relationships (inner model), and mediation effects through bootstrapping (5000 iterations). The outer model demonstrated adequate validity with 12 of 15 indicators loading above 0.7, and all constructs meeting composite reliability (CR > 0.7) and average variance extracted (AVE > 0.5) criteria. The inner model revealed that product quality significantly influenced customer trust (β = 0.624, p < 0.001), while brand perception showed no significant direct effect (β = 0.045, p = 0.767). Customer trust strongly predicted loyalty (β = 0.650, p < 0.001). Product quality demonstrated a significant indirect effect on loyalty through trust (β = 0.405, p < 0.001), indicating full mediation. The model explained 43.5% of trust variance and 42.2% of loyalty variance. Product quality emerged as the dominant antecedent of customer trust and loyalty, while brand perception did not significantly contribute. Trust served as the critical mechanism translating quality into loyalty. These findings suggest that companies should prioritize quality assurance and consistent delivery over brand marketing campaigns for sustainable loyalty development. The research contributes to mediation theory in consumer behavior and provides actionable strategic guidance for practitioners in emerging markets.

This study aims to compare the effectiveness of QRIS with other cashless payment methods in driving the development of the digital economy in Indonesia. The background to this study stems from the increasing use of digital transactions and the strategic role of QRIS as a national standard for QR-based payments. Google Forms was used to disseminate the survey online and successfully collected 100 respondents who were users of cashless digital payment services. A purposive sampling technique was used to ensure that respondents had experience using QRIS and other digital payment methods. Data were examined using the SmartPLS 4 program using a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach to test the relationships between variables. The results indicate that perceived ease of use, transaction security, and influence on digital behavior have a positive or significant influence on QRIS effectiveness. However, availability and accessibility variables did not have a significant effect. This finding indicates that QRIS effectiveness is more influenced by user experience and perceptions, rather than availability or ease of access. This research is expected to contribute to the development of strategies to increase digital payment adoption in Indonesia.

The rapid development of artificial intelligence, particularly ChatGPT, has created new opportunities to support students’ academic activities in higher education. However, its utilization needs to be evaluated in terms of the alignment between academic task characteristics and technological capabilities to ensure optimal outcomes. This study aims to examine the feasibility of using ChatGPT in students’ academic activities by applying the Task–Technology Fit (TTF) model. This research employed a quantitative approach using Structural Equation Modeling based on Partial Least Squares (SEM-PLS). Data were collected through questionnaires distributed to university students and analyzed using SmartPLS 4 software. The variables examined included Task Characteristics, Technology Characteristics, Task–Technology Fit, Performance Impact, and Utilization. The results indicate that Task Characteristics and Technology Characteristics have a positive and significant effect on Task–Technology Fit. Furthermore, Task–Technology Fit significantly influences Performance Impact and Utilization. Performance Impact also shows a positive and significant effect on the utilization of ChatGPT by students. These findings suggest that the alignment between academic task requirements and the capabilities of ChatGPT plays a crucial role in improving students’ performance and encouraging sustained technology use. The implications of this study highlight the importance of selective and purposeful use of ChatGPT in higher education and provide a reference for higher education institutions in formulating policies related to the ethical and effective integration of artificial intelligence technologies as learning support tools.

This study aims to analyze the effect of the Current Ratio (CR), Debt-to-Equity Ratio (DER), and Return on Equity (ROE) on the stock prices of healthcare companies classified as sharia-compliant on the Indonesia Stock Exchange (IDX) for the 2020–2024 period. The background of the study is motivated by notable stock price fluctuations among sharia healthcare issuers, such as the sharp decline in PT Kimia Farma Tbk and price dynamics of other issuers including KLBF, MIKA, PEHA, and SIDO. The analysis uses a quantitative approach applying Partial Least Squares – Structural Equation Modeling (PLS-SEM) implemented in WarpPLS 8.0. The results indicate that CR does not have a significant effect on stock price (p = 0.174), while DER has a negative but not statistically significant effect (p = 0.484). In contrast, ROE has a positive and significant effect on stock price (p < 0.001), making ROE the dominant factor influencing investor interest. Simultaneously, the three independent variables explain only 20.2% of stock price variation, while the remaining 79.8% is influenced by factors outside the research model. The Tenenhaus goodness of fit (GOF) value of 0.450 suggests the research model has good overall quality despite the limited explanatory power of the tested financial variables.

