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This study aims to improve the kinesthetic intelligence and physical fitness of students with hearing impairments through rhythmic gymnastics lessons in Physical Education, Sports, and Health (PJOK) at SLB Muhammadiyah Kelayu, East Lombok Regency. The research employed a collaborative classroom action research (CAR) approach involving both the researcher and the PJOK teacher. The action was conducted in two cycles, each comprising planning, implementation, observation, and reflection stages. The research subjects consisted of eight students with hearing impairments, including four females and four males. Data were collected through documentation, observation, and performance assessments using developed kinesthetic intelligence and physical fitness instruments, and then analyzed descriptively using quantitative methods. The results indicate that after the rhythmic gymnastics intervention, kinesthetic intelligence improved, with 75% of students reaching the expected development level by the end of Cycle II. Physical fitness also showed significant improvement across five main components: endurance increased to 68.7%, muscle strength and endurance to 65.6%, agility to 68.7%, flexibility to 65.6%, and balance to 71.8%. Rhythmic gymnastics proved to be an effective approach to support inclusivity while enhancing the kinesthetic intelligence and physical fitness of students with hearing impairments.

This study examines the influence of work discipline, training, and motivation on employee performance at PDAM Tirta Ayu, Tegal Regency. In a public service organization such as PDAM, employee performance is crucial to ensure customer satisfaction and strengthen institutional competitiveness. A quantitative survey method was used with questionnaires distributed to all 115 employees using a saturated sampling technique, as the entire population was included. The independent variables analyzed were work discipline (attitude, compliance with rules, responsibility), training (materials, methods, instructor competence, duration, facilities), and motivation (intrinsic and extrinsic). Employee performance, as the dependent variable, was measured using indicators of work quality, work quantity, teamwork, and problem-solving ability. The data were processed through multiple linear regression to assess simultaneous and partial effects. The research findings revealed that work discipline, training, and motivation each have a positive and significant impact on employee performance, but for training has a positive and insignificant effect, this is shown by the significant results of each significant level for work discipline sig value = 0.000 (<0.05), training sig value = 0.632 (>0.05) and motivation with sig value = 0.000 (<0.05). These results highlight the importance of strengthening employee discipline, implementing well-structured and sustainable training programs, and fostering sustainable motivation strategies. This research provides practical implications for PDAM Tirta Ayu management in designing policies to improve overall employee performance, thereby improving service quality and organizational competitiveness.  

AImbalanced data remains a significant issue in heart disease classification using machine learning, as it tends to cause models to overestimate the majority class while ignoring minority classes with high clinical value. This can lead to a decrease in accuracy and the model's ability to accurately detect disease cases. Therefore, this study aims to assess the effectiveness of oversampling techniques, namely Random Oversampling and Synthetic Minority Oversampling Technique (SMOTE), in improving the performance of the K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF) algorithms. The dataset used comes from Kaggle and consists of 918 data sets with 12 attributes representing patient information related to heart disease prediction. The research stages include data preprocessing, baseline model testing, and re-evaluation using the two oversampling methods. Experimental results show that oversampling can improve the performance of all algorithms. KNN achieved the best results with SMOTE, with an accuracy of 72.98% and an F1-score of 75.39%. In the Naive Bayes algorithm, both oversampling techniques produced relatively stable performance, with the highest F1-score of 73.56% using SMOTE. Meanwhile, Random Forest showed the most optimal performance when combined with Random Oversampling, with an accuracy of 79.19% and an F1-score of 81.51%. These findings confirm that the success of data balancing techniques is strongly influenced by the characteristics of the classification algorithm used, and provide a practical contribution in determining strategies for handling imbalanced data in health research.

The rapid development of digital streaming platforms such as Netflix has generated a large volume of content data with diverse characteristics, thereby requiring effective analytical methods to understand emerging patterns and trends. This study aims to classify Netflix content into two main categories, namely movies and television shows, and to analyze genre trends and content characteristics using a data mining approach with the Naive Bayes algorithm. The dataset used in this study is the Netflix Shows dataset, consisting of 8,809 content entries, with the primary features analyzed including genre, rating, and country of production. The research process begins with data exploration and preprocessing stages, including data cleaning, handling missing values, and transforming categorical features to enable effective model construction. Subsequently, the dataset is divided into training and testing sets to objectively and systematically build and evaluate the Naive Bayes classification model. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics to assess the model’s ability to accurately distinguish between Netflix content types. The experimental results demonstrate that the Naive Bayes algorithm is able to classify Netflix content into Movie and TV Show categories with accuracy, precision, recall, and F1-score values of 100%, respectively. The confusion matrix indicates that no misclassification occurred, suggesting that genre, rating, and country of production features provide a very clear separation between content classes. These findings indicate that the Naive Bayes algorithm can achieve exceptionally high classification performance with optimal evaluation results. The results further reveal distinct differences in characteristics between movies and television shows based on genre and production attributes. Therefore, this study is expected to contribute to the development of content recommendation systems and strategic content management within the streaming industry.

