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The implementation of the Global Minimum Tax (GMT) through Pillar Two of the OECD/G20 marks a fundamental change in the international tax architecture, especially for developing countries such as Indonesia. One of the key instruments in Pillar Two is the Qualified Domestic Minimum Top-Up Tax (QDMTT), which provides an opportunity for source countries to retain the right to tax the profits of multinational companies with an effective tax rate below 15 percent. This study aims to analyze Indonesia's readiness to face the implementation of GMT through the QDMTT policy, focusing on regulatory aspects and tax administration capacity. The research method uses literature studies with a qualitative-descriptive approach through the analysis of policy documents, tax regulations, as well as academic literature and international reports. The results of the study show that Indonesia's readiness is still in the transition stage. In terms of regulation, Indonesia has shown an initial commitment through the issuance of PMK Number 136 of 2024, but the regulation still needs to be strengthened at a higher level of regulation for long-term legal certainty. From the administrative aspect, the main challenges include the complexity of calculating jurisdiction-based Effective Tax Rates, cross-border data management, as well as increasing the capacity of human resources and information technology infrastructure. This study concludes that the success of QDMTT implementation in Indonesia depends on strengthening regulations, increasing tax administration capacity, and reformulating sustainable investment policies.

Customer churn is a strategic challenge for digital streaming platforms because it directly Impacts revenue and business sustainability. This study aims to analyze the factors influencing customer Churn and develop a churn prediction model using the Random Forest algorithm. The study uses a Quantitative approach with an explanatory design and utilizes secondary data from the Netflix Customer Churn and Engagement Dataset available on Kaggle. The dataset consists of 1,000 customer data with 16 Variables covering demographic characteristics, service usage behavior, financial condition, and customer Satisfaction level. The data was processed through preprocessing, one-hot encoding, and a 70:30 split Between training and test data. Model performance was evaluated using accuracy, precision, recall, F1 Score, and ROC-AUC metrics. The results show that the Random Forest model produces an accuracy of 53.7%, precision of 56.3%, recall of 63.6%, F1-score of 59.7%, and ROC-AUC of 0.534, indicating Moderate predictive ability and only slightly better than random classification. Feature importanceAn.evealed that user engagement levels, such as viewing duration and frequency of interactions, Were the most dominant factors influencing churn, followed by economic factors and customer satisfaction. The results of this study are expected to provide a basis for streaming platforms to design more effective Customer retention strategies.

This study examines the integration of technology in the process of Human Resource (HR) transformation through the perspective of employee data analytics as a strategic approach to modern HR management. The primary focus of the study is to analyze the impact of the simultaneous integration of digital HR systems and organizational digital transformation on improving the efficiency of HR functions, with organizational agility positioned as a moderating variable that strengthens this relationship. In addition, the study explores the potential optimization of Artificial Intelligence (AI) technologies and predictive analytics methods, such as Bayesian Optimization, in predicting workforce dynamics, including employee attrition risk and competency development needs, while also bridging the analytical skills gap among HR practitioners. The research method employed is a systematic literature review of relevant scientific publications from 2021 to 2025, selecting sources that address digital HR transformation, HR analytics, and the application of AI in organizational contexts. The findings indicate that digital HR systems have a strong and significant effect on enhancing operational efficiency and the quality of HR decision-making, and this effect becomes more optimal when supported by a high level of organizational agility. Furthermore, AI and predictive analytics are proven to generate more accurate predictions and simplify technical complexity, making them easier for HR practitioners to adopt. This study concludes that the success of HR transformation requires a holistic approach that aligns the use of advanced technologies with organizational capabilities, human resource readiness, and ethical considerations to create sustainable organizational value.

