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This study investigates the long-term impact of adaptive digital learning ecosystems on students' self-regulated learning (SRL) behaviors and academic persistence. Adaptive learning systems personalize the learning experience by adjusting content and feedback to meet individual students' needs, preferences, and performance. These systems enhance engagement, motivation, and learning outcomes through real-time adjustments and continuous feedback. The research aims to explore how adaptive learning systems influence SRL and academic persistence in university courses over time. Using a longitudinal quantitative design, the study tracks SRL behaviors and academic persistence at multiple points during the semester. Results show significant improvements in SRL behaviors such as goal setting, planning, self-monitoring, and reflection among students engaged with adaptive learning environments. These students exhibited greater autonomy, improved metacognitive awareness, and higher motivation. Additionally, students in adaptive systems demonstrated greater academic persistence, as indicated by more time spent on tasks, higher assignment completion rates, and sustained engagement. The findings suggest that adaptive learning platforms promote SRL and academic persistence by offering personalized, responsive learning experiences. Unlike static, non-adaptive environments, adaptive systems provide dynamic support, enhancing students' ability to regulate their learning and remain engaged despite challenges. The study concludes that adaptive learning systems are vital for long-term academic success, though further research is needed to assess the sustainability of these effects in various educational settings and among diverse student populations.

In the current era of digital transformation, organizations are increasingly turning to Artificial Intelligence (AI) to enhance the precision and agility of managerial decisions. This study investigates how AI adoption within management accounting systems influences decision-making efficiency and managerial performance. Using survey data collected from managers and accountants in medium-to-large firms across Central Java, the research applies Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the hypothesized relationships. The results reveal that AI adoption significantly improves decision-making efficiency, which in turn enhances managerial performance. Furthermore, leadership support strengthens the relationship between AI adoption and decision-making efficiency, indicating that human and organizational factors remain critical for realizing technological benefits. The findings contribute to management accounting literature by elucidating the mechanism through which AI technologies create managerial value specifically, by accelerating analytical processes and improving the quality of managerial judgments. Practically, the study suggests that firms should focus on leadership involvement, data governance, and employee training to ensure that AI systems complement, rather than replace, human judgment. Overall, this study highlights that successful AI adoption in management accounting is not merely a technical transition but a strategic and cultural evolution toward evidence-based decision-making.

This study aims to deeply analyze the contribution of work motivation and work environment conditions in improving employee satisfaction and performance. The methodology applied is a Systematic Literature Review (SLR) following the guidelines of Wahono (2015) and Kitchenham (cited in Fauzi et al., 2018). Data were collected through literature exploration in Google Scholar, Garuda Ristekdikti, and university journal websites with a publication limit between 2020 and 2025. Through a selection process, a number of relevant articles were successfully collected for narrative analysis. The results of the study revealed that work motivation has a positive and significant influence on employee performance, both directly and indirectly through job satisfaction as a mediator. A supportive work environment also plays a crucial role in increasing employee enthusiasm, productivity, and job satisfaction. Overall, the synergy between high work motivation and a conducive work environment can create maximum performance and strengthen employee loyalty to the organization. The results of this study provide importance for human resource managers to pay more attention to motivation and work conditions as an approach to improving performance and efficiency in organizations.

This study aims to analyze the influence of work discipline and work motivation on employee performance at Bank Sulutgo Tondano. The research method used is a quantitative approach with multiple linear regression analysis. The population in this study consists of all 76 employees of Bank Sulutgo Tondano, and the sample was obtained using a saturated sampling technique. Data collection was carried out through questionnaires. The results of the study indicate that partially, work discipline has a positive and significant effect on employee performance, and work motivation also has a positive and significant effect on employee performance. Simultaneously, work discipline and work motivation have a significant effect on employee performance, with a coefficient of determination of 44.4%, indicating that these two variables are able to explain the variation in employee performance. Meanwhile, 55.6% is influenced by other factors outside the scope of this study. Therefore, it can be concluded that improving work discipline and work motivation will have an impact on employee performance at Bank Sulutgo Tondano.  

