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Inventory management of raw materials is a crucial aspect in the manufacturing industry, particularly in the pharmaceutical sector, as it directly affects the continuity of the production process. This study aims to analyze the raw material inventory accounting system in inventory control at PT Bernofarm Pharmaceutical Company. The methods used include direct observation of operational processes and interviews with management to obtain relevant and accurate data. The results of the study show that PT Bernofarm has implemented an integrated accounting information system within an ERP framework, covering procedures for raw material requisition, issuance, return, and recording of production costs. Each procedure is systematically arranged with clear task separation and is fully computerized. This facilitates internal control and monitoring of raw material flow, while minimizing recording errors. With this effective system, the company is able to avoid both overstocking and stock shortages that could disrupt production. This study is expected to serve as a reference for improving the efficiency and accuracy of raw material inventory management in other pharmaceutical companies.

Stockpiles are accumulations of materials such as coal or ore stored at specific locations. Accurate stockpile volume measurement is crucial in the mining and logistics industries for inventory management and cost efficiency. Conventional methods like Real-Time Kinematic (RTK) GPS rely on surface coordinate measurements but require numerous points, especially for irregular-shaped objects. Newer technologies like Terrestrial Laser Scanners (TLS) offer high-precision alternatives by capturing thousands of points per second, expediting and enhancing the resolution of volume measurements. This study compares TLS and RTK GPS methods in measuring the volume of andesite rock stockpiles at PT Bara Tabang. This research uses a quantitative approach, involving six Ground Control Points (GCPs) measured using the Sokkia GRX2 RTK GPS on October 24, 2024. TLS generated over 8.6 million point cloud data filtered down to 35,197 points, while RTK GPS yielded 2,276 coordinates. Accuracy testing showed very small RMSE values (RMSEr: 0.008 m; RMSEz: 0.007 m), and both LE90 and CE90 demonstrated 90% confidence within a 0.012 m range. Volume calculation using Surpac software with the cut and fill method showed TLS produced a volume of 18,766 bcm (51,982 tons/m³), while RTK GPS resulted in 18,694 bcm (51,782 tons/m³), with a difference of 72 bcm or 0.211%.These results indicate that both methods offer acceptable accuracy; however, TLS provides greater data density, efficiency, and precision, particularly for complex or large-scale stockpile objects. Therefore, TLS is recommended for high-accuracy volume measurement in mining operations that require efficiency and detailed analysis.

This study aims to analyze financial statements as a tool to assess the financial performance of PT Unilever Indonesia Tbk for the period 2020–2023. Using a descriptive quantitative approach, this research calculates key financial ratios, including liquidity ratios (current ratio), solvency ratios (debt to equity ratio), activity ratios (total asset turnover), and profitability ratios (net profit margin). The results show that the current ratio experienced a declining trend from 66.09% in 2020 to 55.16% in 2023, reflecting a weakening ability of the company to meet its short-term liabilities. The debt to equity ratio increased from 315.90% in 2020 to 392.85% in 2023, indicating a high dependence on debt financing. Meanwhile, the total asset turnover improved from 315.90% in 2020 to 392.85% in 2023, suggesting better efficiency in utilizing assets to generate sales. However, the net profit margin declined from 16.42% in 2020 to 12.26% in 2023, signaling a decrease in the company's effectiveness in converting sales into net profit. Based on these findings, PT Unilever Indonesia Tbk is advised to enhance the management of current assets, strengthen its capital structure by reducing reliance on debt, and thoroughly evaluate cost control and marketing strategies to improve profitability and ensure business sustainability in the future.   

Economic Order Quantity (EOQ) is one of the methods in inventory management used to determine the optimal order quantity to minimize total inventory costs, which include ordering costs and storage costs. This study aims to analyze the application of the EOQ model in managing inventory in a company. By using data on raw material usage, ordering costs, and storage costs, the results show that the application of EOQ can optimize the amount of purchases and order frequency more efficiently. The results of the analysis show that the application of the EOQ model contributes to reducing operational costs and increasing the effectiveness of stock management. Therefore, EOQ can be used as an appropriate inventory management strategy to support the efficiency and productivity of the company. Economic Order Quantity (EOQ) in controlling coffee inventory at the Suteki Medan Coffee Shop to minimize total inventory costs. The EOQ method is used to determine the optimal order quantity with the most efficient cost, as well as considering safety stock, reorder point, and total inventory cost.  