This study aims to investigate the impact of social media advertising on clothing choices at Universitas Dinamika Bangsa Jambi students. In today's world, where many people, especially young people who frequently shop online, often struggle to accurately determine the quality of items. A quantitative approach was employed, with a survey as the primary method of data collection. A questionnaire was distributed online via Google Forms and successfully elicited responses from 102 active students who are also social media users. The sampling technique used was purposive sampling, with participants selected based on criteria that matched the focus of the study. The data were then processed using SmartPLS 4 software with the Partial Least Squares Structural Equation Modeling (PLS-SEM) method to test the relationship between variables. The main findings indicate that social media promotions have a strong positive influence on students' clothing purchasing decisions. This underscores the crucial role of targeted advertising strategies in the digital world in shaping consumer preferences. This research is expected to serve as a guide for clothing entrepreneurs in developing online marketing plans that better suit the tastes and needs of students as their target market.

The development of information technology has encouraged restaurants and cafés to function not only as dining places, but also as venues for hosting various events. However, the event reservation process at Rumah Makan Ny. Hartini and Café Rain is still carried out manually through logbooks, telephone calls, and WhatsApp, resulting in problems such as unorganized data, delayed confirmations, and miscommunication with customers. In addition, the manual system limits access to information regarding venue availability, reservation schedules, and additional facilities required by customers. This study aims to develop a web-based event reservation information system using the prototyping method. The system design was carried out using Unified Modeling Language (UML), including use case diagrams, activity diagrams, and class diagrams to model user interactions, process flows, and system structure. The results of the study show that the developed system is able to automate the reservation process, customer data recording, reservation confirmation, schedule management, and additional facilities management. This system improves operational efficiency, data accuracy, and service quality, while also making it easier for customers to make reservations independently and obtain information quickly and accurately.

WPS Office stands for Writer, Presentation, and Spreadsheets—a software suite offering diverse office functions, including document processing, spreadsheet creation, and presentation tools. This study analyzes user satisfaction levels and the influence of the variables Content, Accuracy, Format, Ease of Use, and Timeliness on WPS Office application users in Jambi City, using the End User Computing Satisfaction method. Data were gathered through an online questionnaire distributed to students in Jambi City who had used the application; created via Google Forms, it garnered 385 responses. Post-collection, analysis was conducted using Structural Equation Modeling in SmartPLS software version 4. Of the five hypotheses tested, four were accepted. The results reveal that accuracy, format, ease of use, and timeliness positively and significantly influence user satisfaction, while content shows no significant effect.

The development of digital technology has led to an increase in the use of short video-based entertainment applications, including the Melolo application. However, the free version still has various complaints, such as inconsistent subtitles, unintuitive navigation, force close glitches, and unstable advertisements, so user satisfaction analysis is needed. This study aims to measure the level of satisfaction of users of the free version of the Melolo application using the End User Computing Satisfaction (EUCS) method, which covers five variables, namely content, accuracy, format, ease of use, and timeliness. Data was collected through an online questionnaire of 385 Melolo app users in Jambi City and analyzed using Structural Equation Modeling (SEM) with the help of SmartPLS 4. The results showed an R-Square value of 0.546, indicating that the model was able to explain 54.6% of the changes in user satisfaction levels. The variables of content and timeliness were found to have a significant effect on user satisfaction, while accuracy, format, and ease of use had no significant effect. These results indicate that content quality and system timeliness are the main factors in increasing user satisfaction. Therefore, Melolo app developers are advised to maintain content quality and improve system performance and stability to optimize the user experience.

This study aims to analyze and test the mediating role of job satisfaction in the relationship between career development and job stress on employee loyalty at BPR Agung Sejahtera. This study used a quantitative methodology to collect primary data from 90 employees of BPR Agung Sejahtera through questionnaires. Partial Least Squares (PLS) with Structural Equation Modeling (SEM) was used to test the relationship between latent variables. Data testing included validity, reliability, bootstrapping , Adjusted R-Square, Path Coefficient, and Specific Indirect Effects . The results showed that career development had a positive and significant effect on job satisfaction. Conversely, job stress was found to have a negative and significant effect on job satisfaction. Career development also had a positive and significant effect on employee loyalty. Job stress had a negative and significant effect on employee loyalty. Job satisfaction had a positive and significant effect on employee loyalty. However, the mediating role of job satisfaction in the relationship between career development and job stress on employee loyalty proved to be insignificant. This finding indicates that employee loyalty is more influenced by good career development and low levels of job stress than the mediating role of job satisfaction. Overall, these findings conclude that managing career development and work stress through job satisfaction is crucial for increasing employee loyalty. It is hoped that these research findings will help BPR Agung Sejahtera's management better manage its human resources to achieve the company's goals.