While TLS 1.3 is the latest standard, TLS 1.2 remains widely implemented in many cloud infrastructures. The selection of cipher suites in TLS 1.2, particularly between AES-128-GCM and AES-256-GCM, presents a trade-off between cryptographic strength and system performance. This research aims to analyze the performance comparison of these two algorithms on an Nginx server to determine the optimal configuration for cloud storage services. The study uses a quantitative experimental method by benchmarking two scenarios: (A) Strict (AES-256-GCM), and (B) Balanced (AES-128-GCM). Performance metrics measured include Requests Per Second (RPS), Latency, and Throughput. The results show that handshake performance (RPS and Latency) is nearly identical across all scenarios. However, in large file transfer tests, the AES-128-GCM algorithm (Scenario B) achieved a throughput of 32.4 MB/s, which is 12.5% faster than AES-256-GCM (28.8 MB/s). This study concludes that AES-128-GCM provides the best balance of security and efficiency for data-intensive environments.

This study examines the determinants of green innovation and its impact on sustainable business performance among micro, small, and medium enterprises (MSMEs) in the Bandung Raya region of Indonesia. Specifically, the study analyzes the influence of environmental knowledge and market pressure on green innovation, as well as the effect of green innovation on sustainable performance. A quantitative approach was employed using data from 150 MSMEs, and structural relationships were tested through multiple regression analysis. All measurement instruments demonstrated high reliability (Cronbach’s Alpha 0.89–0.95) and validity (corrected item–total correlation > 0.80). The results reveal that environmental knowledge has the strongest positive and significant effect on green innovation (β = 0.728; p < 0.001), indicating that MSMEs with greater environmental awareness are more likely to adopt eco-friendly innovations. Market pressure also significantly influences green innovation (β = 0.257; p < 0.001), demonstrating the role of consumer expectations, competition, and green product trends in shaping sustainable business practices. Furthermore, green innovation has a very strong and significant impact on sustainable business performance (β = 0.847; p < 0.001), suggesting that eco-friendly practices enhance cost efficiency, customer satisfaction, firm reputation, and environmental outcomes. Overall, the study highlights the importance of combining internal awareness with external pressures to foster green innovation and strengthen sustainability among MSMEs. The findings provide theoretical contributions to green innovation and sustainability frameworks, while offering practical implications for MSMEs, policymakers, and business support institutions.

This study investigates the influence of financial literacy, entrepreneurship training, and financial inclusion on the performance of micro, small, and medium enterprises (MSMEs) through business sustainability. Using a systematic literature review (SLR) examines the impact of financial literacy, entrepreneurship training, and financial inclusion on MSME performance through business sustainability mediation by synthesizing empirical data from 12 research (2020–2025) across Scopus and Web of Science. Positive direct effects on sustainability (financial literacy via budgeting/risk management; training via adaptive resilience; inclusiveness via digital access) and performance metrics like profitability/growth are confirmed by results using the PRISMA 2020 flow.  Amid obstacles like financial access restrictions and COVID-19 disruptions, business sustainability appears as a crucial mediator, linking these factors to improved MSME results in developing contexts (Africa, Indonesia). Practical implications compel policymakers to give integrated literacy programs, contextual training, and inclusive finance top priority. Theoretical contributions combine financial literacy, entrepreneurial learning, and sustainability ideas into a holistic mediation model. The results highlight the importance of integrating financial education, entrepreneurial skill development, and inclusive financial systems to strengthen MSME resilience and competitiveness. This study provides practical implications for policymakers, financial institutions, and support organisations in designing effective interventions that foster sustainable business growth. The research also contributes theoretically by confirming the mediating role of business sustainability in the relationship between financial literacy, entrepreneurship training, financial inclusion, and MSME performance. Future studies may expand these insights by examining additional contextual factors such as digital technology adoption and business networking that further support sustainable MSME development.

This study aims to examine the influence of product innovation, digital marketing, and business networking on the competitiveness of small and medium-sized enterprises (SMEs), with customer engagement positioned as a mediating variable. Employing a Systematic Literature Review (SLR) approach, thirty Scopus-indexed articles published between 2020 and 2025 were analysed to synthesise theoretical and empirical insights related to SME competitiveness in contemporary digital and urban business environments. The findings indicate that product innovation, digital marketing, and business networking each play a significant role in strengthening SME competitiveness, particularly within markets characterised by rapid technological change. Customer engagement emerges as a critical mediating mechanism that connects these strategic variables to sustainable competitive advantage. It enhances the impact of innovative and digital strategies by fostering stronger emotional, behavioural, and participative interactions between SMEs and their customers. The review also highlights that SMEs adopting integrated digital management practices, such as the utilisation of human-resource information systems (HRIS) and data-driven decision-making tend to demonstrate greater adaptability, market responsiveness, and long-term performance. The study contributes theoretically by integrating resource-based and dynamic capability perspectives, offering a holistic understanding of how digital and relational capabilities interact to elevate competitiveness. Practically, the findings provide strategic guidance for policymakers, SME managers, and practitioners in designing innovation-oriented and digitally enabled initiatives that support sustainable SME growth in the digital era.

