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This study aims to evaluate user satisfaction with the Inspekta Application at PT PLN UP2D S2JB using the PIECES Framework approach, which covers six key aspects: Performance, Information, Economy, Control, Efficiency, and Service. Data was collected through questionnaires distributed to 30 respondents who are active users of the application. The analysis results show that all PIECES indicators scored in the "satisfied" category, with the highest score in the efficiency aspect (4.14) and the lowest in the economic aspect (3.80). Although the Inspekta application has generally had a positive impact on operational efficiency and reporting processes, improvements are still needed in data security and technical support. This study offers strategic recommendations to enhance application quality and support the organization’s overall performance.

The study was conducted as an analysis of the impact of liquidity risk, credit risk, operational risk, asset growth, and net interest margin on financial performance. This study consists of five independent variables consisting of liquidity risk with a proxy for the Loan to Deposit Ratio, credit risk proxied by NonPerforming Loan, operational risk proxied by Operating Expenses to Operating Income, Asset Growth proxied by the Asset Growth Ratio, Net Interest Margin as a measure of net interest margin, and financial performance measured by Return on Assets. There are 39 banks with a total of 117 observation data samples in the study. With the object of research conventional banks that report their annual reports on the IDX in 2021-2023. Based on the results of the study, liquidity risk and asset growth do not affect financial performance. Credit risk and operational risk have a negative effect on financial performance. Also, the net interest margin has a positive effect on financial performance.

This study investigated the influence of financial report preparation skills, financial literacy, and accounting information systems on the performance of micro, small, and medium enterprises (MSMEs) in Cirebon Regency. The research adopted a quantitative approach with a causal associative design. Data were collected through closed-ended questionnaires distributed to 100 culinary-sector MSMEs selected using purposive sampling. The data analysis involved validity and reliability testing, classical assumption tests, and multiple linear regression. The findings revealed that financial report preparation skills, financial literacy, and accounting information systems had both partial and simultaneous positive effects on MSME performance. Specifically, the ability to prepare financial statements and the use of accounting systems significantly enhanced operational efficiency and financial outcomes, while financial literacy contributed to better financial decision-making. The study concluded that improving financial competencies and adopting technology-based accounting practices are key drivers for strengthening MSME performance in the region

This study aims to evaluate the operational efficiency of PT Asuransi Jasa Tania Tbk in 2023 using the operating expense to revenue ratio (Expense Ratio). The data used is the company's financial statements for the year ended December 31, 2023. The results show that PT Asuransi Jasa Tania Tbk successfully improved its operational efficiency significantly, with a decrease in the Expense Ratio by 17.24% to 48% compared to the previous year. This efficiency improvement was driven by strong net premium income growth, effective operating expense control, and investments in digitalization. Compared to the general insurance industry average in Indonesia, PT Asuransi Jasa Tania Tbk demonstrates a better level of efficiency. These findings highlight the importance of operational efficiency for the profitability and competitiveness of insurance companies, as well as the relevance of technology adoption in enhancing efficiency. Practical and theoretical implications are discussed.

The automotive industry is a strategic sector that plays a crucial role in national economic growth. Facing rapid digital transformation, automotive companies are increasingly adopting Business Intelligence (BI) systems to facilitate a data-oriented decision-making process. This study aims to explore the implementation of BI in XYZ, one of Indonesia’s largest car manufacturers. Using a descriptive qualitative approach through literature review, the research highlights how BI dashboards enable real-time analysis of vehicle sales trends, regional performance, and product models. The findings indicate that BI significantly enhances strategic planning, marketing accuracy, and operational efficiency. Visual tools such as Tableau provide centralized dashboards that facilitate faster insights, enabling companies to adjust production and distribution strategies based on data patterns. The study concludes that BI is not merely a visualization tool but a vital element of the digital transformation strategy in the automotive sector.

This article aims to examine the role of leadership in mass media management through a case study of a private broadcasting institution in Indonesia. The focus of the research is on how leadership styles influence organizational performance, editorial decision-making, and operational effectiveness. A qualitative approach was employed, using in-depth interviews, participatory observation, and document analysis on a national private broadcasting institution. The findings reveal that visionary, communicative, and adaptive leadership plays a crucial role in fostering synergy across departments and enhancing responsiveness to the dynamics of the media industry. These results highlight the significance of strategic leadership in maintaining the sustainability and credibility of private broadcasting institutions amid the challenges of digitalization and increasingly competitive markets.