This study explores how nascent entrepreneurs in Indonesia can improve their business innovation performance by utilizing volunteerism and digital competencies, particularly in the context of financial and technological limitations. These challenges significantly hinder their capacity for effective innovation, especially in resource-limited settings where access to tools and expertise is often scarce. A quantitative approach was employed, using data from 156 nascent entrepreneurs, which was analyzed through Structural Equation Modeling (SEM). The results indicate that the voluntary exchange of ideas and competences, framed through the Service-Dominant Logic (S-DL) perspective, plays a critical role in overcoming resource constraints and enhancing innovation outcomes. By facilitating collaboration and knowledge-sharing via volunteerism, nascent entrepreneurs can strengthen their digital and managerial capabilities, which are essential for driving innovation. The study highlights the importance of creating ecosystems that support stakeholder-driven volunteer initiatives, which help develop strategic digital competencies among emerging entrepreneurs, thereby fostering greater innovation capacity and ensuring long-term business sustainability.

This study aims to examine and analyze the effects of product quality and distribution channel on repurchase intention, with customer satisfaction and customer trust serving as mediating variables. The research employed a quantitative method with a causal-explanatory approach. The study population consisted of customers at the Weber building materials manufacturing company in East Java, with a sample of 275 respondents selected using purposive sampling. Data were collected via questionnaires and assessed using a Likert scale. The data analysis was conducted using the Structural Equation Modeling (SEM) technique with Partial Least Squares (PLS) software. The findings of the study reveal the following: 1) Product quality has a significant positive effect on customer satisfaction; 2) Product quality has a significant positive effect on customer trust; 3) Product quality has a significant positive effect on repurchase intention; 4) Distribution channel has a significant positive effect on customer satisfaction; 5) Distribution channel has a significant positive effect on customer trust; 6) Distribution channel has a significant positive effect on repurchase intention; 7) Customer satisfaction has a significant positive effect on customer trust; 8) Customer satisfaction has a significant positive effect on repurchase intention; 9) Customer trust has a significant positive effect on repurchase intention.

This study aims to analyze the influence of Corporate Image and Service Quality through Customer Satisfaction on Customer Loyalty at Semen Indonesia distributors in Gresik using the Structural Equation Modeling (SEM) approach. The study population consisted of 102 customers, all of whom were sampled. The results indicate that Corporate Image and Service Quality have a significant influence on Customer Satisfaction. Both variables were also shown to have a significant influence on Customer Loyalty. However, unlike several previous research findings, Customer Satisfaction did not have a significant influence on Customer Loyalty. This finding indicates that in the context of the cement distribution business, customer loyalty is not formed solely from satisfaction, but is more influenced by other factors such as consistent service quality, a strong corporate image, competitive pricing, and long-term business relationships. This study emphasizes the importance of strengthening image and improving service quality as key strategies for maintaining customer loyalty.

This study investigates the impact of auditor competence and red flag awareness on fraud detection ability, examining the moderating role of professional skepticism. As fraudulent financial reporting poses a critical threat to the integrity of financial disclosures and stakeholder trust, understanding the key factors influencing an auditor's detection capabilities is essential. This study employed a quantitative approach, gathering data from auditors at Public Accounting Firms (KAP) in Bali Province via a four-point Likert scale questionnaire. The data were subsequently analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4 software. The findings indicate that both auditor competence and an awareness of red flags significantly and positively enhance fraud detection capabilities. Conversely, professional skepticism, when analyzed for its direct influence, demonstrated a significant negative effect on this ability. Furthermore, skepticism exhibited a complex moderating role: it significantly weakened the positive relationship between competence and fraud detection, while not significantly moderating the link between red flags and detection ability. These results provide crucial theoretical contributions by revealing the nuanced and sometimes counter-intuitive role of professional skepticism. Practically, they inform policy for audit firms and regulatory bodies, suggesting that while fostering competence and red flag awareness is vital, the application of skepticism requires a more sophisticated and refined approach to truly enhance audit quality and overall fraud detection effectiveness.

