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This study examines the effect of working capital on firm performance and sustained growth among consumer non-cyclicals manufacturing companies listed on the Indonesia Stock Exchange (IDX) over the period 2019–2023. Working capital is operationalized through three distinct proxies derived from Akgün and Memiş Karatəs (2021): the Cash Holding Level (CHL), which measures the proportion of cash and cash equivalents relative to total assets; the Cash Interactive Effect (CIE), which captures the efficiency of converting revenue into operating cash flow; and the Gross Working Capital Ratio (GWCR), which reflects the share of current assets within total assets. Firm performance is assessed through Return on Assets (ROA), Return on Equity (ROE), and Tobin’s Q, while sustained growth is measured using the model proposed by Gerson et al. (2025), expressed as SG = b × ROE, where b denotes the earnings retention ratio. Panel data regression analysis is applied to 225 firm-year observations drawn from 45 companies. The study employs the Fixed Effect Model (FEM) for ROA and ROE, and the Random Effect Model (REM) for Tobin’s Q, as determined by the Hausman specification test. The findings reveal that CHL and CIE exert significant positive effects on ROA and ROE, while CIE is the only proxy to produce a statistically significant positive effect on Tobin’s Q. With respect to sustained growth, CHL and GWCR demonstrate significant negative effects, whereas CIE shows a significant positive effect, indicating that operational efficiency dimensions of working capital actively support long-term growth sustainability. These results reinforce the liquidity management theory and contribute empirical evidence that the structure and efficiency of working capital are strategic determinants of both short-term financial performance and long-term growth sustainability in Indonesia’s consumer goods manufacturing sector.

Social media has evolved into a significant platform where consumers freely express their opinions, experiences, and levels of satisfaction regarding various products, including those offered by Micro, Small, and Medium Enterprises (MSMEs). The comments and reviews shared by customers on these platforms contain diverse sentiments that can serve as valuable indicators of how consumers perceive product quality. Understanding these sentiments is crucial for MSME owners, as it allows them to evaluate their products and adapt to market expectations more effectively. This study aims to analyze customer sentiment toward MSME products on social media by utilizing the Naïve Bayes algorithm, a widely used classification method in text mining. The data used in this research consist of customer comments collected from various social media platforms. The research process involves several stages, including data collection, manual labeling of sentiments, text preprocessing (such as tokenization, case folding, and stopword removal), and splitting the dataset into training and testing subsets. Subsequently, the classification process is carried out using the Naïve Bayes algorithm to categorize sentiments into positive, negative, and neutral classes. The results of this study demonstrate that the Naïve Bayes method is effective in classifying customer sentiments with a satisfactory level of accuracy. These findings provide a comprehensive overview of consumer perceptions regarding the quality of MSME products. Furthermore, this research is expected to assist MSME business owners in understanding customer feedback more systematically and using it as a basis for improving product quality and enhancing customer satisfaction in a competitive digital marketplace.

This study examines the effect of transfer pricing, thin capitalization, and intangible assets on tax avoidance among manufacturing companies listed on the Indonesia Stock Exchange (IDX) during 2022-2024. Using a purposive sampling method, 90 firms were selected, yielding 262 firm-year observations after removing 8 outliers from an initial pool of 270. Tax avoidance is proxied by the Cash Effective Tax Rate (CETR); transfer pricing by the Related Party Transaction ratio (RPT); thin capitalization by the Debt-to-Equity Ratio (DER); and intangible assets by the ratio of intangible assets to total assets. The results indicate that transfer pricing has a significant negative effect on tax avoidance, thin capitalization has a significant negative effect on tax avoidance, and intangible assets do not significantly affect tax avoidance. The model is jointly significant (F = 25.422; p < .001) with an Adjusted R² of 21.92%, indicating that 21.92% of the variation in tax avoidance is explained by the three independent variables. These findings carry important implications for tax authorities seeking to strengthen oversight of related-party transactions and the capital structures of multinational enterprises.

