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This research aims to analyze the implementation of da'wah management in community organizations in Kresek District, Tangerang Regency. The research focuses on the application of da'wah management functions including planning, organizing, actuating, and controlling in da'wah activities. The research method used is descriptive qualitative with data collection techniques through observation, in-depth interviews, and documentation. The research subjects were administrators of Islamic community organizations in Kresek District. The results showed that the implementation of da'wah management in community organizations in Kresek District still requires optimization, especially in the aspects of organizing and controlling. Da'wah planning has been carried out well through the preparation of short-term and long-term work programs, but there are still obstacles in organizing human resources and coordination between organizational elements. The implementation of da'wah programs runs smoothly despite challenges in adapting to technological developments. The supervision and evaluation system needs to be improved to ensure the effectiveness of da'wah programs. This research provides recommendations for strengthening organizational management capacity through da'wah management training and developing collaboration between community organizations.

This study aims to determine the Implementation of Da'wah Management in Increasing the Motivation of Members of the Al-Ikhlas Taklim Assembly to Follow the Study at the Al-Ikhlas Taklim Assembly, Pahlawan Village, Kemuning District, Palembang City. This research was conducted at the Al-Ikhlas Taklim Assembly, Pahlawan Village, Kemuning District, Palembang City. The type of research used is descriptive research, the method used in this study is by observation and interview. This study consists of two types of sources, namely primary data and secondary data. To obtain data properly and as expected, relevant methods are used. Data analysis is an effort to achieve and systematically organize records of interview results, observations, and other documentation. To improve the researcher's understanding of the case being studied and make it a finding for others. Interactive analysis techniques according to Miles and Huberman are analysis techniques used to identify, categorize, and interpret qualitative data. Based on the results of the research conducted on the application of da'wah management in increasing the motivation of members of the taklim assembly to attend religious studies at the Al-Ikhlas Taklim Assembly, Pahlawan Village, Kemuning District, Palembang City and after going through the discussions that have been presented in the previous chapters supported by existing data and theories, it can be concluded that: The application of da'wah management at the Al-Ikhlas Taklim Assembly, Palembang is running well with effective planning, organizing, and evaluation, thus increasing the motivation of members to attend religious studies. Supporting factors for motivation include the desire to deepen religious knowledge, a comfortable environment, and relevant materials. Meanwhile, the inhibiting factors are concurrent activities and bad weather. Members of the taklim assembly are motivated to increase their faith, friendship, and be a good example for their families.

This study explores the integration of Artificial Intelligence (AI) with thermal optimization in Waste-to-Energy (WtE) systems to enhance both energy recovery and emission control. Introduction: The growing need for sustainable urban waste management has highlighted the importance of optimizing WtE systems. AI technologies, including machine learning and deep learning, have shown potential in improving the efficiency of WtE processes, especially in reducing emissions and enhancing energy recovery. Literature Review: Previous research indicates that AI has been successfully applied to various WtE technologies such as pyrolysis, gasification, and incineration, yet the integration of AI specifically for thermal optimization remains underexplored. Most studies focus on predictive models for emission reduction rather than real time thermal optimization. Materials and Method: The study proposes the development of an AI-driven framework that integrates real time data collection from IoT sensors, predictive modeling, and real time control algorithms. The system optimizes key parameters such as combustion temperature and fuel flow to enhance energy recovery and minimize emissions. The method includes data collection from operational WtE plants, followed by model development using machine learning algorithms. Results and Discussion: Initial simulations and pilot testing showed significant improvements in energy efficiency and emission reduction. AI-driven systems outperformed conventional WtE systems by optimizing operational parameters in real time. The study identifies gaps in AI integration for thermal optimization and suggests future research directions, including the integration of AI with smart grids and carbon credit systems for more sustainable WtE operations.

The integration of solar powered IoT irrigation systems in precision agriculture offers a sustainable solution to address water scarcity and enhance crop productivity. By leveraging real time data from soil sensors, weather APIs, and machine learning algorithms, these systems optimize irrigation schedules and improve water use efficiency. This research explores the potential of integrating renewable energy sources, such as solar power, with edge computing in smart irrigation systems to promote sustainable agricultural practices. The study aims to evaluate the performance of the proposed system in terms of water savings, crop yield, energy efficiency, and adaptability to varying climate conditions. Literature Review: Previous studies highlight the importance of smart irrigation systems in reducing water waste and improving crop yield through real time monitoring and automated decision making. However, existing systems often lack the integration of renewable energy and edge computing, which are critical for ensuring sustainability and operational efficiency in rural agricultural settings. The combination of renewable energy with IoT devices offers a promising solution to reduce energy costs and carbon emissions, while edge computing enhances real time data processing, ensuring prompt and accurate irrigation adjustments. Materials and Method: The proposed system integrates solar powered IoT devices, soil moisture sensors, weather data APIs, and edge computing devices to manage irrigation. Machine learning algorithms and evapotranspiration models are used to predict irrigation needs and optimize scheduling based on real time data. The system's performance is evaluated through metrics such as water savings percentage, crop yield improvements, and energy consumption, with a comparative analysis against traditional irrigation methods. Results and Discussion: The results indicate that the system successfully reduces water usage by 30% to 40%, increases crop yield by 25%, and operates with energy autonomy, powered entirely by solar energy. The system's adaptability to varying climate conditions ensures optimal crop growth, even under environmental stresses. The integration of renewable energy and edge computing significantly enhances the sustainability and efficiency of irrigation systems.

