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The advancement of information technology has driven companies to adopt technology-based systems to enhance operational efficiency while also increasing the complexity of information security risks. This study aims to analyse risk factors associated with information security assets, identify potential threats, assess risks, and provide mitigation recommendations. The OCTAVE method was applied to identify threats, vulnerabilities, and critical information technology assets, while FMEA was used to determine risk mitigation priorities based on the Risk Priority Number (RPN). The mitigation recommendations were developed in accordance with ISO 27001:2022 standards. Data collection was conducted through interviews with XYZ company representatives, identifying 34 information security asset risks, including 6 hardware failure potentials, 6 software failure potentials, 14 data failure potentials, 4 human resource failure potentials, and 4 network failure potentials. The risk categorisation results revealed 5 high-level risks, 6 moderate-level risks, 20 low-level risks, and 3 very low-level risks. The mitigation recommendations include three ISO/IEC 27001:2022 clauses: Human Resource Controls, Physical Controls, and Information Technology Controls.

This study aims to analyze the factors that influence the work discipline of employees of the Regional People's Representative Council (DPRD) of Malang Regency during operational hours. Work discipline, which includes compliance with regulations, effective use of time, responsibility, and absence rate, is a key element in improving organizational performance and efficiency. A qualitative approach was used with data collection techniques through observation, interviews, and document studies. The results of the study showed that the majority of employees comply with the regulations, but there were several cases of non-compliance and ineffective use of time. In addition, employee responsibility is generally high, although there are still obstacles in completing tasks on time. A good absence rate is an indicator of discipline, but some employees show a high level of absenteeism. The implications of this study include strategic recommendations such as strengthening work rules, time management training, and the use of digital absence technology to improve employee discipline. These findings are expected to help improve overall organizational performance.

This research examines the governance of the National Amil Zakat Agency of Barito Kuala Regency as an effort to optimize transparent, professional, and sharia-compliant zakat management. Although South Kalimantan has the highest zakat potential in Kalimantan Island, its zakat realization is still far from the target, reflecting the challenges of public trust and the effectiveness of the institution's governance. This study aims to evaluate the governance of Barito Kuala National Amil Zakat Agency using three main indicators: Zakat Core Principles Index, Sharia Compliance Index of Zakat Management Organization, and Financial Ratio Analysis. Using a qualitative-descriptive approach, data was collected through observation and internal document analysis. The result shows that the implementation of Zakat Core Principle scores 0.44 (good enough category), while sharia compliance scores 0.72 (good category), and financial ratio shows high efficiency and effectiveness in collecting and distributing zakat. Important findings reveal that the suboptimal documentation of Operational Procedur Standard, absence of internal audit, and high operational burden become obstacles to ideal governance. Although the management of zakat in Barito Kuala National Amil Zakat Agency has been running according to regulations and sharia principles, improvement of institutional structure, documentation, efficiency of human resources, and transparency of financial statements are needed. This research provides a practical contribution as an internal evaluation of the institution and a scientific contribution in the development of accountable and sustainable zakat management in Indonesia.

This study aims to strengthen industrialization in the livestock sector to increase productivity and investment at the district and city levels. The use of modern technologies, such as digital livestock management systems, sensor-based animal health monitoring, and automated feeding systems, has increased in recent years in response to the need for efficiency and improved production outcomes. Data were collected from various livestock units, ranging from small-scale to large-scale farms, through surveys and observations over a two-year period. The analysis results show a significant positive relationship between the level of investment in livestock technology and productivity improvements, both in terms of livestock population growth and production outputs (meat, milk, eggs). Higher investment in technology tends to result in better operational efficiency, lower animal mortality rates, and improved product quality. These findings indicate that accelerating the adoption of technology in the livestock industry can be an effective strategy to enhance the competitiveness and sustainability of the national livestock sector.