This study examines customer churn prediction in subscription-based telecommunications from a digital marketing perspective using machine learning. The analysis utilizes a secondary dataset of 7,043 customer records that simulate behavioral, contractual, and financial attributes commonly found in telecom services. Three classification algorithms Logistic Regression, Random Forest, and Gradient Boosting are applied to model churn behavior. Data preprocessing includes handling missing values, encoding categorical variables, and splitting data into training and testing sets. Model performance is evaluated using accuracy, recall, and ROC-AUC, with emphasis on recall due to its importance in identifying at-risk customers. The results show that Gradient Boosting achieves the highest overall performance with an ROC-AUC of 0.84, while Logistic Regression provides relatively higher recall. Key drivers of churn include short-term contracts, higher monthly charges, and lower service engagement. However, recall remains moderate, indicating limitations in capturing complex behavioral factors. These findings suggest the need to combine predictive models with behavioral insights and highlight the importance of early customer engagement and long-term retention strategies.

Based on Law Number 11 of 2006 concerning the Government of Aceh, the Government of Aceh has been granted special authority to manage and regulate various governmental affairs, including land administration. This authority is specifically outlined in Article 16 paragraph (1) letter k, which empowers the Government of Aceh to handle land affairs that span across regencies and municipalities. This power is further reinforced by Presidential Regulation Number 23 of 2015, which transformed the Regional Office of the National Land Agency of Aceh into the Aceh Land Agency, and Qanun Aceh Number 13 of 2019 regarding the Establishment and Organizational Structure of Aceh Regional Apparatus, which laid the foundation for the establishment of the Aceh Land Office. This office is responsible for managing and certifying the Aceh Government’s land assets. This study aims to assess the implementation of the Aceh Land Office’s authority in certifying government land assets, the institutional synergy with the National Land Agency, and the challenges faced in ensuring legal certainty over these assets. The research employs an empirical juridical method with a sociological approach. Data were gathered from interviews with officials from the Aceh Land Office, the Aceh Financial Management Agency, and the National Land Agency, as well as secondary data from legal documents, books, and academic resources. The study found that while the certification process is legally supported, challenges such as incomplete ownership documents and limited resources hinder effective implementation. Measures, such as re-tracing and remapping land assets, are being taken to address these challenges.

The phenomenon of mafqud (missing heirs) creates complex legal issues within Islamic inheritance law, particularly regarding the realization of justice and legal certainty in the distribution of estate assets. This study examines the legal position of mafqud as a temporary impediment in inheritance allocation and analyzes the procedural mechanisms applied by religious courts in determining mafqud status. Employing a normative juridical method, this research relies on primary legal materials, including the Qur’an, Hadith, classical fiqh references, and relevant judicial decisions, supported by secondary materials such as academic books and scholarly journals. The findings indicate that mafqud serves as a temporary barrier to inheritance distribution, requiring careful judicial assessment to ensure the protection of all heirs’ rights while remaining consistent with Islamic legal principles and the applicable national legal system. Therefore, structured and systematic procedures implemented by religious courts play an essential role in guaranteeing both justice and legal certainty in inheritance disputes involving mafqud.

This study aims to analyze the implementation of Statement of Financial Accounting Standards (PSAK) 109 concerning Accounting for Zakat, Infaq, and Sadaqah at BAZNAS Tegal Regency and to assess its level of compliance with the applicable standards. This research is motivated by the importance of transparency and accountability in the management of zakat funds as a form of responsibility to the public and stakeholders. The study employs a qualitative method with a descriptive approach through observation, interviews, and documentation studies of the institution’s financial statements. The results indicate that BAZNAS Tegal Regency has prepared its financial statements in accordance with the components required by PSAK 109, including the statement of financial position, statement of changes in funds, statement of changes in managed assets, statement of cash flows, and notes to the financial statements. In terms of distribution, presentation, and disclosure, the implementation of the standard has complied both formally and substantively. However, in the aspects of recognition and measurement, the implementation is not yet fully comprehensive, as there has been no realization of non-cash asset receipts and no impairment testing has been applied to non-cash assets. Overall, the implementation of PSAK 109 has been administratively well executed, but further strengthening is required in technical and procedural aspects to ensure more optimal, consistent, and comprehensive application in accordance with sharia accounting principles.

This study analyzes the impact of fixed asset revaluation on corporate income tax liabilities in the domestic shipping sector. The background of this research is based on the importance of accurate fixed asset valuation in tax calculations, particularly for assets subject to value fluctuations, such as ships. The objective of this study is to identify the difference between the book value and the revalued value of ship fixed assets, and to analyze its implications for tax liabilities. This research uses a quantitative approach with descriptive analysis methods, utilizing secondary data from the financial statements of PT. ABC as a case study. The Wilcoxon signed-rank test was used to test the significant difference between the book value and the revalued value of assets. The results show a significant difference between the book value and the revalued value of PT. ABC's ship assets, resulting in an increase in tax liabilities. This research has significant implications for companies in implementing proper accounting practices and complying with applicable tax regulations, as well as for tax authorities in improving supervision and tax compliance.

