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This study aims to analyze the prevalence of illegal parking attendants and their impact on public service delivery and the Regional Gross Domestic Product (GDP) in Sorong City. The method used is a mixed-method approach. The study employed quantitative methods, including a questionnaire survey and descriptive statistical analysis, as well as correlation analyses, and qualitative methods, including in-depth interviews and source triangulation. The results indicate that local government oversight, compliance with parking regulations, and the quality of public services are positively and significantly related to increases in local revenue (PAD). Qualitative findings revealed that illegal parking practices lead to revenue leakage, degraded service quality, and public order issues. Consequently, local governments need to strengthen oversight, digitize parking payments, and provide officers with training to increase transparency and optimize local revenue (PAD). The study's limitations lie in its small sample size and its focus on a single location. Future research is recommended to expand the sample, compare several regions, and employ a more comprehensive policy analysis. These results contribute to the development of informal-economy and social-order theory in the context of urban parking management. In practice, the research findings can serve as the basis for formulating more effective and sustainable parking management policies. Implementation of the recommendations is expected to improve the quality of public services and public trust in local governments effectively and sustainably in the future.

The policy of increasing fuel prices is a strategic public policy that has broad implications for the lives of people in Indonesia. Fuel plays an important role in supporting economic activity, distribution of goods, and social mobility. This article aims to analyze the impact of the fuel price increase policy on public interests by emphasizing the current context in Indonesia and the empirical realities that arise in society. This study uses a qualitative approach through literature review and policy analysis. The results of the study show that the increase in fuel prices has a direct impact on the increase in the cost of living, a decline in people's purchasing power, and the emergence of social pressure, especially among low-income groups. Although the government views this policy as a rational step to maintain fiscal stability and the sustainability of energy subsidies, compensation and social protection policies remain key factors in ensuring that the public interest and the goal of community welfare are maintained.

This article examines the role of context and underlying assumptions in the formulation of econometric models for economic policy analysis. While econometric models are widely employed to support policy decisions, their construction is often treated as a purely technical process, overlooking the contextual factors that shape variable selection, model specification, and interpretation of results. This study explores how institutional settings, policy objectives, and behavioral considerations influence the assumptions embedded in econometric modeling. By drawing on policy documents, academic literature, and illustrative cases from applied economic research, the article highlights how implicit assumptions may affect the validity and relevance of econometric outcomes. The analysis emphasizes that econometric models are not value-neutral tools but are shaped by theoretical choices and contextual judgments made during their formulation. Understanding these dimensions is crucial to avoid misinterpretation of empirical findings and to enhance the usefulness of econometric analysis in policymaking. The study contributes to the methodological discussion in applied econometrics by encouraging greater transparency and reflexivity in model construction, particularly in the context of economic policy evaluation.      

The Local Currency Settlement (LCS) initiative is a strategic instrument to promote ASEAN regional financial integration while reducing dependence on the US dollar in trade and investment transactions. Sub-regional cooperation between Indonesia, Malaysia, and Thailand has become an important policy arena to test the effectiveness of LCS as part of the implementation of the ASEAN Economic Community (AEC) Blueprint 2025. This article aims to analyze the role, opportunities, and challenges of LCS implementation within the framework of ASEAN financial integration, focusing on the dynamics of cooperation between the three countries. The research uses a qualitative approach through literature review and policy analysis. Data were obtained from official central bank documents, regional cooperation agreements, international agency reports, and academic literature related to financial integration and regional monetary cooperation. The analysis was conducted descriptively and analytically to identify implementation patterns, structural barriers, and policy implications. The results of the study indicate that LCS contributes to increasing the efficiency of cross-border transactions, reducing exchange rate risk, and strengthening sub-regional ASEAN financial cooperation. However, its implementation remains limited due to differences in financial infrastructure readiness, variations in domestic regulations, and low adoption by business actors. This article emphasizes the importance of policy coordination, regulatory harmonization, and private sector involvement to optimize the role of the LCS in supporting ASEAN financial integration.

