Retail investment in emerging markets has experienced rapid growth, driven by technological advancements and increasing public awareness of financial management. However, behavioral biases often influence retail investment decision-making, such as overconfidence and herding, which can undermine portfolio performance. This study aims to identify the key behavioral biases affecting retail investors' decision-making in emerging markets and analyze their impact on investment performance. A mixed-methods approach was employed, combining a quantitative survey of 200 retail investors with in-depth interviews to gain qualitative insights. The results reveal that overconfidence bias dominates, with a prevalence of 70%, followed by herding bias at 50%, anchoring at 40%, and loss aversion at 60%. Overconfidence bias showed a significant positive correlation with investment returns (r = 0.65, p < 0.01), while herding (r = -0.48, p < 0.03), anchoring (r = -0.35, p < 0.05), and loss aversion (r = -0.60, p < 0.02) had negative impacts on portfolio performance. This research contributes to the behavioral finance literature by highlighting the unique conditions of emerging markets, such as low financial literacy and limited access to information, which exacerbate the effects of behavioral biases. As a practical implication, the development of behavioral finance-based educational programs is recommended to help investors understand and manage these biases. Furthermore, future research is encouraged to explore the use of alternative data, such as social media, to monitor behavioral trends in real-time

This study aims to analyze the implementation, stages, and obstacles in public policies issued by the Kutai Kartanegara Regency government to protect the social environment of the community, through Regent Regulation Number 22 of 2022 concerning Smoke-Free Areas (KTR). This policy aims to reduce exposure to cigarette smoke in public places, in order to improve public health. The method used in this study is descriptive qualitative, with data collection through observation, interviews, and documentation. The focus of this study is on policy analysis, socialization, the establishment of special smoking areas, controlling tobacco advertising, as well as law enforcement and the role of the community. The results of the study indicate that there are challenges in the implementation of the KTR Perbup, including low public awareness, limited resources, and lack of coordination between related parties. This study suggests the need for expanded socialization, strengthening the role of the community, consistent law enforcement, and closer collaboration between government, industry, academics, the community, and the media. With pentahelix synergy, it is hoped that the KTR policy can be more effective in creating a healthier and smoke-free environment in Kutai Kartanegara.

The purpose of this study was to determine the level of financial performance of the North Sumatra government. The example used is the Budget Realization Report of North Sumatra Province 2019-2022. The analysis method uses five types of indicators, namely: regional financial independence ratio, effectiveness ratio, growth ratio, harmony ratio, and fiscal decentralization ratio. The data collection method is done by taking data from the BPS (Central Bureau of Statistics) website of North Sumatra. The results showed that the ratio of regional financial independence in the financial statements of North Sumatra province has a delegative pattern, while the effectiveness ratio is in the ineffective area. Other results show that the growth ratio is fluctuating, the harmony ratio is in an unbalanced condition, and the fiscal decentralization ratio is in the high category.

Employee engagement is a crucial factor in organizational success, particularly in the dynamic and competitive technology sector. Traditional approaches, such as financial incentives, often fail to create sustainable intrinsic motivation, necessitating innovative solutions such as gamification. This study aims to analyze the role of gamification in enhancing employee engagement through the implementation of digital platforms in medium-sized technology companies. The research employs a case study methodology with a qualitative approach, involving in-depth interviews with 15 informants, including employees and managers, as well as user activity data analysis from the gamification platform. The findings indicate that gamification significantly improved employee engagement, with the proportion of employees exhibiting "High" engagement levels increasing from 25% to 60%, and "Very High" levels rising from 10% to 35%. Conversely, the "Very Low" category decreased from 15% to 5%. These results suggest that gamification elements such as rewards, challenges, and team collaboration fulfill employees’ psychological needs and create a more dynamic and productive work environment. This study contributes to the literature by providing insights into the effectiveness of gamification in the technology sector and offering practical recommendations for organizations to design relevant and sustainable gamification platforms. In conclusion, gamification is a promising strategy for enhancing employee engagement, provided that its elements are tailored to individual preferences and the technological readiness of the organization

This study examines the impact of relocating Indonesia's capital city from Jakarta to East Kalimantan on the local economy, workforce, and associated challenges. Using qualitative methods and descriptive analysis, the findings reveal positive effects such as increased investments and economic growth driven by new infrastructure and heightened economic activity in property, hospitality, and transportation sectors. However, challenges like rising property prices, gentrification, and social changes demand sustainable spatial planning and effective social impact management. Proposed mitigation strategies include thorough planning, social infrastructure development, local economic empowerment, and regular impact monitoring. The capital relocation has the potential to boost local economic growth if managed effectively.