This study was conducted to analyze the relationship between students’ learning motivation and their perception of mathematics with mathematics achievement among tenth-grade students at SMA Al Islam 1 Surakarta. Mathematics was chosen as the focus because it is often considered a subject that presents significant challenges for students, both in terms of conceptual understanding and in shaping positive attitudes toward learning. The study employed a correlational quantitative method, with the population consisting of all tenth-grade students and the sample limited to class X-9, comprising 36 students. The instruments used were a motivation questionnaire, a perception questionnaire, and mathematics learning outcomes obtained from mid-semester test scores. Data analysis included normality testing and Pearson correlation to identify the relationship among variables. The results indicated that learning motivation did not have a significant correlation with mathematics achievement. In contrast, students’ perception of mathematics showed a significant correlation with their academic performance. These findings suggest that students’ positive perceptions of mathematics can play a crucial role in supporting better academic achievement, while motivation alone may not guarantee higher performance. This study contributes to a deeper understanding of the factors influencing mathematics learning outcomes and provides a foundation for further research to develop more comprehensive tools for measuring motivation. Future studies are also encouraged to consider additional variables such as teaching strategies, parental involvement, and the overall learning environment in order to present a broader analysis of student success.

This study aims to explore students' experiences, reflections, and perceptions of innovative learning in drama studies through digital performance. This qualitative descriptive research utilized a case study approach with 36 students enrolled in the Drama Studies course (2025/2026) within the Indonesian Language and Literature Education program at Universitas Singaperbangsa Karawang. Primary data consisted of students' written reflections and digital performance artifacts, supported by secondary data from literature on constructivism, dual coding/multisensory theory, and emotional intelligence, as well as curriculum documents. Data collection involved documentation, participatory observation, and semi-structured interviews. Data analysis employed thematic analysis through systematic steps including reading, coding, categorization, interpretation, and presentation. Data validity was ensured through source triangulation, member checking, and peer debriefing. The findings indicate that digital performance provides positive learning experiences, strengthens the integration of theory and practice, fosters problem-solving skills, and develops collaboration, empathy, and students' emotional intelligence. These results affirm that digital performance-based drama learning is a pedagogical innovation relevant to 21st-century competencies and can serve as a model for project-based curriculum development in literature education.

This study aims to examine the strengthening of school literacy culture through the establishment of a Literacy Corner at SMPN 4 Mangarabombang as a strategy to enhance students’ reading interest and engagement in literacy activities. The research problem arises from students’ low reading intensity and the limited availability of literacy-supporting facilities at school. A qualitative descriptive method was employed, utilizing observation, interviews, and documentation as data collection techniques. The findings indicate that the Literacy Corner significantly improves students’ access to reading materials, fosters more consistent reading habits, and creates a more conducive learning environment. Moreover, the involvement of teachers and students in managing the Literacy Corner strengthens their sense of ownership and increases active participation in school literacy activities. By facilitating a space dedicated to reading, the initiative addresses the challenges related to the scarcity of resources and encourages students to make reading a regular activity. Teachers also reported increased student motivation and engagement in literacy programs, which translates into greater academic performance. These results highlight that establishing a Literacy Corner is an effective and strategic approach to reinforcing school literacy culture. Furthermore, it can serve as a model for other schools aiming to enhance their literacy programs and improve student engagement in reading activities.

This study aims to examine the effect of green innovation on company value, with financial performance as a mediating variable, in the mining industry. This study uses a systematic literature review approach by examining various relevant previous studies. The results of the study indicate that green innovation plays a significant role in improving environmental performance and operational efficiency of companies, which in turn positively impacts financial performance. Good financial performance is a key factor in strengthening company value and stakeholder trust. These findings confirm that the implementation of green innovation not only supports environmental sustainability but also provides long-term economic benefits for mining companies. This study also found that companies that successfully implement green innovation tend to have a better image in the eyes of investors and the public, which contributes to increasing the company's market value. These findings confirm that the implementation of green innovation not only supports environmental sustainability but also provides long-term economic benefits for mining companies, strengthening their position in an industry that increasingly prioritizes sustainability and social responsibility.