The problem of this research is how the implementation of the management control system in TOSERBA MMTC and what strategies are carried out to improve competitiveness in TOSERBA MMTC. The purpose of this study is to determine how the implementation of the management control system in TOSERBA MMTC and to determine what strategies are carried out to improve competitiveness in TOSERBA MMTC. This research method uses a qualitative approach. The data analysis techniques used are data reduction, data presentation and drawing conclusions. The results of the study TOSERBA MMTC has implemented a structured management control system at three levels: strategic, operational, and financial. Strategic involves quarterly planning based on historical data; operational combines computerized information systems and a collaborative work culture; while finance applies tiered authorization and bottom-up budgeting, which has successfully reduced budget deviations by 22%. To improve competitiveness, MMTC carries a hyperlocal strategy by selling 45% local products, digital transformation such as WhatsApp Business and dynamic discount systems, and marketing through local social media and digital loyalty programs, which have proven effective in increasing sales and customer retention.

In the digital era, companies are required to manage data optimally to improve operational efficiency. This research aims to analyze the role of data management and database systems in supporting company work efficiency. A descriptive approach is used by reviewing literature from various relevant academic sources. The results show that the implementation of a structured database system can reduce data redundancy, increase the speed of information access, and strengthen the decision-making process. With good data integration between divisions, the company can improve internal coordination and response to market changes. The findings confirm the importance of an effective data management strategy in supporting an organization's competitive advantage.    

Penelitian ini bertujuan untuk mengetahui dan menganalisis penilaian rasio profitabilitas pada kinerja keuangan PT Unilever Indonesia Tbk selama periode 2021 hingga 2023. Metode penelitian yang digunakan adalah kuantitatif deskriptif dengan menggunakan data sekunder yang diperoleh dari Bursa Efek Indonesia yaitu laporan keuangan tahunan PT Unilever Indonesia Tbk. Analisis dilakukan dengan menggunakan rasio profitabilitas seperti Gross Profit Margin (GPM), Net Profit Margin (NPM), Return on Assets (ROA), Return on Equity (ROE), dan dan Earning per Share of Common Stock (EPS). Hasil penelitian menunjukkan bahwa GPM perusahaan konsisten di atas standar industri dan mencerminkan efisiensi produksi yang baik. Namun, NPM dan ROA menunjukkan tren penurunan dan berada di bawah standar industri, menunjukkan adanya tekanan pada laba bersih dan efisiensi penggunaa aset. ROE mengalami peningkatan signifikan yang berada di atas standar industri, menandakan efektivitas pengelolaan modal sendiri. Sementara itu, EPS mengalami penurunan sejalan dengan penurunan laba bersih. Secara keseluruhan, PT Unilever Indonesia Tbk menunjukkan kekuatan dalam efisiensi produksi dan pengelolaan ekuitas, namun menghadapi tantangan dalam mempertahankan laba bersih dan efektivitas aset. Penelitian ini merekomendasikan efisiensi biaya operasional, optimalisasi penggunaan aset, serta strategi penguatan ekuitas untuk memperbaiki kinerja keuangan di masa depan. This study aims to determine and analyze the assessment of profitability ratios on the financial performance of PT Unilever Indonesia Tbk during the period 2021 to 2023. The research method used is quantitative descriptive using secondary data obtained from the Indonesia Stock Exchange, namely the annual financial report of PT Unilever Indonesia Tbk. The analysis was carried out using profitability ratios such as Gross Profit Margin (GPM), Net Profit Margin (NPM), Return on Assets (ROA), Return on Equity (ROE), and Earning per Share of Common Stock (EPS). The results of the study show that the company's GPM is consistently above the industry standard and reflects good production efficiency. However, NPM and ROA show a downward trend and are below the industry standard, indicating pressure on net profit and asset use efficiency. ROE has increased significantly above the industry standard, indicating the effectiveness of equity management. Meanwhile, EPS has decreased in line with the decline in net profit. Overall, PT Unilever Indonesia Tbk shows strength in production efficiency and equity management, but faces challenges in maintaining net profit and asset effectiveness. This study recommends operational cost efficiency, optimization of asset utilization, and equity strengthening strategies to improve financial performance in the future.

