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Law Number 11 of 2006 on the Governance of Aceh embodies the principle of lex specialis due to its territorial scope, while Law Number 4 of 2009 on Mineral and Coal Mining also contains a lex specialis character as it specifically regulates the mining sector. This situation raises a legal question regarding how the principles of lex specialis derogat legi generali, lex superior derogat legi inferiori, and lex posterior derogat legi priori should be interpreted and applied by the Government within the framework of Aceh as a Special Autonomous Region. This study aims to conduct an in-depth legal analysis using a progressive law approach to examine whether the principles of lex posterior or lex superior may override the principle of lex specialis as applied in Aceh’s special autonomy regime. The research employs normative legal methods with qualitative analysis. The findings indicate that the principle of lex specialis derogat legi generali as stipulated in the Law on the Governance of Aceh must take precedence due to its specific territorial applicability and special autonomous status. In resolving such normative conflicts, a clear legal framework is required, accompanied by the renewal of legal theories and principles to ensure legal certainty, prevent regulatory overlap, and promote harmony among statutory regulations.

International student enrollment is a critical driver of financial sustainability for Higher Education Institutions (HEIs). While advanced forecasting is standard in the corporate sector, its application in educational planning remains limited. This study addresses this gap by comparing the predictive performance of ARIMA, Facebook Prophet, and Long Short-Term Memory (LSTM) models. Using a publicly available annual dataset from a US-based institution (2000–2022), the analysis employed a strategic partition training on 2000–2017 and testing on 2018–2019 to validate models on stable, pre-pandemic data. Empirical results revealed that the statistical ARIMA (2,1,0) model demonstrated superior accuracy, achieving a Mean Absolute Percentage Error (MAPE) of 1.26%. Conversely, Prophet (11.81%) and LSTM (13.84%) struggled with the limited sample size, failing to generalize effectively compared to the linear approach. The findings suggest that for annual enrollment trends, parsimonious statistical models outperform complex deep learning architectures, providing administrators with a robust, accessible framework for data-driven strategic decision-making.

The construction and infrastructure sectors are shifting toward lighter, low-emission, and sustainable materials in response to the high carbon footprint and excessive weight of common materials such as concrete and steel. One promising alternative widely developed is natural fiber–based composites. However, studies comparing mechanical properties of variations of natural fibers within a single framework remain limited. This study aims to evaluate and compare composite mechanical properties reinforced by sisal fiber, bamboo fiber, and pineapple leaf fiber to determine the optimal fiber type for sustainable infrastructure applications. The research methodology involved fabrication of composite specimens using a unidirectional fiber configuration with a resin matrix, molded following ASTM D638 Type I dimensional and geometrical requirements. Tensile testing was conducted to evaluate mechanical responses, including ultimate tensile behavior, deformation characteristics, and elastic properties, which were presented in tabular and graphical forms. The results show that incorporation of all natural fiber types significantly enhanced composite mechanical properties, exhibiting an average tensile strength of approximately 26 MPa. Pineapple leaf fiber demonstrated balanced mechanical behavior combining strength and ductility, while sisal fiber showed superior tensile resistance and rigidity. Bamboo fiber provided moderate mechanical improvement. Overall, natural fiber–reinforced composites demonstrate strong potential as environmentally friendly alternative materials for infrastructure applications, with mechanical characteristics adjustable based on reinforcing fiber type.

This study aims to evaluate the implementation of Sidoarjo Regent Regulation Number 69 of 2017 concerning the Civil Servant Code of Ethics in the Family Planning Sector at the Sidoarjo Regency Women's Empowerment, Child Protection, and Family Planning Office. A qualitative approach with a case study was used to explore the implementation of the policy through interviews, observation, and documentation. The analysis was conducted using William Dunn's six policy evaluation indicators, namely effectiveness, efficiency, adequacy, equity, responsiveness, and accuracy. The results show that the policy has provided clear behavioral guidelines and is applied evenly in the work environment. However, the effectiveness and efficiency of implementation are not optimal due to disciplinary violations, weak supervision, and unstructured communication between superiors. The aspects of adequacy and accuracy are considered relevant to the needs of the organization, but have not been able to fully overcome obstacles such as high workloads and low internalization of ethical values ​​among employees. In general, this policy contributes to shaping the professionalism of civil servants, but still requires strengthening through continuous supervision and more systematic coaching. The originality of the study lies in the use of Dunn's evaluation model in the context of the implementation of the civil servant code of ethics at the regional level, as well as identifying gaps between normative policies and field practices. These findings confirm that the success of a code of ethics depends heavily on organizational communication, work culture, and consistency of oversight.

This study aims to analyze the effect of product innovation, brand image, after-sales service, experiential marketing, and product quality on purchase intention and its impact on purchasing decisions of Toyota Avanza consumers in Semarang City. The research was conducted using a quantitative approach with primary data collected through questionnaires distributed to 120 respondents selected by purposive sampling. Data analysis employed the Structural Equation Modeling (SEM) technique using AMOS 18 software. The results indicate that all five independent variables—product innovation, brand image, after- sales service, experiential marketing, and product quality—have a positive and significant effect on purchase intention. Furthermore, purchase intention significantly affects purchasing decisions. Among the tested variables, product quality and product innovation were found to have the greatest influence on purchase intention, followed by brand image, experiential marketing, and after-sales service. These findings suggest that Toyota’s success in maintaining consumer interest and purchasing decisions largely depends on its ability to provide superior product quality, continuous innovation, and memorable customer experiences. The study contributes to marketing management literature by highlighting the integrated role of innovation and experiential marketing in shaping consumer behavior in the automotive sector.