Introduction: The integration of Digital Twin (DT) technology and the Internet of Things (IoT) into Building Energy Management Systems (BEMS) offers a transformative approach to optimizing energy consumption in buildings. This study explores the development of a Digital Twin based BEMS prototype, which leverages real time data collection, predictive analytics, and machine learning to enhance energy efficiency, reduce costs, and support sustainability goals in modern buildings. The research also addresses key gaps in current energy management systems, including real time adaptive control and integration with smart grid platforms. Literature Review: Previous research highlights the limitations of traditional BEMS, which often rely on static control strategies and lack real time adaptability. Recent advancements, including predictive maintenance and machine learning integration, have improved energy optimization. However, challenges such as data interoperability, scalability, and cybersecurity remain. This review consolidates current approaches and identifies opportunities for enhancing BEMS through the integration of DT technology, IoT, and machine learning. Materials and Method: The methodology employed involves the design of a Digital Twin based BEMS prototype, incorporating IoT sensors for real time data collection on variables such as HVAC load, occupancy, and environmental factors. The system uses time series forecasting and adaptive control strategies to optimize energy consumption. A case study building is used for validation, with performance metrics such as energy savings, CO₂ footprint reduction, and peak load reduction assessed to evaluate the system's effectiveness. Results and Discussion: The results demonstrate a significant reduction in energy consumption (up to 50%) compared to traditional BEMS, along with improved forecasting accuracy and sustainability performance. The prototype achieved a high R² score in predicting energy usage, validated through real world application in the case study building. The economic feasibility analysis showed substantial cost savings and a strong return on investment, making the system a financially viable solution for energy efficient building management.

Aquaculture is a vital sector in global food production, providing essential protein sources. However, the industry faces significant challenges, including high energy consumption and environmental impact. The integration of renewable energy, particularly solar power, with automation and IoT systems offers a promising solution to enhance energy efficiency, sustainability, and productivity in aquaculture operations. This study aims to evaluate the effectiveness of solar powered autonomous systems in reducing energy usage, improving operational efficiency, and promoting environmental sustainability in aquaculture. Literature Review: Recent research has explored various technologies, such as Digital Twins (DTs) and Precision Fish Farming (PFF), which integrate IoT sensors for real time monitoring and optimization of fish farming operations. The combination of Artificial Intelligence (AI) and the Internet of Things (IoT), known as AIoT, has further advanced the industry by enabling automated decision making and predictive analytics. Solar power integration with IoT systems has been shown to significantly reduce operational costs, minimize carbon emissions, and enhance the sustainability of aquaculture practices. These advancements have the potential to address the challenges of energy consumption and environmental degradation in the industry. Materials and Method: This research utilizes a hybrid solar powered IoT system for aquaculture, integrating solar panels, IoT sensors, and automated control systems. The system monitors key water quality parameters, such as pH, dissolved oxygen, turbidity, and temperature, to maintain optimal conditions for aquatic life. Data is collected through IoT sensors and analyzed through a cloud-based platform. A pilot study is conducted on a small scale aquaculture farm to evaluate the system's performance, including energy consumption, water quality management, and fish health. Energy savings, operational efficiency, and environmental impact are assessed. Results and Discussion: The integration of solar powered IoT systems significantly reduced energy consumption compared to traditional systems, with a notable decrease in grid electricity reliance. The system successfully maintained optimal water quality conditions, enhancing fish health and growth. Solar powered systems proved reliable, even in regions with variable sunlight, and demonstrated improvements in operational efficiency through automation. The environmental benefits were evident, with a reduction in carbon emissions and lower operational costs. The study highlights the feasibility of solar powered IoT systems as a sustainable solution for modern aquaculture operations.