This research was conducted at PTPN IV Regional II Adolina Palm Oil Plantation and Mill Unit with the aim of evaluating the effect of Ripple Mill machine efficiency on palm kernel impurity levels through a multiple linear regression analysis approach. Ripple Mill is a vital piece of equipment in the palm kernel cracking process, which plays a role in determining the quality of the palm kernel. This machine works by breaking the kernels to produce kernels that are separated from the shells. However, when machine efficiency decreases due to technical and operational factors, palm kernel impurity levels tend to increase, characterized by an increase in shell fragments, fibers, and other foreign materials. This condition can have an impact on decreasing product quality and increasing further processing costs. Data collection was carried out over five days of observations covering the variables of machine efficiency and kernel impurity levels obtained from production results. The study found a strong negative correlation between machine efficiency and impurity levels in palm kernels. This means that the higher the efficiency of the Ripple Mill, the lower the resulting palm kernel impurity levels. This finding also confirms that quality control in the production process is not only determined by raw material factors but also highly dependent on the performance of the processing machine. Furthermore, the research results demonstrate the importance of implementing preventive maintenance strategies, including regular inspections of the rotor bar, square bar, and drive motor, as well as regulating operating parameters such as rotational speed and machine load. Proper preventive maintenance and operational control are essential to sustain machine efficiency and product quality. Therefore, continuous improvement in technical and managerial aspects is essential to maintain palm kernel quality and support optimal palm oil mill productivity.

This study aims to analyze the effect of leverage, liquidity, and company size on profitability in food and beverage companies listed on the Indonesia Stock Exchange (IDX) during the 2019–2023 period. Testing was conducted both partially and simultaneously to obtain a comprehensive picture of the relationship between variables. This study uses a quantitative approach with a sample of 14 companies selected through a purposive sampling method based on certain criteria, such as the completeness of annual financial reports during the study period and the availability of relevant data. The data used are secondary data obtained from the official IDX website (www.idx.co.id), including annual financial reports containing information on total assets, total liabilities, total equity, financial ratios, and the company's profit level. Data analysis was carried out using the multiple linear regression method using SPSS version 26 software, so that the effect of each independent variable on the dependent variable can be tested both individually and together. The results of the study indicate that simultaneously, the variables leverage (X1), liquidity (X2), and company size (X3) have a significant effect on profitability (Y). However, partial test results revealed that leverage had a negative and significant effect on profitability, indicating that a high proportion of debt can reduce a company's ability to generate profits. Meanwhile, liquidity and company size were not shown to have a significant influence on profitability, suggesting that these factors are not the main determinants of profit performance in this sector. This study implies that food and beverage company management needs to carefully consider capital structure to maintain profitability. For further research, it is recommended to add other variables such as operational efficiency, sales growth, and dividend policy, as well as extend the observation period for more in-depth and representative analysis results.

Employee performance is the result of an individual's efforts in carrying out their duties and responsibilities, which is reflected in the quality and quantity of work achieved. PT. Mitra Tunas Harapan, a company engaged in providing construction services in Cilegon, has various activities that include planning, design, project supervision, and construction management services. To achieve company goals, employee performance is one of the important factors influencing the company's operational success. Several factors believed to influence employee performance include communication and workload. This study aims to analyze the effect of communication and workload on employee performance at PT. Mitra Tunas Harapan. This study involved a population consisting of 36 employees at the company. The sampling technique used was nonprobability sampling with a total sampling method, meaning the entire population was sampled in this study. The results showed that communication partially had a positive and significant effect on employee performance. This indicates that good communication between superiors and employees, as well as between employees, can improve task understanding and streamline work processes, which ultimately contribute to improved performance. In addition, workload was also shown to have a positive and significant effect on employee performance. A workload that matches employee capacity can improve their productivity and work quality. Simultaneously, both communication and workload have a positive and significant impact on employee performance. Therefore, companies need to address both factors to improve employee performance and optimally achieve company goals. Furthermore, this study highlights the importance of effective communication management within organizations. Clear and open communication between managers and employees can reduce misunderstandings and improve coordination in carrying out tasks.