The use of protective equipment in the form of helmets is an important aspect of ensuring motorcycle rider safety. However, violations of helmet usage still frequently occur and are difficult to monitor continuously. This study proposes a real-time helmet detection system using the YOLOv8 object detection method. The YOLOv8n model was trained using a helmet and no-helmet image dataset that underwent data augmentation to improve the model’s robustness against variations in environmental conditions. The system was implemented using the Python programming language with the support of the Ultralytics and OpenCV libraries. The system input was obtained from a webcam with a resolution of 640×640 pixels, where each video frame was processed in real time to detect the Helmet and No Helmet classes. The system displays bounding boxes and class labels in real time and is equipped with a violation duration calculation mechanism. When a no-helmet condition is detected continuously, the system generates pop-up alerts and automatic notifications via the Telegram application. The experimental results show that the system is capable of detecting helmet usage and no-helmet violations in real time with stable performance. The integration of violation duration calculation helps reduce momentary detection errors and improves the reliability of identifying valid violations

Managing Regional Public Service Agency (Badan Layanan Umum Daerah/BLUD) hospitals requires planning and budgeting processes that are accountable, measurable, and aligned with service performance. In practice, BLUD planning is still constrained by fragmented applications (hospital information system/SIMRS, finance, human resources, e-office, and procurement), duplicate data entry, approval delays, and limited monitoring of process compliance. This study aims to analyze requirements and design a web-based BLUD planning information system using an Enterprise Application Integration (EAI) approach through middleware to improve cross-system interoperability, data consistency, and the timeliness of executive reporting. The study adopts the Design Science Research (DSR) framework, comprising problem identification, definition of solution objectives, artifact design and development, demonstration, evaluation, and communication/report writing. The proposed system includes a unit-based budget proposal module and item management, a role-based approval workflow (RBAC) with SLA tracking, a budget ceiling (pagu) master to benchmark proposals, audit trails and report exports, and an executive dashboard integrating budget perspectives, service indicators (e.g., bed occupancy rate/BOR and patient visits), and process compliance. It also provides an integration design via middleware (ESB/message broker) supported by a canonical data model (CDM) and traceable logging (trace_id/correlation_id). Evaluation using black-box testing and API contract testing indicates that the main planning workflow operates as intended and the integration interfaces are consistently defined, providing a foundation for staged implementation and further performance evaluation.

The National Student Arts Festival and Competition (FLS3N) is an event aimed at developing students’ talents and achievements in the arts, including solo vocal competitions. The assessment process in this category involves multiple criteria, which may lead to subjectivity in decision-making. This study aims to design and develop a web-based Decision Support System (DSS) for selecting non-academic students in the FLS3N solo vocal category using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The assessment criteria are based on the 2025 FLS3N Technical Guidelines, consisting of voice quality, vocal technique, expression, and performance. The TOPSIS method is applied to generate alternative rankings based on the highest preference value. The system is developed using a web-based software development approach and tested using participant data from both male and female categories. The results indicate that the system can provide objective and consistent ranking recommendations, thereby assisting schools in selecting the best students to represent them in the FLS3N competition.

Information technology (IT) governance plays a strategic role in supporting the achievement of organizational goals, especially in higher education institutions. Adiwangsa University Jambi, as a private university, is required to manage IT effectively, efficiently, and in line with the institution's vision. This study aims to analyze the level of IT governance capabilities at Adiwangsa Jambi University using the COBIT 2019 framework with a focus on the EDM (Evaluate, Direct, Monitor) domain. The research method used is a descriptive quantitative method through data collection using questionnaires, interviews, and documentation studies. The results show that the level of IT governance capability in the EDM domain is at level 2 (Managed Process), indicating that the IT governance process is running but still needs improvement in several aspects, especially in monitoring and controlling IT performance. This study is expected to provide recommendations for Adiwangsa University Jambi in improving IT governance in a sustainable manner.