Micro, Small, and Medium Enterprises (MSMEs) play a vital role in supporting regional economic development. However, inefficient inventory management remains a significant challenge in operational effectiveness. This study aims to analyze raw material inventory control at Kebab & Burger Foursist MSME in Tarakan City using the Economic Order Quantity (EOQ) and Reorder Point (ROP) methods. A descriptive quantitative approach was employed, utilizing annual sales data, ordering costs, and storage costs of main raw materials. The results indicate that the implementation of EOQ and ROP effectively determines the optimal purchase quantity and reorder timing, thereby minimizing total inventory costs and reducing the risk of stockouts or overstocking. The use of POM-QM for Windows software enhances the accuracy of the analysis. The implications of this study offer practical solutions for MSME actors in managing raw material procurement more efficiently and systematically.

This study analyzes the security policies implemented by President Nayib Bukele in El Salvador and their implications for crime rates and democratic institutions. Using a decriptive qualitative approach, the study finds that although crime has significantly declined, it has come at the cost of weakned democratic instutions, rising electoral authoritarianism, and the use of fear-based politics to gain public legitimacy. Mass detentions, reduced civil liberties, and the centralization of executive power show that stability is being built not through repressive control. Furthermore, vulnerable groups such as the poor and women are disproportionately affected between public security and human rights protection to avoid deepening structural inequalities and creating new forms of social vulnerability.

This study examines the role of management accounting in supporting strategic decision-making in manufacturing companies in Indonesia. With a literature study approach and Systematic Literature Review (SLR), this study analyzes various secondary sources from 2020 to 2025. The results of the study indicate that management accounting functions as the main tool in planning, cost control, performance evaluation, and strategic decision-making. The information generated such as cost data, budgets, and performance reports greatly assists management in choosing effective and responsive strategic alternatives to market dynamics. Case studies of several large manufacturing companies show that the implementation of a good management accounting system can improve operational efficiency, optimize resource allocation, and reduce the risk of failure in decision-making. Management accounting also plays a role in supporting investment and product development to maintain competitiveness in the global market. In conclusion, management accounting is a crucial element that strengthens strategic decision-making, so that manufacturing companies can grow and be sustainable in a competitive business environment.

The advancement of Internet of Things (IoT) technology has significantly transformed traditional homes into intelligent living environments. This study presents the implementation of a smart home automation system utilizing IoT components to control and monitor household devices remotely. The system integrates NodeMCU ESP8266 microcontrollers, sensors (temperature, motion, light), and actuators (relays for lights, fans, and appliances) which are connected through a Wi-Fi network. A mobile application is developed to enable real-time control and monitoring, enhancing user convenience, energy efficiency, and home security. The system also includes automated scenarios such as turning off lights when no motion is detected or adjusting ventilation based on temperature. Testing results show that the system responds within an average delay of less than 1.5 seconds and maintains stable performance across various network conditions. The findings confirm that IoT-based home automation offers a scalable, cost-effective solution to improve the quality of life and resource management. This study contributes to the development of sustainable and intelligent home systems for modern living.

This study examines the effect of liquidity, leverage, cash flow, and managerial agency cost on financial distress among manufacturing companies listed on the Indonesia Stock Exchange. Using multiple linear regression analysis, the results show that liquidity and cash flow have a significant negative effect on financial distress, indicating that firms with higher current ratios and stronger operating cash flows are less likely to experience financial difficulties. In contrast, leverage and managerial agency costs have a significant positive effect, suggesting that excessive debt and inefficient managerial spending increase the likelihood of financial distress. The coefficient of determination (R²) of 0.983 indicates that these four variables explain 98.3% of the variation in financial distress. The findings emphasize the importance of maintaining financial efficiency and controlling agency costs to enhance corporate financial stability.