This study aims to examine and analyze the effects of product quality and distribution channel on repurchase intention, with customer satisfaction and customer trust serving as mediating variables. The research employed a quantitative method with a causal-explanatory approach. The study population consisted of customers at the Weber building materials manufacturing company in East Java, with a sample of 275 respondents selected using purposive sampling. Data were collected via questionnaires and assessed using a Likert scale. The data analysis was conducted using the Structural Equation Modeling (SEM) technique with Partial Least Squares (PLS) software. The findings of the study reveal the following: 1) Product quality has a significant positive effect on customer satisfaction; 2) Product quality has a significant positive effect on customer trust; 3) Product quality has a significant positive effect on repurchase intention; 4) Distribution channel has a significant positive effect on customer satisfaction; 5) Distribution channel has a significant positive effect on customer trust; 6) Distribution channel has a significant positive effect on repurchase intention; 7) Customer satisfaction has a significant positive effect on customer trust; 8) Customer satisfaction has a significant positive effect on repurchase intention; 9) Customer trust has a significant positive effect on repurchase intention.

As the center for employee competency development, BSI Corporate University (BSU) plays a crucial role in strengthening BSI’s competitiveness within Indonesia’s rapidly growing Islamic banking industry. BSU’s performance from 2021 to 2023 experienced fluctuations, indicating the need for an evaluation of factors influencing employee performance. Previous studies have also shown inconsistent findings regarding the influence of Islamic Work Ethics and AKHLAK core values on performance, thereby creating a research gap. This study aims to examine the influence of Islamic Work Ethics and AKHLAK core values on employee performance at BSU. Data were collected through a quantitative survey of BSU employees, resulting in 60 valid responses, and analyzed using SmartPLS 4.1.1.2. The research employed a descriptive quantitative approach with Partial Least Squares Structural Equation Modeling (PLS-SEM) as the analytical technique. The findings indicate that Islamic Work Ethics and AKHLAK core values have a positive and significant influence on employee performance.

This study analyzes the impact of artificial intelligence on youth digital political engagement through a Structural Equation Modeling approach. The development of algorithmic technology has changed the way young people access, assess, and react to political information, requiring an empirical understanding of its mechanisms of influence. This study explores how digital literacy, trust in AI, and perceived usefulness shape online political participation. Data was obtained from an online questionnaire targeting individuals aged 17-30 who are active with AI, then analyzed using the SEM-PLS 4 method. The main findings reveal that digital literacy and trust in AI have a strong influence on perceived usefulness, which acts as a key mediator in encouraging such participation. This indicates that the impact of AI is not direct, but rather occurs through cognitive processes that guide young people in assessing the benefits of technology. The implications of this research emphasize the importance of strengthening digital literacy, algorithm transparency, and responsible AI implementation to strengthen inclusive youth political participation in the digital environment.

The purpose of this study is to investigate the connection between the probability of financial statement fraud and the components of the Fraud Hexagon: pressure, opportunity, rationalization, capability, arrogance, and collusion. Additionally, we examine how Environmental, Social, and Governance (ESG) Disclosure functions as a moderator. Banks listed on the Indonesia Stock Exchange (IDX) between 2021 and 2024 are the subject of this study. We make use of secondary data gathered from business sustainability and annual reports. Purposive sampling was used to choose the bank sample depending on the completeness of the data. We use the Partial Least Squares (PLS) method of Structural Equation Modeling (SEM), which works well for evaluating models with complex variables, for the analysis. The results of this study are expected to provide insights into how each element of the Fraud Hexagon contributes to financial statement fraud and how ESG Disclosure can mitigate these risks.

Diseases in primary health services exhibit complex spatial-temporal dynamics due to urbanization and population mobility. Conventional surveillance approaches are difficult to capture these patterns adaptively. Machine learning (ML) based on spatio-temporal modeling offers a solution with the ability to detect disease clusters automatically and with high precision. Research Objectives: This research aims to develop a machine learning model to detect disease hotspots from primary service data in Indonesia, with a focus on improving prediction accuracy, interpretability, and relevance of health policies. Methodology: The primary service dataset for 2024 (5,343 entries) was analyzed using three ML models Gradient Boosting Machine (GBM), Temporal Random Forest (TRF), and Multi-EigenSpot with spatial (village) and temporal (week, month) features. Performance evaluation includes predictive (AUC, F1-score) and spatial (Moran's I, Spatio-Temporal Correlation Index) metrics. Results: The results showed that Multi-EigenSpot achieved the best performance (AUC=0.91; F1=0.86), with the detection of dominant hotspots in Sungai Asam and Beringin Villages. Moran's I value of 0.63 indicates a strong spatial autocorrelation, while STCI=0.57 indicates moderate temporal stability. Conclusions: ML-based spatio-temporal models are effective in identifying hidden disease patterns and have the potential to be integrated into national digital surveillance systems. This approach supports precision public health by providing a scientific basis for real-time location- and time-based intervention policies.