Introduction: Achieving carbon neutrality in industrial systems is essential for mitigating climate change and promoting sustainability. The increasing demand for energy optimization and carbon emission reduction has driven the development of advanced technologies, particularly hybrid machine learning (ML) models. These models, combining ensemble learning and reinforcement learning (RL), offer significant promise in optimizing industrial processes, reducing energy consumption, and improving environmental performance. This study explores the application of hybrid ML models in achieving carbon neutral goals through dynamic process optimization and energy control in industrial settings. Literature Review: Hybrid ML models integrate different machine learning techniques to handle complex and dynamic environments effectively. Ensemble learning methods, such as boosting, bagging, and stacking, combine multiple algorithms to improve predictive performance and robustness. Reinforcement learning (RL), on the other hand, enables real time decision making and adaptation based on trial and error interactions with the environment. In energy optimization, these models are used to reduce energy intensity and carbon emissions, enhancing overall operational efficiency. Previous studies have demonstrated the effectiveness of ML models in energy management, but challenges such as data quality, model integration, and computational complexity remain. Materials and Method: The study applies hybrid ML models combining ensemble learning and RL to optimize energy consumption and minimize carbon emissions in industrial processes. Data from real time sensors and operational parameters are used to train the models. The ensemble learning component improves the accuracy of energy predictions, while RL ensures dynamic process adjustments in response to fluctuating energy demand. The models were tested in various industrial settings, including manufacturing processes, smart grids, and microgrid systems. Performance metrics such as energy efficiency, carbon emissions reduction, and operational costs were evaluated to assess the effectiveness of the models.  Results and Discussion: The hybrid ML models achieved significant reductions in energy intensity (15-20%) and carbon emissions (18-25%). The real time adaptability of the RL component allowed the models to adjust energy consumption patterns dynamically, improving energy efficiency and reducing waste. The models demonstrated their ability to adapt to varying operational conditions, ensuring optimal energy use. A cost-benefit analysis showed that the hybrid models provided substantial energy savings and reduced operational costs, with a return on investment (ROI) of 30-35% within the first year of deployment. However, challenges such as computational complexity and data quality issues were identified, highlighting the need for further refinement in model development.

Stunting, also known as growth failure in children, is a condition caused by a child's nutritional needs not being met for a long time, specifically during the first 1,000 days of life. The first 1,000 days of life are the first 270 days (nine months) in the mother's womb plus the first 730 days (two years) of a baby's life after birth. Education is a learning process aimed at developing the potential or quality of thought patterns and knowledge of an individual or group. One of the goals of education is to increase public knowledge. The target group for the first 1,000 days of life is pregnant women. The purpose of this study was to determine the effect of education on pregnant women's knowledge about the first 1,000 days of life at the Tombulilato Community Health Center, Bone Raya District, Gorontalo City. The type of research used is Quasi Experiment with a two group pretest and posttest design approach. The sample in this study were 32 pregnant women in the intervention group and 32 in the control group. Data collection was done through a questionnaire. Data were analyzed using a paired T test with a 95% confidence interval (CI) and α <0.05. The results of the study showed that Educational Intervention had a significant effect on the knowledge of pregnant women (p = 0.000) in the First 1000 Days of Life in the Tombulilato Health Center area, Bone Raya District. It is hoped that more practical information will be provided to the community, especially prospective mothers, in preparing a quality next generation, especially in the First 1000 Days of Life. As well as optimizing Posyandu services, and involving the participation of families such as husbands and parents when socialization and information provision are carried out.

Heatwaves are becoming more frequent and severe, posing significant health risks, especially for vulnerable populations in urban areas. In response to this, green-exercise interventions, which combine physical activity with exposure to green spaces, offer a promising strategy for mitigating heat-related health risks. This study evaluates the effectiveness of green-exercise interventions in reducing heat-related mortality risks in heat-vulnerable communities. The research follows a longitudinal design, collecting data on physical activity, vegetation indices (NDVI), and heat-related mortality rates in urban areas prone to heatwaves. The findings reveal that green-exercise interventions resulted in up to an 18% reduction in heat-related mortality risks, demonstrating the significant impact of physical activity in natural environments. Moreover, positive correlations were found between increased green space and reduced health risks during heatwaves, emphasizing the importance of urban greening in mitigating the effects of extreme heat. Participants who engaged in outdoor physical activity in green spaces showed greater resilience to heat stress compared to those who opted for indoor exercises, underscoring the added benefits of natural environments for physical and mental health. However, the effectiveness of these interventions varied depending on environmental and behavioral factors, including the quality of green spaces and individual motivation to engage in outdoor activities. This study highlights the need for further research into the long-term effects of green-exercise interventions and the combined impact of socio-economic and environmental factors on health outcomes. The findings suggest that urban greening and physical activity in green spaces should be integrated into public health strategies, particularly in heat-vulnerable areas.