Corporate financial performance is an important factor in maintaining business sustainability amid increasingly intense competition. One of the commonly used indicators of financial performance is Return on Assets (ROA), which reflects a company’s ability to generate profits through the efficient use of its assets. Corporate profitability is influenced by various internal factors, including capital structure and liquidity. This study aims to analyze the effect of Debt to Equity Ratio (DER) and Current Ratio (CR) on Return on Assets (ROA). This research employs a quantitative approach using secondary data obtained from corporate financial statements. The research sample consists of 36 observations selected through purposive sampling. Data analysis techniques include descriptive statistical analysis and multiple linear regression analysis using SPSS software. The results show that, partially, the Debt to Equity Ratio does not have a significant effect on Return on Assets, while the Current Ratio has a positive and significant effect on Return on Assets. Simultaneously, Debt to Equity Ratio and Current Ratio have a significant effect on Return on Assets, with Current Ratio being the most dominant variable. The findings indicate that effective liquidity management plays a crucial role in improving corporate profitability. The implications of this study are expected to provide useful insights for corporate management in making financial decisions, particularly related to liquidity management and capital structure.

The increasing volume and complexity of data have made traditional 2D visualization methods insufficient for effectively exploring and understanding high-dimensional datasets. Immersive Virtual Reality (VR) presents a promising solution by providing an interactive 3D environment that enhances spatial understanding, task efficiency, and user satisfaction. This research aims to evaluate the user experience (UX) and interaction design quality of immersive VR interfaces for 3D data visualization in complex environments. The study employs a mixed-methods approach, combining usability testing, UX questionnaires, and task-based performance analysis. Participants interacted with VR prototypes designed to visualize complex data and were assessed on their ability to manipulate and explore the data efficiently. The findings show that immersive VR interfaces significantly improve spatial comprehension, reduce cognitive load, and increase task performance efficiency compared to traditional 2D systems. Additionally, user satisfaction was notably high, with participants appreciating the intuitive and engaging interaction methods. The study concludes that immersive VR can provide substantial benefits in real-world data visualization applications, particularly in domains requiring the exploration of complex and high-dimensional data. However, further research is needed to optimize VR interfaces and address challenges such as motion sickness and interaction complexity.

This study explores the role of visual analytics in enhancing decision-making processes within creative industries, focusing on its application to large-scale multimedia datasets. Visual analytics integrates interactive visualization techniques with computational algorithms, enabling users to explore complex datasets intuitively and derive actionable insights. The research centers on the design and implementation of interactive dashboards tailored to the creative sector, particularly film, music, and advertising industries, to facilitate real-time data exploration. The study also investigates the usability of these tools through expert-based evaluations, aiming to assess their effectiveness in supporting informed and timely decision-making. The findings reveal that interactive visualizations significantly improve insight discovery and pattern recognition, enabling decision-makers to uncover hidden trends in large multimedia datasets. However, challenges related to scalability, user acceptance, and real-time processing were encountered during the implementation phase. The research highlights the practical benefits of integrating visual analytics into industry workflows, which include enhanced content creation, audience engagement, and strategic planning. Furthermore, the study identifies key visual analytics techniques such as dynamic dashboards, pattern recognition, data mining, and clustering, which are essential for analyzing multimedia data. The study concludes by emphasizing the potential for wider applications of visual analytics in other sectors, suggesting future research directions to improve tool performance, scalability, and user accessibility, as well as exploring the integration of emerging technologies like artificial intelligence and virtual reality.