This study investigates how data visualization helps retail companies make Business Intelligence (BI)-based decisions. The study processed one year of sales data with a quantitative descriptive approach to find seasonal patterns, performance by region, and customer consumption tendencies. The analysis results show that BI software such as Microsoft Power BI can visualize data more easily and accelerate understanding of business performance. Strategic decisions such as promotional planning and product inventory management are more effective with interactive visualization, which allows decision makers to see data in a more understandable way. The study found that data visualization in BI systems can help retail businesses make better decisions. These results show the importance of incorporating BI into a company's operational processes and using interactive visualization to improve overall business performance.

Background: The rapid advancement of digital technologies in the Industry 4.0 era has transformed industrial mechanical systems into highly interconnected and data driven environments through the integration of sensors, the Internet of Things (IoT), data analytics, and cyber physical systems. This increasing complexity requires more adaptive and accurate monitoring and prediction methods than conventional simulation approaches, which often face limitations in capturing real time dynamic system behavior. Objective: This study aims to develop a predictive performance model for industrial mechanical systems by integrating Digital Twin technology with Physics Informed Machine Learning in order to improve monitoring accuracy and support predictive maintenance strategies. Methods: This research adopts a data driven modeling and simulation approach by developing a digital representation of an industrial mechanical system that is connected to real time sensor data. The prediction model is constructed using a Physics Informed Neural Network (PINN), which integrates operational data with physical principles governing system dynamics. The research process includes the development of a Digital Twin model, integration of sensor data, training of the PINN model, model validation using experimental data, and evaluation of prediction performance using statistical metrics. Results: The results indicate that the integration of Digital Twin technology and PINN significantly improves the prediction accuracy of industrial mechanical system performance compared with conventional simulation methods and purely data driven machine learning models. The proposed model is capable of representing system dynamics more consistently, accurately following sensor data patterns, and providing strong potential for supporting machine condition monitoring and predictive maintenance strategies in modern industrial environments.

In the increasingly competitive hospitality industry, the role of the front office has become very crucial because it is the first and last point of interaction between hotels and guests. The operational success of a hotel is largely determined by the performance of the workforce in the front office, which directly affects the first impression, guest satisfaction, and overall reputation of the hotel. This study discusses in depth the importance of the Front Office Workforce Maintenance Program at Hotel Room Inc Bandung as a human resource management strategy to improve service performance. The program covers various aspects, such as providing regular training, developing communication skills, understanding guest management, and improving employee well-being, motivation, and loyalty. The main goal of this program is to minimize turnover rates, improve workforce competence, and maintain optimal hotel service standards. Through this approach, hotels can create a conducive working atmosphere, consistent service, and a memorable stay experience for guests. The findings in this study indicate that investment in workforce maintenance directly has a positive impact on service quality and guest satisfaction, which is the key to success in the modern hospitality industry.

The Ripple Mill machine is one of the vital components in a Palm Oil Mill (PKS) that functions to separate the palm kernel from its shell. This process is very important because the quality and quantity of the palm kernel produced will directly affect the economic value and production efficiency in the palm oil industry. At PTPN IV Regional II Plantation Unit and Adolina PKS, various technical problems were found that caused a decrease in machine performance, including rotor bars and square bars that experienced wear due to age and improper machine settings. In addition, other damage that often occurs is a broken van belt due to age and excessive tension, feeder motor dysfunction caused by excessive load, and motor overheating that occurs due to age and high workload. To overcome these problems, this study uses the Failure Mode and Effect Analysis (FMEA) method. This method aims to identify various potential failures, assess the severity, frequency of occurrence, and detection capability, so that the Risk Priority Number (RPN) value can be calculated as a basis for repair priorities. The results showed that worn rotor bars and worn square bars had the highest RPN values, each at 280, equivalent to 40.23% of the total identified risks. This indicates that these two components are critical points requiring immediate repair and maintenance attention. Applying FMEA in this context provides tangible benefits, including helping the company formulate a more targeted maintenance strategy, reducing the risk of recurring damage, and minimizing downtime that impacts production. With more systematic maintenance, operational efficiency can be improved while extending the lifespan of the Ripple Mill machine.