Village financial management is one of the key indicators in realizing effective village governance. However, in many villages, including Sidoan Timur Village, Sidoan District, Parigi Moutong Regency, Central Sulawesi, the practice of financial transparency and accountability still faces various obstacles. This study aims to assess the application of the principles of transparency and accountability in the financial management of Sidoan Timur Village, Sidoan District, Parigi Moutong Regency, Central Sulawesi, by referring to the perspective of village governance. The approach used is a qualitative method through case studies. Data were obtained through in-depth interviews, participatory observations, and review of official village documentation. Data analysis was carried out with the stages of data reduction, data presentation, and inductive conclusion drawing. The research findings reveal that village financial management has not fully implemented the principle of transparency, because financial information is not routinely published and administrative accountability is still formal, without active involvement from the community. In addition, the implementation of financial governance regulations has not been consistent at the operational level. In conclusion, the practice of transparency and accountability in Sidoan Timur Village needs to be strengthened by increasing the capacity of village officials, optimizing public information media, and empowering community participation. Further research is suggested to examine the comparison between several villages with a quantitative approach in order to obtain a broader picture of village financial management from a governance perspective.  

Artificial Intelligence (AI) has become an essential tool in the world of management for decision-making. This article examines the ways in which AI can be used to improve the quality and speed of decision-making, and how AI can improve the operational efficiency of companies. In addition, this article also examines the challenges and opportunities that companies face in adopting AI.In the rapidly evolving digital era, AI has become an essential component of modern business strategies. Today's managers are often faced with the challenge of analyzing very large and complex volumes of data. To make good and timely decisions, AI offers a potential solution with fast and precise data analysis capabilities.The use of AI in decision-making involves machine learning algorithms and models to efficiently process and analyze large amounts of data. This helps managers gain deeper and more accurate insights, enabling more effective decision-making.

Financial reports are made to provide information regarding the financial position, performance and changes in the financial position of a company and there are demands in reporting and compliance with the timeliness of submitting the company's financial reports. Consumer non-cyclical is a type of stock issued by companies whose business is not affected by the seasons or economic cycles in a country. The purpose of this study is to test and analyze the effect of operational complexity, management replacement and company size on audit report lag. The data used in this study are secondary data accessed through the website www.idx.co.id with 65 research samples. The results of this study indicate that operational complexity, management replacement, and company size have an effect on audit report lag.

According to data from the Ministry of Cooperatives and SMEs in 2024, Indonesia has more than 60 million SMEs, which are very important to its economy, contributing 60% to the national GDP. However, around 77.5% of SMEs do not keep financial records, while 22.5% have some form of financial reporting that is often inconsistent and poorly documented. Many entrepreneurs do not realize the importance of financial reports, instead focusing on business growth and sales. Lack of financial literacy leads to poor decision-making and operational transparency. Quality financial reports can facilitate access to capital and serve as an indicator of business success. Programs aimed at improving financial literacy and the importance of financial reporting have shown a positive impact on SME performance. The training program for Dapur Umi Ila SMEs in Bojongkerta aims to empower participants through education on financial management, emphasizing the importance of maintaining accurate financial records for proper decision-making and sustainable business growth. By creating financial reports, Dapur Umi Ila SMEs are expected to be more concerned about operational finances so that they can learn to make decisions based on relevant and reliable financial reports..

Belawan Port as the main port in Indonesia plays an important role in international trade. The Main Harbormaster and Port Authority Office (KSOP) of Belawan has the authority to supervise operational activities at the Port including regulating stevedoring workers (TKBM). This regulation is implemented through the TKBM Monitoring System (SIMON TKBM) to manage workers efficiently, transparently, and safely. This study uses a qualitative descriptive approach with literature study methods, interviews, observations and documentation then analyzed using data reduction methods, data presentation and drawing conclusions. The results of the study showed that the role of KSOP is very central in regulating TKBM through SIMON TKBM which includes TKBM supervision, competency improvement, rule enforcement, and coordination with stakeholders. However, technical obstacles were also found including the failure of duplicate data management and the absence of an automatic age filter in the system, as well as practical obstacles, namely the lack of coordination regarding TKBM competency certificates.