The increasing intensity of business competition requires companies to maintain strong financial conditions to avoid financial distress that may disrupt business continuity. This study aims to assess the financial stability and predict the potential bankruptcy of PT Sido Muncul Tbk for the 2022–2024 period using the Altman Z-Score model. A descriptive quantitative approach was applied, utilizing secondary data obtained from annual reports published by the Indonesia Stock Exchange and the company’s official website. Five key ratios in the Altman model were used as indicators to evaluate the company’s financial position and resilience. The results show Z-Score values of 4.74 in 2022, decreasing slightly to 4.66 in 2023, and rising again to 4.79 in 2024. These scores are significantly above the safe threshold of 2.675, indicating that the company is in a healthy financial state with a very low risk of bankruptcy. Overall, PT Sido Muncul Tbk demonstrates stable financial performance, supported by a strong capital structure and consistent operational results. The Altman Z-Score model also proves to be an effective early-warning tool for identifying potential financial problems.

Rapid developments in the era of digital transformation, which refer to the emergence of business and technology innovations based on artificial intelligence, big data, and the Internet of Things (IoT), have great potential for strategic sustainability for businesses in the digital age. Efforts to transform digital business models as a global competitive advantage and provide outputs that can be oriented towards future predictions. Digital business models refer to strategic designs for creating platform networks that are implemented through relationships with consumers and cross-sector collaboration. Challenges and opportunities for development between transformation and innovation are necessary in order to create and capture competitive value and provide added value in the digital economy era. The use of bibliometric analysis in research provides direction in understanding the perspectives and issues that require further research, opens up space for exploring publication trends, and identifies the mapping of key concepts that form the basis of main ideas, thereby providing a more structured understanding and developing new research opportunities, especially in the field of digital business models. Bibliometric analysis aims to gain an in-depth understanding of research using the R studio application as a tool for processing data trends over time and VOSviewer as a knowledge map visualization tool. The research was conducted to provide an understanding of current and future developments in a dynamic environment.

Early marriage remains an issue with negative impacts on the health and quality of the younger generation, particularly through the risk of stunting in children. This article discusses the results of a counseling session titled "Marriage is Not Just Legal: Legal Education on Marriage Dispensation and Its Implications for Preventing Stunting Among Students" conducted at SMA Negeri 3 Parepare. The counseling aimed to raise students' awareness about the relationship between the marriage age limit, marriage dispensation, and the health impact on children, specifically the risk of stunting. The methods used include deconstruction, brainstorming, material delivery, reconstruction, and evaluation through pre-tests and post-tests. The results show a significant improvement in students' understanding from both medical and legal perspectives. The discussion focuses on analyzing the contradictions in legal norms regarding the marriage age limit and marriage dispensation, which often cause implementation issues. This counseling emphasizes the importance of integrating legal education and reproductive health as a preventive strategy to reduce early marriage and prevent stunting. Thus, this program contributes to shaping a healthy, legally aware younger generation capable of making mature and responsible marriage decisions.

The development of digital technology and the massive penetration of social media have transformed consumption patterns, particularly among younger generations. Excessive use of social media may lead to addictive behavior, which has the potential to trigger psychological anxiety such as fear of missing out, and ultimately encourage impulse buying behavior. Denpasar City, as a center of urbanization and consumption in Bali, serves as an appropriate location to examine this phenomenon, as its community tends to be consumptive and highly active in social media use. The purpose of this study is to explain the role of fear of missing out in mediating the effect of social media addiction on impulse buying. This study employs Uses and Gratifications (U&G) theory as the theoretical foundation, with a sample of 130 respondents selected using purposive sampling. Data collection was conducted through a questionnaire method. The study applied descriptive and inferential techniques, including Path Analysis, Classical Assumption Test, and Sobel Test. The findings reveal that fear of missing out mediates the effect of social media addiction on impulse buying. This study strengthens the understanding that social media not only influences social interaction but also shapes impulsive consumption behavior as a result of psychological pressure and exposure to digital content.