This study analyzes the influence of Augmented Reality (AR) technology on consumer purchase intention with customer engagement as an intervening variable at Sephora Indonesia. This quantitative research involved 150 respondents who are users of Sephora Virtual Artist, analyzed using Structural Equation Modeling (SEM). The research instrument measures three main variables: Augmented Reality (interactivity, vividness, novelty), Customer Engagement (cognitive, emotional, behavioral), and Purchase Intention (intention, willingness, likelihood to purchase) using a five-point Likert scale. The research results show that AR has a positive and significant effect on customer engagement and purchase intention. Customer engagement also has a positive effect on purchase intention and is proven to mediate the relationship between AR and purchase intention. This study contributes to the AR literature in beauty e-commerce and recommends that Sephora optimize AR features, develop comprehensive customer engagement strategies, and continue investing in AR technology as a competitive advantage.

The purpose of this research is to learn how certain students in Medan City are influenced to buy Azarine sunscreen products by influencer marketing and brand image. More and more people, particularly students, are realizing the need of preventing sun damage to their skin, therefore sunscreen solutions have quickly become an integral part of skin care regimens. Researchers in this research used a quantitative approach by surveying people who had used Azarine products. Using SPSS software version 29, the data that was obtained was examined. The findings show that consumers are influenced by both favorable brand image and influencer marketing, which has a major impact on their purchase choices. There is a significant relationship between the dependent variable and each of the independent variables.

This research discusses the application of Islamic economic principles during the leadership of Al-Khulafa' Al-Rasyidin, namely Abu Bakar Ash-Shiddiq, Umar bin Khattab, Uthman bin Affan, and Ali bin Abi Talib. The focus of the study lies on the application of sharia values such as justice, honesty, zakat, and wealth distribution in economic policy and governance. The study found that the caliphs consistently implemented Islamic economic principles in state financial management, zakat collection and distribution, market supervision, and protection of property rights. Such implementation proved the effectiveness of the Islamic economic system in creating social justice and public welfare. The application of Islamic economic principles in financial management has an important role because it provides a strong ethical and moral basis, supports the creation of a sustainable economy, and emphasizes justice in the distribution of wealth in society. These principles serve as guidelines for individuals to manage their finances wisely so as to bring benefits not only to themselves, but also to their social environment. By applying these values, wealth inequality can be prevented and a stable and fair economic system can be built. Islamic economics also emphasizes the importance of transparency and honesty in every financial transaction. By avoiding unethical practices such as usury and excessive speculation, individuals can maintain integrity and create a financial system that is aligned with moral values. In addition, Islamic economic principles contain a high value of social responsibility, reflected in the teachings on Zakat, Infaq, and Sadaqah which encourage individuals to participate in social development and help those in need. Therefore, managing personal finances based on these principles not only ensures individual financial sustainability, but also has a positive impact on the well-being of society at large.

This study aims to analyze the concept of economic justice according to Ibn Taimiyah and examine its relevance to the challenges of the modern economic system. Through a qualitative approach based on literature studies, it was found that principles such as honesty in trade, justice in the distribution of wealth, state involvement in maintaining market balance, and the prohibition of exploitation and monopoly are important foundations in Ibn Taimiyah's thinking. These values ​​have proven to remain contextual in facing the problems of global economic inequality, the dominance of large corporations, and the crisis of social justice in the modern era. The integration of the principles of ethics and justice promoted by Ibn Taimiyah is the key to building a more humane, inclusive, and sustainable economic order

This study aims to analyze the economic thought of Ibn Khaldun and its relevance to the modern economic system. Employing a qualitative approach through library research and document analysis, the study examines key concepts in Al-Muqaddimah, such as the economic cycle theory, market mechanisms, the role of the state, and distributive justice advocated by Ibn Khaldun. The findings reveal that Ibn Khaldun’s economic thought holds significant relevance in addressing modern economic challenges, particularly social inequality, wealth redistribution, and market regulation. Empirical studies on the implementation of zakat systems in Malaysia and modern economic theories like Keynesianism and institutional economics support these findings. Therefore, integrating Ibn Khaldun’s classical economic values into contemporary economic policies can enhance the stability, justice, and sustainability of the global economic system. This research contributes to enriching Islamic economic literature and offers practical solutions for inclusive and equitable economic development.