Toko Jahit SA’aminah is a business engaged in tailoring services and the sale of sewing supplies that still manages data manually using record books. This condition causes several problems, such as slow data recording, the risk of data loss or damage, difficulties in monitoring the status of tailoring work and inventory, as well as obstacles in preparing tailoring service and sales reports. This study aims to design and develop a web-based tailoring service and sales information system to optimize the effectiveness and efficiency of operational performance. The system development method used is the waterfall method, which includes the stages of requirements analysis, system design using UML (Use Case Diagram, Activity Diagram, and Class Diagram), implementation using the Laravel framework with the PHP programming language and MySQL database, as well as system testing using the Black Box Testing method. The results show that the developed system is able to facilitate the management of tailoring service and sales data, monitor the status of tailoring work, check the availability of sewing supplies, and accelerate the preparation of tailoring service and sales reports to be submitted to the owner of Toko Jahit SA’aminah.

Employee performance is a strategic aspect that plays an important role in achieving company goals, especially in facing increasingly fierce business competition. Employee performance is not formed directly, but is influenced by various internal organizational factors, such as work discipline, compensation systems, and work environment conditions. This study aims to analyze the influence of work discipline, compensation, and work environment on employee performance at PT Dunia Aneka Usaha. This study uses a descriptive quantitative approach. The sampling technique used is a saturated sample, so that the entire population of 70 employees was used as research respondents. Data were collected through questionnaires compiled based on indicators of each variable and measured using a Likert scale. Furthermore, the data were analyzed using validity and reliability tests to ensure the quality of the research instrument, as well as statistical analysis in the form of t-tests, F-tests, and multiple linear regression analysis to test the proposed hypotheses. The results of the study indicate that work discipline, compensation, and work environment have a positive and significant effect on employee performance, both partially and simultaneously. These findings indicate that improving work discipline, providing appropriate compensation, and creating a conducive work environment can encourage improved employee performance. Therefore, companies are advised to manage these three factors sustainably so that employee performance improves and organizational goals can be achieved optimally.

Sudden Infant Death Syndrome (SIDS) is a sudden and unexpected death in infants that is often associated with the prone sleeping position. This study aims to develop an automated monitoring system capable of detecting SIDS risk factors using the YOLOv8 algorithm and to analyze the effect of data augmentation on model performance. The dataset consists of two classes, baby-lying-on-back (supine) and baby-lying-on-stomach (prone), which were processed through model training and evaluation using precision, recall, F1-score, and mAP metrics. The model was trained under two scenarios, without data augmentation and with data augmentation. The results show that the model without augmentation achieved a precision of 90%, recall of 85%, F1-score of 86%, and mAP50 of 93.7%. After applying augmentation, performance improved to a precision of 90%, recall of 87%, F1-score of 88%, and mAP50 of 95.1%. These findings indicate that augmentation increases detection accuracy and enhances model generalization, including robustness against variations in lighting and camera angles. Furthermore, testing with image and video inputs revealed that the non-augmented model exhibited a tendency toward overfitting, particularly in favor of the baby-lying-on-stomach, whereas the augmented model successfully classified both classes accurately. The developed system is also equipped with an alarm feature and early-warning notifications via Telegram to smartphone when a prone position is detected for a certain duration. Overall, the results demonstrate that YOLOv8 with data augmentation is effective for an automated, non-invasive monitoring system for infants, making it suitable for detecting and preventing potential SIDS risk factors.

Sentiment analysis is a technique in Natural Language Processing (NLP) used to identify user opinion tendencies based on textual reviews. This study analyzer user reviews of the Maxim application on the Google Play Store and compares three Machine Learning algoritmhs-Naïve Bayes, Support Vector Machine (SVM), and CatBoost-in classifying sentiment. The research stages include data collection, text preprocessing, feature extraction using TF-IDF and Chi-Square, class balancing using SMOTE, and performance evaluation through Accuracy, Precision, Recall, and F1-Score. ANOVA is used to examine the influence of feature selection on model performance. The results show that each model exhibits different performance level across the tested feature combinations. The CatBoost achieved the highest accuracy of 99,26% and demonstrating the most stable performance. Meanwhile, the Naïve Bayes and SVM models experienced performance decreases experiments, especially after applying SMOTE. These findings indicate that the choise of algorithm, feature extraction method, and class balancing technique significantly affects classification outcomes. Overall, CatBoost is identified as the best-performing model, providing more consistenst classification result in accordance with the characteristics of the user reviews.