Background: The global energy transition requires low-carbon solutions that can be integrated into existing thermal systems without drastic infrastructure changes. Hydrogen blending in conventional combustion systems has emerged as a promising pathway to reduce carbon emissions while maintaining operational flexibility. Objective: This study aims to experimentally evaluate the effect of hydrogen blending ratios (0–100% by volume) on thermal efficiency, CO₂ emissions, and NOx emissions, and to determine the optimal blending range based on technical and economic feasibility. Methods: An experimental thermal system prototype was developed and tested under controlled conditions with three repetitions per operating point. Performance parameters included combustion temperature, fuel consumption rate, and thermal efficiency, while emissions of CO₂ and NOx were measured using a calibrated gas analyzer. Data were analyzed using descriptive statistics, one-way ANOVA at a 0.05 significance level, confidence interval estimation, and linear regression to examine the relationship between hydrogen fraction and emission reduction. Results: The findings indicate that increasing hydrogen fraction significantly improves thermal efficiency, reaching 87.5% at 100% hydrogen, while CO₂ emissions decrease linearly to zero. However, NOx emissions increase with higher hydrogen content due to elevated combustion temperatures. Statistical analysis confirms that hydrogen ratio has a significant effect on efficiency and emissions, with a strong linear correlation between hydrogen fraction and CO₂ reduction. A blending range of 40–60% hydrogen provides the most balanced performance in terms of efficiency improvement, emission reduction, and cost feasibility.

Outdoor lighting, such as in community parks, plays a crucial role in supporting community activities at night while enhancing safety and comfort. Unfortunately, traditional electricity usage is often inefficient and has negative environmental impacts due to carbon emissions from fossil fuel-based power plants. This study aims to implement solar panel technology as an environmentally friendly lighting solution in Taman RukunWarga, located in Kelurahan Bendung, Kecamatan Kasemen, Kota Serang. The methods applied in this study include site surveys, assembly of the solar-powered lighting system, and performance analysis after installation. The lighting system consists of a 30 Wp solar panel, a battery (accumulator), a solar charge controller, a 12V 20W DC LED light, and other supporting components. The results show that this system can provide adequate and even lighting for the park area while being energy-efficient. One of the main advantages of this system is its ability to reduce operational costs by using renewable solar energy and requiring minimal maintenance. Additionally, the implementation of this solar-powered lighting system has increased public awareness of the importance of utilizing renewable energy and reducing carbon emissions in the surrounding environment. Overall, the installation of this solar-powered lighting system has proven to be a sustainable, energy-efficient, and environmentally friendly alternative for meeting lighting needs in community parks. This system not only reduces dependence on fossil fuels but also contributes to efforts to mitigate negative environmental impacts. By utilizing solar energy, this system serves as a positive example of the application of renewable energy technology in daily life, while helping to create a cleaner and healthier environment for the community.

In order to meet the difficulties of digital transformation and contemporary market dynamics, organizational innovation is essential.  The main concerns of organizational innovation are discussed in this article, along with obstacles such a lack of digital skills, high implementation costs, change aversion, and inflexible company culture.  A literature review using a qualitative methodology is the research strategy employed to examine sources pertaining to innovation, drivers, and inhibitors.  The findings demonstrate that technological integration, flexible leadership, a positive company culture, and efficient human resource management (HRM) procedures are all necessary for innovation to succeed.  Islamic principles like accountability (amanah), consistency (istiqamah), and honesty (shiddiq) also support the innovation culture. According to the article's conclusion, responsive HRM, adaptive culture, and controlled innovation can all help organizations become more resilient and competitive in the digital age.    

The existence of laws in health has been proven to have serious impacts on the health sector in various countries, including Indonesia. Through an analysis of philosophical texts and related research, this article reveals how corrupt practices affect the allocation of funds for the health system, disrupt the provision of adequate health facilities and services, and affect the quality of medical services. The economic implications of corruption on the health sector, including the high cost of obtaining quality health services and unequal access to health services. The social impacts of corruption are also analyzed, including poverty, unequal income distribution, and increased risk of criminal acts. In an effort to minimize the negative impacts of corruption on public health, this article proposes the implementation of strategies of transparency, accountability, and public participation. Implementation of the systeme-governmentand the active role of society ascitizen controlis a concrete step that can be taken to combat corruption and ensure more efficient and transparent use of public resources. The need for a joint role from the government, civil society, and the private sector in building a health system that is fairer, more transparent, and free from corruption. By strengthening the foundations of democracy and prioritizing transparency, Indonesia can move towards more equitable and quality public health for all people.