This study compares the scalability and maintainability of three prominent programming paradigms-functional programming (FP), object-oriented programming (OOP), and declarative programming (DP)-in the context of distributed data processing systems. The research aims to evaluate how each paradigm performs under increased data volume and its ability to handle complex operations, while also assessing the ease of maintenance through code readability, modularity, and the flexibility of updating and debugging. The study employs a comparative experimental design, implementing identical data processing tasks, such as data aggregation, filtering, and transformation, across each paradigm. Key findings indicate that FP and DP outperform OOP in terms of scalability, with their stateless nature and high-level abstractions enabling efficient parallel processing and task distribution. FP, with its emphasis on immutability and concurrency, and DP, with its focus on describing desired outcomes rather than implementation specifics, both demonstrate superior performance in handling large datasets. However, while OOP excels in modularity and flexibility, its reliance on mutable state and shared resources hampers its scalability in distributed environments. In terms of maintainability, both FP and DP offer clearer, more maintainable code due to their abstraction levels, making them easier to update and extend. OOP, while modular, presents challenges in managing mutable state, complicating maintenance. This paper concludes with practical recommendations for developers on when to use each paradigm based on system requirements and suggests areas for future research, such as hybrid paradigms and long-term maintainability studies in real-world applications.

This study presents a comprehensive comparative analysis of four traditional machine learning algorithms Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine for Android malware detection using the preprocessed TUANDROMD dataset comprising 4,465 instances and 241 features representing both static and dynamic application characteristics. Motivated by the limitations of conventional signature-based and hybrid detection methods, especially in managing imbalanced datasets and detecting emerging malware variants, the study employed SMOTE to ensure balanced training data and fair model evaluation. The dataset was divided into 80% training and 20% testing subsets, and models were assessed using key performance metrics including accuracy, precision, recall, F1-score, and ROC AUC. The findings revealed that the proposed Random Forest model outperformed the other classifiers, achieving an accuracy of 0.993, precision of 0.992, recall of 1.000, F1-score of 0.996, and a near-perfect ROC AUC of 0.9998 surpassing state-of-the-art approaches. These results affirm the superior predictive capability, consistency, and robustness of the Random Forest algorithm in Android malware detection. The study concludes that base models, when integrated with class-balancing techniques, provide reliable and efficient malware detection across imbalanced datasets. For future research, the study recommends exploring advanced hybrid or ensemble frameworks that integrate Random Forest with deep learning architectures or other meta-heuristic optimization techniques to further enhance detection accuracy, adaptability, and resilience against rapidly evolving Android malware threats.

This study aims to examine and analyze the effect of capital structure and fixed asset turnover on return on assets (ROA) at PT Argo Pantes Tbk during the period 2013–2022. The research employs a quantitative method with a statistical analysis approach, using secondary data obtained from the company’s financial statements. The data analysis techniques include classical assumption tests, namely normality, multicollinearity, heteroscedasticity, and autocorrelation tests, followed by multiple linear regression analysis. Hypothesis testing is conducted through the coefficient of determination, partial statistical testing (t-test), and simultaneous statistical testing (F-test). The results indicate that capital structure has a significant effect on return on assets. This is evidenced by a t-value of 2.986, which is greater than the t-table value of 2.36462, and a significance level of 0.020, which is lower than 0.05. These findings suggest that an optimal capital structure plays an important role in improving the company’s profitability performance. Therefore, capital structure management should be considered a strategic factor by management in enhancing financial efficiency and overall corporate performance.

This study examines how company size, asset growth, tangibility, leverage, and total asset turnover affect profitability in consumer manufacturing companies listed on the Indonesia Stock Exchange from 2019 to 2023, using secondary data collected via purposive sampling. The independent variables in this study include the natural logarithm of total assets, asset growth (this year’s total assets relative to the previous year), and tangibility (the fixed asset ratio to total assets). Leverage uses the debt-to-asset ratio, and total asset turnover uses the total asset turnover ratio, while the dependent variable of profitability uses return on assets. Of the 108 companies in the population, 19 that met the research sample criteria were selected, yielding 95 observations. Data analysis was conducted using multiple linear regression, accompanied by classical assumption tests and hypothesis testing through F-tests and t-tests. The findings of this study reveal that asset growth has a significant positive effect on profitability, while leverage shows a significant negative effect. However, firm size, tangibility, and total asset turnover do not exhibit significant relationships with profitability. This study contributes both theoretically and practically to understanding the internal determinants of financial performance in the consumer sector and serves as a reference for management.