The role of school administration is very important in carrying out the basic functions of Islamic educational institutions. School administration as the center for managing teaching and learning activities also covers various fields ranging from resource management, financial management, to planning and implementing academic policies. This research aims to identify the role of school administration in improving the operational effectiveness and quality of Islamic education. The method used in this research is library research, which aims to collect, analyze and compile information from various literary sources in order to obtain a comprehensive understanding of the topic being researched. The data analysis technique used is descriptive-qualitative, with the aim of describing and interpreting information obtained from the literature. This research shows that the role of school administration is very important in improving the operational effectiveness and quality of Islamic education. Good administration includes technical management, creating an environment that supports the moral and spiritual development of students. With effective implementation, it is hoped that schools will be able to provide Islamic education that is not only of academic quality, but can also shape students' character in accordance with Islamic values. It is hoped that this research can become a reference for policy makers and Islamic education practitioners in formulating better management strategies, so as to improve the overall quality of Islamic education.

Research aims to design and develop a boiler system for heating Marine Fuel Oil (MFO) 180 CST by integrating Internet of Things (IoT) technology to enhance efficiency and operational monitoring. The methods used include boiler system design, selection of types and materials according to international standards, and the implementation of an optimal combustion system. IoT sensors are strategically placed to monitor key parameters such as temperature, pressure, and fluid flow in real-time. The collected data is transmitted to a cloud platform, enabling remote monitoring and automated performance analysis through a web or mobile-based application.. The research results indicate that IoT integration in the boiler system improves fuel heating efficiency, optimizes energy consumption, and facilitates easier monitoring and process control. Testing includes pressure tests, combustion efficiency, steam capacity, and material durability, with real-time monitoring to support performance analysis and early problem detection. Operational data evaluation allows for design adjustments or system settings to further enhance energy efficiency. With this innovation, the boiler system can operate more optimally, support energy efficiency, and facilitate predictive maintenance for sustainable industrial operations. The implementation of IoT in this system is expected to improve safety, effectiveness, and automation in boiler management, making it a more reliable and modern solution.

This study aims to analyze the causes of data duplication in companies, its impact on operational efficiency, and the solutions that can be implemented to prevent and address it. Using a qualitative method with a library research approach, this study examines various literature related to data management. The findings indicate that data duplication is caused by a lack of standardization, input errors, and weak integration systems. The recommended solutions include the implementation of database management technology, regular data audits, and strict data governance policies. With the right strategies, companies can enhance efficiency and ensure data accuracy in business decision-making.  

 The purpose of this study is to analyze the implementation of Database Management System (DBMS) in improving the operational efficiency of companies. This study uses a qualitative research method through a literature review, where various academic sources and literature are analyzed to identify the benefits, challenges, and impacts of DBMS implementation on operational efficiency. The results indicate that DBMS helps in structuring data management, enhancing data security, and accelerating decision-making processes. Future research is suggested to conduct empirical studies with a quantitative approach to measure the impact of DBMS implementation more specifically across various industrial sectors.

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.

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.

Archives management plays a crucial role in ensuring the accountability of an organization, as proper management of archives can enhance transparency and operational effectiveness. Based on observations made at the Maluku Province Regional Library and Archives Office, several issues were identified within the current archiving system. One of the key problems is the lack of knowledge about proper archives management, which causes significant barriers during the archiving process. The office employs two archivists with high school and diploma (D3) education backgrounds. Although they have attended archives management training, the current system remains ineffective, which in turn affects work efficiency, particularly in the process of searching for archives. In many instances, the required archives are either difficult to locate or entirely missing. This study aims to assess the impact of archives management on work efficiency at the Maluku Province Regional Library and Archives Office. The research employs quantitative analysis with a sample size of 42 employees. Data were collected through a survey method, using a questionnaire to capture relevant information from the participants. Simple linear regression analysis conducted using SPSS 24 revealed that archives management accounts for 35% of the variance in work efficiency, while the remaining 65% is influenced by other factors. The findings suggest that effective archives management has a positive and significant impact on employee work efficiency. Based on the results, several practical implications are suggested: (1) the current archiving system should be maintained and improved for better performance, (2) training and development programs should be implemented to enhance the skills of archivists, (3) archivists should be equipped with specialized skills in archives management to improve overall efficiency, and (4) ongoing coaching and mentorship for archivists should be provided to ensure continuous improvement.

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.