Public-Private Partnerships (PPPs) have emerged as a strategic approach to addressing the challenges of public sector development, particularly in the context of budget constraints and the demand for sustainability. Although numerous studies have explored the potential of PPPs, there remains a gap in understanding how this approach can simultaneously enhance operational efficiency and project sustainability. This research aims to evaluate the contribution of PPPs to operational efficiency and sustainability through case studies from five key sectors: transportation, energy, health, education, and clean water. The research employs a qualitative approach, utilizing thematic analysis based on primary data from semi-structured interviews with stakeholders and secondary data from project documents and official reports. The findings reveal that PPPs can improve cost efficiency by up to 20% in transportation projects through the application of technology and innovative management. In the energy sector, a 40% reduction in carbon emissions was achieved due to the integration of environmentally friendly technologies. Moreover, in the health sector, project completion times were reduced by 15%, while patient satisfaction rates increased by 30%. The study also highlights that the success of PPPs depends on balanced risk-sharing, contract transparency, and effective collaboration between the public and private sectors. This research contributes to strengthening empirical evidence on the relevance of PPPs as a strategic solution for enhancing operational efficiency and sustainability in the public sector. These findings are expected to serve as a guide for policymakers in designing more adaptive PPP contracts that support sustainable development. Thus, this study reinforces the role of PPPs as a key instrument in addressing modern development challenges

Corporate governance is crucial in enhancing competitiveness and attracting investment, particularly in emerging markets that face challenges such as complex regulatory environments and ownership structures. Shareholder activism has emerged as an essential mechanism for improving corporate accountability and performance. This study aims to analyze the impact of shareholder activism on corporate performance in emerging markets, considering corporate governance as a moderating variable. A quantitative approach is employed, utilizing panel data analysis from publicly listed companies in emerging markets over a period of 5 to 10 years. Corporate performance is measured using financial indicators such as Return on Assets (ROA), Return on Equity (ROE), and Tobin's Q, while shareholder activism is assessed based on the number of resolutions proposed in shareholder meetings. A panel regression model is applied to identify the relationships among the variables. The results indicate that shareholder activism has a significant positive impact on corporate performance, with a coefficient of 0.35 for ROA (p-value 0.001). Corporate governance acts as a moderator that strengthens this relationship, with an interaction coefficient of 0.10 (p-value 0.05). Overall, the regression model explains 78% of the variation in corporate performance (R-squared = 0.78). This study provides empirical evidence demonstrating that shareholder activism, supported by robust governance, can enhance corporate transparency and accountability in emerging markets. These findings are relevant for stakeholders aiming to promote better governance practices to sustainably enhance corporate competitiveness

The rapid digital transformation has significantly impacted public governance, particularly through the adoption of e-government systems. While numerous studies have highlighted the benefits of e-government at the national level, attention to its implementation at the local authority level remains limited. Local authorities face unique challenges, such as limited technological infrastructure, inadequate staff competencies, and varying levels of public trust. This study aims to identify the key factors influencing e-government adoption at the local level and evaluate its impact using a quantitative approach. Data were collected from 150 local authorities in urban and rural areas through structured surveys and analyzed using multiple linear regression. The study’s results indicate that digital infrastructure has the strongest influence on e-government adoption, with a regression coefficient of ? = 0.45 (p = 0.001). Staff competency (? = 0.35, p = 0.015) and public perception (? = 0.25, p = 0.045) also significantly contribute to adoption levels. Together, these three factors explain 65% of the variance in e-government adoption (R² = 0.65). These results align with the Technology Acceptance Model (TAM), which identifies perceived ease of use and perceived usefulness as key determinants of technology acceptance. This study contributes to the existing literature by focusing on the context of local authorities and providing empirical evidence on how infrastructure, staff competencies, and public perceptions influence e-government adoption. The results underscore the importance of improving technological infrastructure, investing in staff training, and promoting digital literacy among the public to accelerate digital transformation at the local level.

This research aims to design and build a system for stirring and monitoring temperature and humidity for ESP32-based compost fertilizer. This system uses an ESP32 microcontroller to control compost mixing and monitor temperature and humidity conditions in real- time. Data obtained from sensors will be sent to the server for monitoring via Blynk. The implementation of this system is expected to increase the efficiency of the composting process and produce high quality compost through optimal monitoring and mixing.

Capital markets play a strategic role in the global economy; however, high volatility often poses challenges for investors in making optimal investment decisions. Stock price prediction has become one of the key elements in effective portfolio management, but the complexity of market data demands more advanced analytical approaches. This study aims to explore the potential of Long Short-Term Memory (LSTM) models in predicting stock price movements and integrating these predictions into portfolio optimization strategies based on modern portfolio theory. The research methodology utilizes historical stock data from the S&P 500 and IDX Composite indices with daily frequency. The LSTM model is compared with Recurrent Neural Networks (RNN), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and accuracy. The LSTM model demonstrated superior performance with an MSE of 0.0018, RMSE of 0.042, and prediction accuracy of 91.5%, significantly outperforming RNN (87.3%), SVM (80.2%), and ANN (82.7%). The prediction results were then integrated into portfolio optimization using the Mean-Variance Optimization approach, resulting in an increase in expected returns from 8.5% to 12.3%, a reduction in risk from 6.2% to 5.8%, and an improvement in the Sharpe ratio from 1.37 to 2.12. This study demonstrates that the LSTM model not only excels in stock price prediction accuracy but also contributes significantly to enhancing portfolio management efficiency. This Deep Learning-based approach offers a more adaptive, data-driven investment strategy, supporting more informed decision-making in the dynamic capital market