This study aims to analyze the bankruptcy prediction of PT Matahari Department Store Tbk using the Zmijewski method. The Zmijewski method, developed in 1984, is one of the most widely used approaches to predict corporate financial distress through the use of financial ratios. The study covers the period from 2019 to 2023 and applies a quantitative research design. The data used in this study are secondary data obtained from the company’s financial reports. Data collection techniques include documentation and literature study, while the data analysis technique applied is the Zmijewski model, which employs three main ratios: return on assets (X1), debt to assets ratio (X2), and current ratio (X3). The results show that in 2019, 2021, and 2022, the X values were -1.92, -0.29, and -0.25, respectively, indicating that PT Matahari Department Store was not predicted to face potential bankruptcy, as the values were below 0. However, in 2020 and 2023, the X values were 1.51 and 0.85, respectively, suggesting that the company had the potential to go bankrupt, as the results exceeded 0. These findings highlight the financial fluctuations experienced by PT Matahari Department Store during the study period, emphasizing the importance of continuous financial performance evaluation and the use of bankruptcy prediction models as an early warning tool for stakeholders and decision makers.

The rapid development of the smart city concept encourages the need for energy management that is more efficient, sustainable and adaptive to the needs of modern urban communities. In this context, renewable energy is the main solution to reduce dependence on fossil energy sources that are limited and pollute the environment. This research aims to optimize the utilization of renewable energy in smart cities by integrating Big Data technology and Decision Support Systems (DSS). The approach used in this research is a case study and system modeling method, which involves collecting energy data from various sources such as IoT sensors, weather stations, and energy distribution systems in real-time. The data is then analyzed using Big Data Analytics techniques to identify energy consumption patterns, potential renewable energy production, and peak load predictions. Furthermore, a decision support system was designed to assist policy makers and city managers in determining optimal energy distribution and usage strategies based on the available data and simulations. The results show that the integration of Big Data and DSS is able to increase the efficiency of renewable energy utilization up to 25% compared to conventional systems. In addition, the system is also able to dynamically respond to changing conditions and provide more accurate and adaptive decision recommendations. These findings indicate that the synergy between data technology and decision support systems plays a strategic role in creating sustainable and environmentally sound smart cities.

This study aims to examine the effect of changes in tax rates, understanding of tax regulations, and tax awareness on the compliance of UMKM taxpayers. This research employs a survey approach with data collection via questionnaires from respondents who are UMKM taxpayers within the jurisdiction of KPP Pratama Sukoharjo. This study uses a sample of 100 determined using the Slovin formula. The data is analyzed using multiple linear regression analysis with SPSS software. The t-test results indicate that all three independent variables have a positive and significant effect on taxpayer compliance, with the calculated t-values being greater than the t-table value (1.984) and the significance value below 0.05. This research proves that changes in tax rates, understanding of tax regulations, and tax awareness affect the compliance of UMKM taxpayers.

This study examines the factors that influence financial distress in companies affiliated with Israel, focusing on the roles of profitability, liquidity, leverage, sales growth, and firm size. The research is driven by the phenomenon of boycotts caused by geopolitical conflicts involving Israel, which have impacted the financial performance of several companies, particularly in Indonesia. The study uses a quantitative approach, analyzing a sample of companies listed on the Indonesia Stock Exchange (IDX) that are affiliated with Israel during the 2023-2024 period. The data consists of quarterly financial statements, which are analyzed using the Altman Z-Score bankruptcy prediction model. The findings show that profitability and liquidity have a significant effect on financial distress, while leverage and sales growth have a smaller impact. Firm size is also found to reduce the risk of financial distress. These results suggest that companies linked to Israel are more vulnerable to financial risks due to boycotts triggered by international political tensions.