This research examines the analysis of Return on Equity (ROE), Quick Ratio (QR), and Debt to Equity Ratio (DER) on corporate valuation, as assessed by Price-to-Book Value (PBV), within technology firms listed on the Indonesia Stock Exchange (IDX) during the period from 2022 to 2024. The primary aim of this investigation is to ascertain the effects of profitability, liquidity, and leverage both in isolation and in conjunction on market valuation in an industry characterized by innovation and intangible assets. This research employs panel data regression analysis utilizing EViews 13 as the quantitative methodology. The findings reveal that ROE significantly enhances PBV, indicating that investors place considerable importance on firms that are capable of generating substantial returns on equity for shareholders. Conversely, QR and DER appear to have no discernible impact on PBV. This observation can be attributed to the unique nature of technology companies, wherein investors prioritize factors other than short-term liquidity and leverage. Nonetheless, when assessed collectively, the three metrics illuminate the variations in corporate value. These results suggest that while financial stability indices exert a positive yet comparatively subdued effect on investor sentiment within the technology sector, profitability remains a paramount determinant. The study elucidates the financial determinants that influence corporate value in innovation-driven industries, providing valuable insights for managers and investors alike.

This study aims to assess the quality of financial reporting at the Arda Jaya Diesel car repair shop on Jalan Dg Hayo Antang 3. The data used are numerical data or quantitative methods. The study was conducted by collecting data from the repair shop's financial reports, such as revenue, expenses, debt, inventory, and fixed assets. The assessment was carried out by examining whether the repair shop has followed accounting principles, how to record transactions, and whether the profit and loss statements and balance sheets are accurate and reliable. The method used is a case study supported by interviews and direct observations at the repair shop. The results show that many repair shops still record their finances simply and do not follow proper accounting standards. Therefore, financial reports need to be improved to assist business decision-making and meet the reporting needs of external parties such as creditors and investors.

This study focuses on analyzing the influence of sustainability reports, free cash flow, and sales growth on the financial performance of energy sector companies listed on the Indonesia Stock Exchange (IDX) during the 2019–2023 period. Financial performance is measured using Return on Assets (ROA) as the main indicator to assess a company's ability to generate profits from its total assets. This study uses a quantitative approach with multiple linear regression analysis to examine the relationship between variables. The data used are documentary data with secondary data sources obtained from financial reports and company sustainability reports. A purposive sampling technique was applied to determine the research sample based on certain criteria, resulting in 95 observational data. The results show that free cash flow and sales growth have a significant influence on financial performance, indicating that the company's ability to generate free cash and increase sales directly contribute to performance improvements. Conversely, sustainability reports were not proven to have a significant effect on financial performance, so sustainability disclosure has not been a determining factor in increasing the ROA of energy sector companies during the study period.

This study aims to implement an information retrieval system for cryptocurrency data using an attribute-based approach integrated with the Vector Space Model (VSM). The primary objective is to develop a system capable of retrieving the most relevant digital asset information according to specific search attributes, including positive sentiment, price fluctuation, and prediction confidence level. The research adopts a descriptive qualitative method combined with an experimental approach to evaluate the retrieval performance of the cosine similarity algorithm on normalized numerical data. Data preprocessing and attribute weighting were conducted to ensure consistency and improve retrieval accuracy. The experiment demonstrates that the proposed system achieves a Precision@5 value of 1.0, which indicates that all top-five retrieved results are fully relevant to user queries. These findings validate the effectiveness of the attribute-based VSM in analyzing multidimensional cryptocurrency datasets. Overall, this research contributes to the advancement of information retrieval applications in the cryptocurrency domain, particularly for supporting data-driven decision-making and intelligent financial analysis.

This study explores how resource-constrained small and medium-sized enterprises (SMEs) in Indonesia’s advertising sector adapt digital transformation strategies to achieve sustainable development. Using the case of Roxy Maharewa, an SME in Makassar–Gowa, the research examines strategic trade-offs between investing in advanced digital platforms and operationally transformative physical assets. Guided by the Resource-Based View, Strategic Trade-off Theory, and Contingency Theory, this qualitative case study employs semi-structured interviews with key decision-makers. Findings reveal that prioritizing high-capacity production equipment eliminated structural cost disadvantages from outsourced printing, enabling twice to fourth times potential profit margin gains and strengthening market competitiveness. Rather than adopting a fully digital-first approach, the firm employed a hybrid strategy leveraging low-cost digital tools for internal efficiency while maintaining offline relationship management to align with client preferences and infrastructural realities. This asset-led hybrid model demonstrates how SMEs in emerging markets can integrate selective digital adoption with tangible investments to achieve both rapid returns and long-term sustainability. The paper contributes to the discourse on digital transformation by providing a context-sensitive perspective for policymakers and practitioners, emphasizing that in heterogeneous digital readiness environments, blended strategies can deliver superior economic and social.