This study aims to examine the contribution of zakat to socio-economic mobility during the leadership of the Prophet Muhammad SAW in Madinah, emphasizing its role not merely as a religious obligation but also as a strategic instrument for social transformation. Employing a qualitative method through literature review, this research explores historical accounts, Islamic economic literature, and academic sources related to zakat management. The findings reveal that zakat, when administered systematically and empowerment-oriented, effectively reduced social inequality, improved the living standards of marginalized communities, and promoted sustainable social mobility. The productive zakat model introduced by the Prophet SAW not only succeeded in establishing social justice in Madinan society but also laid the foundation for an economy based on communal solidarity. These results highlight the relevance of the Prophetic zakat model as an alternative solution for addressing contemporary socio-economic challenges, particularly in promoting equitable and sustainable development.

Ibn Khaldun is recognized as a prominent Muslim thinker who made significant contributions to the fields of economics, sociology, and politics. In his seminal work Muqaddimah, he introduced economic ideas that were far ahead of his time, including the concepts of national wealth, wage theory, market mechanisms, and the role of the state in fiscal policy. This study aims to examine the core economic thoughts of Ibn Khaldun and their relevance to current economic dynamics. Using a qualitative descriptive approach based on literature review, the study finds that many of Khaldun's ideas remain highly relevant, particularly regarding the importance of productivity, fiscal justice, and the integration of social and economic aspects in building a sustainable system. Most people know that Ibn Khaldun is one of the most famous Muslim thinkers, and he did a lot in the fields of politics, economics, and sociology.  In his monumental work, Muqaddimah discusses various long-established economic concepts, such as the market mechanism, wage theory, national wealth, and the role of the state in fiscal policy.  The purpose of this research is to study Ibn Khaldun's ideas on economics and evaluate how these impact contemporary economic developments.  The study found that many of Khaldun's ideas are still relevant today thanks to the use of descriptive qualitative methodology based on literature study. A sustainable and inclusive economic system relies on the idea of productivity as the source of economic growth, fiscal justice in the taxation system, and integration between social and economic aspects.  The results show that classical economic thought not only has historical value, but also offers solutions to many modern economic problems such as inequality, productivity stagnation, and institutional crisis.

This study is motivated by the phenomenon that many micro and small business actors are trapped in easy online loans but pay high interest. In addition, in the business they own, product pricing is oriented towards general conditions so that labor costs are rarely taken into account. As a result, they often make mistakes in measuring business productivity which ultimately leads to business decline. The problems that are often faced are more in financial management due to low financial literacy, lack of experience in finance and lifestyle. This study aims to test and analyze the influence of financial literacy, financial experience and lifestyle on the financial management behavior of MSE’s actors. Data were obtained from questionnaires to 100 respondents which were processed using multiple linear regression analysis. The results of the study stated that financial literacy, financial experience have a positive and significant effect on financial management behavior while lifestyle does not have a significant effect on financial management behavior.

This study aims to analyze the marketing strategies implemented by Enauto Café to enhance customer attraction and loyalty in Nabire. A descriptive qualitative method was employed through direct observation, interviews with the café manager and customers, and questionnaires distributed to 40 respondents. The findings indicate that social media-based marketing strategies, strong visual branding, and consistent customer service contribute significantly to customer loyalty. However, there are notable weaknesses in direct promotion and menu variety. Therefore, strengthening local promotion efforts and product innovation are recommended as strategic improvements.

This article examines the crucial role of eco-leadership in achieving a sustainable and environmentally friendly economy, particularly within the dynamic context of the digital age and industry 4.0. In this era, the pressure on companies to integrate environmentally friendly initiatives into their corporate social responsibility (CSR) programs is increasing, supported by government policies and regulations in Indonesia. This article highlights that the effectiveness of environmental protection and sustainable corporate performance can be measured through various practices such as waste management, pollution control systems, recycling, and environmental impact mitigation. In conclusion, environmentally oriented leadership plays a central role in promoting more responsible business practices and behaviors, which are essential for reaching sustainability targets and realizing an economy that is not only financially profitable but also responsible for environmental preservation.1

The use of log wood as raw material for furniture and construction industry requires an optimal approach in the dimensions of management and production efficiency to increase economic value. This study aims to analyze the relationship between log wood dimensions (length, width, diameter) with the value of production results based on the percentage of efficiency (60%, 50%, 40%, and 30%). The data used are the results of observations of log wood dimensions from several types (unnamed and with codes: Piton, DL, UP, UD) along with the basic price value of the log. Calculations are carried out to determine the economic value of log wood based on the production efficiency scenario. The results of the study indicate that increasing production efficiency significantly affects the economic value obtained, with the UD log type providing the highest value at all levels of efficiency. This study provides recommendations for wood production optimization strategies based on comparisons of dimensions and efficiency as a basis for decision making in the wood resource-based industry.