This study examines the application of big data analytics on Bhinneka.com, a leading e-commerce platform in Indonesia, to tackle the increasing in complexity of online user behavior in a swiftly changing digital environment. The primary issue is too challenges in evaluating extensive, unstructured, and heterogeneous user data, which obstructs personalization, marketing efficacy, and operational decision-making. The study seeks to assess the efficacy of big data instruments, specifically Artificial Intelligence Recommendation (AIRec) and Customer Data Platform (CDP), in improving user behavior forecasting. Service customization, and data-informed strategies. This study utilizes a qualitative case study methodology, including literature review and platform observation, to synthesis the many forms of big data analytics (descriptive, diagnostic, predictive, and prescriptive) and their implementation at Bhinneka.com. Significant findings indicate that the integration of AIRec and CDP has augmented the platform’s capacity to predict consumer preferences, improve marketing accuracy, and optimize logistics. However, obstacles stay the same, such as disjointed data systems, data quality concerns, and internal opposition to embracing a data-driven culture. The study suggests that although big data analytics substantially enhances Bhinneka.com’s digital competitiveness, ongoing investment in data infrastructure and organizational competence is crucial to fully harness its potential and preserve a competitive advantage in Indonesia’s e-commerce market.

This study aims to develop effective educational learning media in preventing pornography addiction and providing a deep understanding of the negative impacts of pornographic content on mental development and adolescent behavior in class 9D students of SMP Negeri 2 Teras Boyolali. The phenomenon of pornography addiction among adolescents is a serious concern because of its impact on psychological, social, and academic development. The media that will be used in the implementation of group guidance services for students at SMP Negeri 2 Teras is by using power point and audio visual media. The method of implementing this counseling service is based on a cognitive approach. One of the techniques of the cognitive approach that can be used to prevent someone from becoming addicted to pornographic content is by providing psychoeducation to participants. The results of the study showed that this learning media can improve students' understanding of the dangers of pornography and provide strategies to avoid negative content. Thus, this learning media can be used as an effective educational tool to prevent pornography addiction in adolescent students.

The objective of this investigation is burnout of occupation on organizational contradiction with the mediation of occupation commitment (case study: Al Muthani Cement Coefficienty). The investigation manner is applied in Conditions of objective and descriptive and survey in Conditions of conducting manner. The statistical society includes all the superintendrs and occupationholders in Al Muthani cement coefficienty, whose number is approximately 500 people, and in instruction to characterize the sample size, Cochran's formula was utilizated, and 217 people were randomly scaled. The manner of collecting inestablishment was a questionnaire, and to scale occupation exhaustion, Maslach (Mezlaj-Mezlach-Maslach) questionnaire (1981), Robbins' organizational contradiction (1994) and occupation occupation commitment was utilizated aboard Shaufli et al.'s (2001) questionnaire. After compiling the initial frameact, coefficient analysis was done to obtain the accuracy of the structure. Cronbach's alpha coefficient was utilizated to scale the relicapability of the questionnaire. Then, all the proposed hypotheses were tested and analyzed using the structural equation figureing technique and using smart pls software, and the findings showed that occupation exhaustion has a remarkable on burnout organizational contradiction with the mediation of occupation commitment in al-Muthani cement coefficienty.

This qualitative literature review examines the impact of strengthened tax enforcement on corporate cash holdings, synthesizing findings from recent empirical studies. The review highlights that enhanced tax enforcement prompts firms to adopt precautionary cash management strategies, often resulting in higher cash reserves to mitigate tax-related risks. Key factors influencing this relationship include firm size, governance quality, industry characteristics, and jurisdictional tax policies. Cross-country comparisons reveal significant variations, with institutional frameworks playing a crucial role in shaping corporate responses. While stricter enforcement ensures compliance, it may inadvertently constrain investment and operational efficiency due to increased liquidity demands. This study underscores the complexity of corporate financial decision-making under tax enforcement pressures and identifies gaps for future research, particularly in emerging markets.  