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 analyze the implementation of financial reporting and evaluate the economic performance of Koperasi Simpan Pinjam dan Pembiayaan Syariah (KSPPS) Nur Insani during the period from 2022 to 2023. A descriptive qualitative method was employed, utilizing secondary data from the Statement of Financial Position, Cash Flow Statement, and Operating Results Report published by the cooperative. The findings indicate that KSPPS Nur Insani has implemented a computerized financial recording system, which enhances accuracy, transparency, and operational efficiency. However, the cooperative experienced significant financial pressure in 2023, as indicated by decreases in cash and cash equivalents, total assets, and temporary syirkah funds, both short-term and long-term. These declines reflect weakened liquidity and reduced fundraising capacity from members. Despite these challenges, the cooperative succeeded in increasing its Net Operating Results (SHU), demonstrating effective revenue management and operational cost control. Overall, the profitability of KSPPS Nur Insani remains positive, yet strategic improvements are necessary, particularly in strengthening liquidity management, increasing funding sources, optimizing asset utilization, and enhancing digital system implementation to support better financial governance. These strategic efforts are expected to improve business sustainability and maintain member trust in the future.

Optimizing human resource (HR) performance through a business process reengineering (BPR) approach is a crucial strategy in a competitive and dynamic industrial environment. This study qualitatively examines through a literature review how BPRs radically redesign business processes to improve productivity, quality, time efficiency, and reduce operational costs, with a focus on HR aspects such as recruitment, training, performance appraisal, and career development. The BPR theory by Michael Hammer and James Champy emphasizes the principles of results-based workflow reorganization, the integration of information technology such as ERP and AI, and the empowerment of HR to eliminate task redundancy. In the Indonesian industrial context, the integration of BPR with digital transformation supports Industry 4.0, where HR acts as a catalyst for innovation through adaptive skills and cross-functional collaboration. The study results show that BPR implementation strategies including as-is process analysis, to-be design, change management, and continuous evaluation increase employee motivation, retention, and sustainable competitive advantage. Case studies such as PT Telkom Indonesia and PT Cahaya Mega Group demonstrate efficiency increases of up to 100%. This approach not only streamlines operations but also builds an organization resilient to market and technological disruption.

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.

This study aims to classify customer payment methods at 17 Coffee & Eatery using machine learning algorithms, namely Naïve Bayes and Support Vector Machine (SVM). The increasing use of digital and non-cash payments has generated large volumes of transaction data that are rarely analyzed optimally, even though such data contain valuable information for business decision making. This research used secondary transaction data collected from January to March 2025, consisting of 10,147 transaction records. The dataset included several attributes such as order time, payment time, transaction type, total sales, number of items, and payment method. Data preprocessing was performed through data cleaning, feature engineering, normalization, and label encoding before being divided into training and testing sets with an 80:20 ratio. The Naïve Bayes and SVM models were then trained and evaluated using accuracy, precision, recall, F1-score, and ROC–AUC metrics. The results show that both algorithms were able to classify payment methods effectively, but SVM achieved higher accuracy and more stable performance than Naïve Bayes. These findings indicate that SVM is more suitable for handling complex and heterogeneous transaction patterns. The implementation of machine learning for transaction classification can support more efficient financial management and data-driven decision making for small and medium enterprises in the culinary sector.