To respond to the challenges and needs of contemporary society, the public sector must rapidly adapt to digital transformation. The objective of this research is to examine relevant and adaptive human resource development strategies for the digital ecosystem and to evaluate how they impact the quality of public services in the era of technological disruption. This research uses a descriptive qualitative approach with a literature review and policy analysis. It analyzes best practices from government institutions, both national and international, in developing human resources oriented towards the digital era. Key findings indicate that optimizing human resources requires not only improving technological capabilities or digital expertise; it also requires reconstructing leadership paradigms, flexible organizational cultures, and implementing meritocratic systems and data-driven performance management. It is evident that technologies such as big data analytics, artificial intelligence (AI), and the Internet of Things (IoT) can help improve public services, but the success of these technologies depends heavily on the capabilities and readiness of the employees who manage these systems. An integrated digital talent ecosystem must be built, encompassing continuous training (learning for life), collaboration between government, academia, and business (the triple helix model), and a regulatory framework responsive to technological developments. Furthermore, it is emphasized that developing digital integrity and ethics is crucial as a pillar of good governance in the digital era. Optimizing human resource development strategies systematically and sustainably will enable Indonesia to improve the efficiency of public services and strengthen the competitiveness of its bureaucracy globally. By 2045, adaptable, innovative, and highly integrated human resources will be the primary drivers of a digital government transformation that is inclusive, responsive, and future-oriented.

The Perintis Lake area in Bone Bolango Regency, Gorontalo Province, faces a growing conflict between tourism investment and environmental conservation. This study aims to analyze the impact of tourism infrastructure development on the lake’s ecosystem, highlight the importance of conservation, and provide strategic recommendations for sustainable tourism development. Using a qualitative-descriptive approach, the research is based on field observations, literature review, and regional policy analysis. Findings show that unregulated investment activities, particularly constructions along the lake's buffer zone, have led to environmental degradation, such as water pollution, habitat loss, and ecological imbalance. Conservation efforts remain fragmented and lack integration into broader development planning. The study recommends the implementation of sustainable spatial planning policies, stricter environmental regulations, and active community involvement. Collaborative governance between local authorities, private investors, and environmental stakeholders is essential to ensure that economic development aligns with ecological preservation. Striking a balance between investment and conservation is key to maintaining Perintis Lake as a resilient ecotourism destination for future generations.

Background: Public opinion analysis has become increasingly important in the digital era, where social media platforms generate large-scale textual data reflecting public perceptions toward environmental policies. Advances in Natural language processing (NLP) and machine learning enable systematic sentiment classification to support data-driven decision-making. Objective: This study aims to evaluate the effectiveness of several sentiment classification models in analyzing Indonesian-language social media data related to environmental policies. Method: The research employed a text mining pipeline including data crawling, preprocessing (case folding, tokenization, stopword removal, and stemming), and vectorization using TF-IDF. Three classification models Logistic Regression, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) were trained and evaluated using accuracy and F1-score metrics. Results: Experimental findings indicate that LSTM achieved the highest performance with 91.7% accuracy and 91.2% F1-score, outperforming SVM (88.5%) and Logistic Regression (84.2%). Sentiment distribution analysis shows that public opinion is dominated by positive sentiment (47.5%), followed by neutral (32.0%) and negative (20.5%). Overall: The results demonstrate that deep learning-based models provide more robust contextual understanding and more reliable sentiment mapping for environmental policy analysis.

In 2022, Indonesia will host the G20 Summit with one of the goals agreed upon by G20 members is to reduce carbon emissions produced by each G20 country. Therefore, a model design was made to consider the most optimal policy to overcome this. The research uses a dynamic system model, with the process of identifying and defining problems, system conceptualization, model formulation, model simulation, verification and validation of policy analysis models, and policy implementation. The most optimum result is the imposition of a tax per carbon emission on producing sources with an effectiveness of 20%.