The interest rate in the Iraqi economy represents an active and important element in the management of monetary policy in the Iraqi economy, as it is used by the monetary authority represented by the Central Bank of Iraq to influence the money supply, as well as the impact of this also by allocating the available resources for savings among foreign investments to achieve the central goal of the monetary authority of achieving stability in prices such as the interest rate and various prices and values of investments together and thus achieve balance at the economic and financial levels. This research analyzes the relationship between interest rate changes (IRC) and foreign direct investment (FDI) in the Iraqi economy during the period from (2004-2023). Multiple analytical tools were used, including descriptive statistics, correlation analysis, time series analysis, and prediction models using ARIMA and Prophet. The results showed an association between the two variables under consideration, with the ability of the ARIMA and Prophet models to provide accurate forecasts of future   FDI trends. A quantitative methodology that includes descriptive statistics, correlation analysis, time series models, and forecasting tools has been adopted to clarify the relationship between the two variables and draw conclusions that support economic decision-making.

Companies often face various obstacles related to managing raw material inventory to meet demand, one of which is Peuyeum Ketan Istimewa. Working in the food processing industry, of course, raw material inventory management needs to be planned optimally to avoid various risks that can harm the company. The Quantity Discount model is used to take advantage of cost savings provided by suppliers when purchases are made in large quantities, while other efforts that can help manage raw materials in a company are by knowing the safety stock and reorder point of raw materials and also forecasting demand to predict future demand. This study will use the Quantity Discount model which optimizes inventory levels by considering storage costs, ordering costs, and quantity discounts. The calculations carried out are also to find the value of the company's Safety Stock and Reorder Point. The results of this study indicate that the use of the Quantity Discount method can reduce total costs by Rp26,319,267/year, while forecasting using the seasonality method increases the accuracy of demand predictions, thus enabling more efficient inventory management. The implementation of this model is expected to provide a significant contribution to operational efficiency and cost reduction at Peuyeum Ketan Istimewa

This study explores the application of machine learning to predict students' GPA based on behavioral and time-related factors, including study hours, extracurricular activities, sleep, social interactions, and physical activity. Seven regression algorithms were employed to evaluate predictive accuracy using metrics such as MAE, RMSE, and R2 Among these, Regularized Linear Regression demonstrated the highest accuracy and interpretability, highlighting its suitability for this dataset. The findings emphasize the potential of machine learning in identifying key predictors of academic performance and offer practical applications for personalized academic advising and time management. This research provides a data-driven framework to support students and educators in optimizing learning outcomes.

Operations management is a series of activities related to planning, organizing, controlling and supervising all resources used in the process of producing goods or services. The main function of operations management is to create quality products or services, at efficient costs, at the right time, and in accordance with market demand. This research is quantitative research that works with numbers and the data is in the form of numbers which are then analyzed using statistics to test hypotheses or to answer specific research questions and to make predictions. This research approach is explanatory research where data collection is carried out simultaneously in one stage (one shot study} or in a cross-section through a questionnaire. One of the main impacts of operations management what is good is increasing the efficiency of the production process by designing and managing efficient production processes, companies can optimize the use of available resources, reduce waste, and increase output without increasing significant costs. Effective operations management has a significant impact on various aspects of company performance, including operational efficiency, cost control, product quality and service, and customer satisfaction. By implementing good operations management principles, companies can increase their competitiveness, reduce waste, and improve the customer experience.

Maritime piracy has long been a significant threat to global shipping, with profound economic and legal implications. This journal explores the economic effects and legal actions associated with maritime piracy. The economic impact of piracy is substantial, costing the international economy between $7 billion and $12 billion annually. The rise in piracy has led to higher ransom demands, with some payments reaching record amounts, and has significantly increased the cost of maritime insurance, particularly in high-risk zones. From a legal perspective, the journal examines the evolution of piracy laws, highlighting the United Nations Convention on the Law of the Sea (UNCLOS) and its definition of piracy. The legal framework under UNCLOS includes three main elements: acts committed for private ends, occurring on the high seas, and involving two ships. The journal also discusses the concept of universal jurisdiction, which allows any state to prosecute pirates, though prosecution must follow the domestic laws of the capturing state.The International Maritime Organization (IMO) has introduced several conventions and initiatives, such as the Djibouti Code of Conduct, to enhance maritime security. This journal will focus on the economical effect and the suitable legal actions to punish and prevent the acts of piracy.