This study investigates the relationship between Environmental, Social, and Governance (ESG) performance and corporate financial distress, with board gender diversity examined as a moderating variable. Using 96 firm-year observations from manufacturing companies listed on the Indonesia Stock Exchange (2022–2024), the analysis employs variance-based Structural Equation Modelling (SEM). The findings reveal that ESG performance does not exert a statistically significant effect on financial distress, and gender diversity does not moderate this relationship. These non-significant results constitute the central empirical contribution of the study, highlighting that ESG engagement and gender diversity have yet to translate into financial resilience in the Indonesian manufacturing context. The study underscores the importance of contextual factors—such as implementation costs, authenticity of ESG disclosures, and limited female representation on boards—in shaping the effectiveness of sustainability practices. The results provide theoretical implications for Stakeholder and Agency Theory and offer practical insights for managers, regulators, and investors in emerging markets.

Budget efficiency is an important issue in state financial management because it is directly related to government spending priorities and their impact on public service programs. Discussions about budget efficiency policies are widespread on social media platform X, generating diverse public responses, thus necessitating an automated approach to understand public opinion trends more quickly and objectively. This research aims to analyze the sentiment of Indonesian people toward budget efficiency policies and compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying sentiment. The research data used 10,909 Indonesian-language tweets sourced from a public dataset, which were then processed thru the preprocessing stages including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Sentiment labeling is performed automatically using the Indonesian Sentiment Lexicon (InSet) approach to categorize data into positive, negative, and neutral sentiments. Feature extraction was performed using Term Frequency–Inverse Document Frequency (TF-IDF), and then the data was divided into training and testing sets with an 80:20 ratio. Model performance evaluation was conducted using a confusion matrix and the metrics of accuracy, precision, recall, and F1-score. The research results show that sentiment distribution is dominated by negative sentiment at 56.78%, followed by positive sentiment at 37.40%, and neutral sentiment at 5.83%. In the classification stage, SVM performed best with an accuracy of 86%, while Naïve Bayes achieved an accuracy of 74%. These findings indicate that SVM is more optimal for sentiment classification on social media text data and can be utilized to more effectively support the analysis of public response to budget efficiency policies.

This study aims to examine the effect of investment in fixed assets, financial performance, and thin capitalization on tax avoidance in non-financial companies listed on the Indonesia Stock Exchange (IDX) in 2023. The research utilizes 431 company samples and employsAmultiple linear regression analysis. A descriptive quantitative method with a purposive sampling technique is applied, ensuring that only companies meeting specific criteria are included in the study. The findings.indicate that, simultaneously, the three independent variables have a significant influence on tax avoidance. However, when tested individually, more detailed results emerge. The variable of.investment in fixed assets does not show a significant effect on tax avoidance, suggesting that the size of fixed assets does not necessarily determine a company’s level of tax avoidance. In contrast, financial performance demonstrates a positive effect, indicating that companies with.stronger performance tend to have a greater ability to engage in tax planning. Meanwhile, thin capitalization has a negative effect, meaning that a higher proportion of certain types of debt tends to reduce the level of tax avoidance. These findings provide a more comprehensive understanding of the factors influencing tax avoidance behavior in Indonesia.

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.

Overburden stripping operations in the coal mining industry require optimal performance of loading and hauling equipment to achieve production efficiency. This study aims to evaluate the performance of loading and hauling equipment using the Match Factor method in overburden stripping operations at PT Bumi Artlantis Raya. The results indicate that the equipment combination achieved a Match Factor of 0.85, reflecting moderate compatibility with a potential efficiency improvement of 15%. The actual productivity of Excavator 4002 reached 137.02 bcm/hour (91.35% of the 150 bcm/hour target), while Excavator 4004 exceeded the target with a productivity of 195.73 bcm/hour (130.49% of the target). In contrast, dump truck productivity remained relatively low (Mercedes dump truck: 35.58 bcm/hour; Hino dump truck: 35.40 bcm/hour), primarily due to waiting time during loading and disposal activities. Statistical analysis reveals a strong negative correlation between cycle time and productivity (R² = 0.9929). The optimal cycle time to achieve a Match Factor of 0.80 is 969 seconds, corresponding to an optimal hauling distance of 5.38–6.725 km. Although mechanical availability and physical availability were high (94–100%), the use of availability and effective utilization were relatively low due to an imbalance between loading and hauling equipment. This study concludes that improving equipment coordination, increasing bucket fill factor, enhancing haul road conditions, and implementing preventive maintenance are essential to achieving more optimal operational efficiency in overburden stripping activities.