This study aims to explore and analyze the mechanisms of participatory communication in community-based tourism development, with a focus on strengthening community participation and understanding the dynamics of communication in community-based tourism development in Jatiluwih Tourism Village. The study employs a descriptive qualitative approach, using purposive sampling techniques. Jatiluwih Tourism Village was selected due to its status as a UNESCO World Cultural Heritage Site since 2012 and its significant potential for community-based tourism development, despite the community participation remaining suboptimal. Key informants include community leaders, tourism village managers, and community members directly involved in managing tourism based on local wisdom. Primary data were collected through observation and in-depth interviews, while secondary data were obtained from relevant documents and reports. Data validity was ensured through triangulation by comparing interview results, observations, and documents. The findings reveal that participatory communication—through access, voice, and control—plays a crucial role in the development of community-based tourism in Jatiluwih Tourism Village. While it has had positive impacts, gaps in control highlight the need for more inclusive mechanisms to ensure equitable community participation. This study underscores the importance of applying participatory communication to realize inclusive and sustainable community-based tourism.    

The National Health Insurance (JKN) program, administered by BPJS Kesehatan, has significantly expanded public access to healthcare services, particularly inpatient care. This study aims to analyze inpatient JKN reimbursement patterns at Surakarta General Government Hospital during the period of 2020 to 2024. The analysis focuses on five main variables: INA-CBGs grouping codes, class of care, severity level, INA-CBGs tariff, and actual hospital costs. A descriptive-analytic method with a quantitative approach was employed, utilizing secondary data extracted from the INA-CBGs system. The results indicate that inpatient reimbursements were predominantly concentrated in Class 3 services (64%–70%) and severity level 1 (45%–59%). From 2020 to 2022, respiratory-related cases dominated, likely due to the COVID-19 pandemic, while in 2023–2024 a shift occurred toward non-communicable diseases such as cardiovascular and metabolic conditions. A consistent negative tariff gap was identified, particularly in Class 3 and severity level 1, where INA-CBGs reimbursements were insufficient to cover actual service costs. These findings underscore the importance of periodic review of INA-CBGs tariff structures, reinforcement of Quality and Cost Control (KMKB), and optimization of reimbursement management information systems to enhance service efficiency and ensure the financial sustainability of JKN, especially in Type C hospitals that serve as the primary level of healthcare delivery.

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.

The marmet technique is a technique used to express breastmilk. This technique provides a relaxing effect and also reactivates the milk ejection reflex (MER) so that milk begins to drip. With the MER activated, breast milk will often spray out by itself. The marmet technique is a massage using two fingers. This method is often referred to as back to nature because it is simple and does not require cost. The purpose of the study was to determine the effect of the marmet technique on the smoothness of breast milk in postpartum mothers in the working area of the Mowewe health center in 2021. This type of pseudo-experimental research uses a nonequivalent control group design model. The research sample was the experimental group who were given treatment with marmet massage techniques on postpartum mothers as many as 10 people and the control group was those who were not given marmet massage treatment on postpartum mothers as many as 10 people. The results showed that there was no effect of marmet technique on the smoothness of breast milk in postpartum mothers in the working area of Mowewe health center. The difference between the average pretest and posttest in the control group using the paired sample t-test test obtained a t value = 3.240 and a p value = 0.010 (p < 0.05). It is hoped that the results of the study will serve as a source of information and add insight into the marmet method for smooth breastfeeding for postpartum mothers.  

This study explores the integration of AI-driven process optimization in Waste-to-Energy (WtE) systems to enhance urban sustainability. The research focuses on designing a gasification-based WtE system, incorporating AI predictive control to optimize energy conversion processes. The AI system adjusts operational parameters in real-time, improving energy conversion efficiency by 25% and reducing carbon emissions by 40%. Additionally, the system's waste-to-energy conversion rate is projected to increase by 20%, and operational costs are expected to decrease by 30%. Data collection and analysis are carried out using advanced sensors to monitor key parameters such as temperature, gas composition, and energy output, which are then processed by machine learning algorithms for predictive analysis. The results show that the AI optimization significantly enhances system performance, offering a sustainable solution for urban waste management. The study highlights the technical and operational challenges of integrating AI into existing WtE systems, including the need for infrastructure upgrades and scalability considerations. It also discusses the socio-economic impacts, including job creation, reduced energy costs, and improved public health. The findings demonstrate the potential of AI-based WtE systems in reducing waste, generating clean energy, and mitigating climate change, positioning them as a viable solution for sustainable urban development.

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.