This study aims to analyze the effect of Environmental, Social, and Governance (ESG) performance on financial misreporting, with investor attention as a moderating variable in banking companies listed on the Indonesia Stock Exchange during the 2019–2022 period. The theoretical framework is grounded in Agency Theory and Legitimacy Theory to explain the role of ESG as an internal control mechanism and a means of gaining external legitimacy. The research employs a quantitative approach using secondary data from annual reports and sustainability reports. Financial misreporting is proxied by earnings management measured through discretionary accruals, while ESG performance is assessed using the GRI Standards index, and investor attention is proxied by institutional ownership. Data analysis was conducted using multiple regression and Moderated Regression Analysis (MRA). The findings reveal that all three ESG dimensions (environmental, social, and governance) have a significant negative effect on earnings management. Institutional investor attention is found to strengthen the negative relationship between environmental and social aspects with earnings management, but weaken the influence of governance. These results indicate that institutional investors tend to be more responsive to environmental and social issues compared to governance aspects. Practically, this study provides empirical evidence that ESG implementation can serve as a control instrument against financial misreporting in the banking sector, while theoretically enriching the literature on investor moderation in the relationship between ESG and earnings management practices.

Inventory is one of the supports for an industry in carrying out its activities, both in the grouping of a machine and the management system in machinery to improve a job. One of the efforts to improve service that can be undertaken is by increasing the amount of inventory owned. The increasing amount of inventory also requires proper inventory management using a database system. Microsoft Access to assist in database design. Before being stored in the database, data and information regarding the inventory will be grouped using the concept of Group Technology (GT) to facilitate the data storage process. Users can be obtained by gathering information from recording staff and the laboratory head through interviews. In addition to defining user needs, the design of the Entity Relationship Diagram (ERD) and Data Flow Diagram (DFD) was carried out to understand the existing system. To analyze the existing system, an analysis is conducted based on Performance, Information, Economy, Control, Efficiency, and Service (PIECES). The results of the validation test, verification test, and prototype test show that the weaknesses of the existing system can be improved and user needs can be met. To identify the weaknesses of the system, a PIECES analysis can be conducted on both the old and new systems. Meanwhile, to determine whether user needs have been met, adjustments can be made by aligning user needs with the prototype through validation and verification tests.

The principle of comparative advantage explains that every country or society, like individuals, can gain benefits from their trade activities by exporting goods or services in which they have a major comparative advantage and importing goods or services in which they do not. Based on the law of comparative advantage, even though a country may be less efficient (having an absolute disadvantage) compared to other countries in the production process, the structure of industrial performance can be seen through the analysis of industrial sector behavior analyzed through various strategies such as Price, Product, and promotion. The theory of comparative advantage related to the exchange of goods is relevant as long as the traded goods are still useful. In other words, Performance is defined as the result of activities influenced by the structure and behavior within the industrial sector, where these results are often measured by the size of a company's market share or profitability in an industry. In more detail, performance can also be reflected in the form of efficiency, development (including market expansion), job creation, employee welfare, and a sense of group pride.

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 the factors affecting real wages of construction workers across provinces in Indonesia from 2010 to 2023 using panel data analysis. The independent variables include Provincial Minimum Wage (UMP), Consumer Price Index (CPI), Open Unemployment Rate (TPT), and Performance Pay (Balas Jasa). A panel dataset of 476 observations from 34 provinces over 14 years was analyzed using three model approaches: Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (REM). The best model was determined through Chow Test, Hausman Test, and Lagrange Multiplier Test, which confirmed that the Fixed Effect Model (FEM) is the most appropriate for analyzing this research data. FEM estimation results show that simultneously, all independent variables (UMP, CPI, TPT, and Performance Pay) have a significant effect on real wages with an F-statistic value of 436,465.9 (p-value = 0.0000 < 0.05), indicating that the model as a whole is highly valid and capable of explaining the variation in real wages collectively. However, partial tests reveal that only the Real Wage variable has a positive and statistically significant effect on Performance Pay (coefficient = 106.3320; t-statistic = 1276.083; p-value = 0.0000), while UMP (p-value = 0.1472), CPI (p-value = 0.6460), and TPT (p-value = 0.6934) show no significant effects at the 5% significance level. The research model demonstrates very high predictive ability with an R-squared value of 0.999735 (99.97%), indicating that the variables studied can explain nearly all variation in real wages of construction workers at the provincial level. This research provides policy implications that improving real wages in the construction sector requires an integrated approach that focuses not only on minimum wage setting but also on regional inflation control, human capital quality improvement, and creating conducive labor market conditions through unemployment reduction

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.