This community service program focuses on transforming financial management at Pharmacare Pharmacy Ciputat through the integration of digital accounting and strengthening human resource (HR) management. The background to this activity stems from the problem of partners who still rely on manual recording for financial transactions, inventory management, and report preparation. This condition results in inefficiency, late reporting, potential recording errors, and difficulties in strategic decision-making. In addition, limited HR competency in the use of digital accounting technology and the absence of financial standard operating procedures (SOPs) are major obstacles to improving organizational performance. The main objective of this activity is to implement an integrated digital accounting system capable of producing real-time financial reports, increasing inventory management efficiency, and building transparent financial governance. Simultaneously, employee competency is improved through intensive training and mentoring in the use of accounting software and the preparation of financial SOPs. The involvement of students and faculty in this activity aligns with the spirit of Independent Learning and Independent Campus (MBKM) and supports the achievement of the university's Key Performance Indicators (IKU), particularly in providing off-campus experiences and utilizing work results for the community. Through this program, it is hoped that Pharmacare Ciputat Pharmacy can improve operational efficiency, strengthen business sustainability, and provide more optimal healthcare services. More broadly, this activity contributes to community economic empowerment by strengthening the capacity of small businesses in the healthcare sector.

The problem faced by PT. Dinda Holding Company (DHC) is that it has difficulty in determining the best employees from a number of employees with varying performance qualities. Until now, the selection process has been carried out manually, taking into account the subjective assessments of the owner or store manager, without a structured evaluation system. As a result, employee selection tends to be inconsistent and leads to dissatisfaction among both employees and the business owner. Additionally, selected employees do not always demonstrate optimal performance, which impacts service quality and the operational sustainability of the store. Therefore, a decision support system was designed to assist PT. Dinda Holding Company (DHC) in selecting the best employees objectively, quickly, and accurately. Employees are human resources used as a driving force in the business operations of a company. The company faces the challenge of maintaining its competitive edge, which requires data on the performance of employees with good performance. This system applies the Simple Additive Weighting (SAW) method to perform calculations and evaluations based on predetermined criteria. The result of this system development is a desktop-based application using the Java programming language and a MySQL database, which is expected to assist management in making more measurable and structured decisions.

This study was conducted to analyze the effect of suction power effectiveness on cyclones in Light Tenera Dust Separator (LTDS) 1 and 2 units on the level of palm kernel losses at PTPN IV Regional II Plantation Unit and Adolina Palm Oil Mill (POM). Palm kernel loss is a significant factor that can reduce production efficiency, as palm kernel has high economic value and is the main product in the palm oil industry. Kernel losses not only reduce production efficiency but also create significant economic disadvantages for the palm oil mill. The research method used was a gravimetric method with a field experiment approach. The research process included sampling from LTDS waste, separating kernels from shells, and measuring suction speed using an anemometer to determine cyclone performance. The gravimetric method was applied to quantify the proportion of kernel mixed with shell waste, providing measurable data on suction efficiency. The results showed that low suction power effectiveness was directly proportional to the increasing number of kernels that were thrown away with the shells. In other words, poor suction performance directly correlates with higher kernel losses. The main contributing factors include leaks in the cyclone, suboptimal blower settings, and inadequate operator skills. Operational skills and equipment maintenance were identified as crucial determinants of system performance. Based on these findings, improvements to the suction system are recommended through routine maintenance, leak checks, and blower setting calibration. Furthermore, operator training is required to precisely adjust suction power. By maintaining suction effectiveness and optimizing blower settings, kernel losses can be reduced below the standard threshold of 2.0%. Thus, mills can improve operational efficiency while minimizing economic losses due to palm kernel loss.  

This research focuses on the development and evaluation of an Adaptive Edge-AI framework designed to optimize real-time data processing and decision-making in resource-constrained environments, specifically within smart city infrastructures. The primary problem addressed is the challenge of minimizing latency, reducing energy consumption, and ensuring the reliability of Cyber-Physical Systems (CPS) when using Internet of Things (IoT) devices. The objective of the study is to assess the effectiveness of this framework in real-world smart city applications such as traffic monitoring, environmental sensing, and smart utilities management. The proposed method integrates lightweight AI models, edge computing, and adaptive resource management techniques, including Federated Learning and Neural Architecture Search, to ensure optimal performance while addressing hardware constraints. The main findings reveal that the framework significantly improves real-time inference speed, reduces energy consumption of IoT devices, and enhances CPS reliability by minimizing communication delays and ensuring continuous system operation despite network disruptions. The application of this framework to smart transportation and urban utilities further demonstrates its potential to optimize city management processes. The study concludes that the Adaptive Edge-AI framework offers a promising solution for smart cities, enhancing operational efficiency, sustainability, and resilience. It is recommended for integration into smart city infrastructures to enable better resource management and decision-making in real-time applications.

The rapid increase in Ro-Ro (Roll-On/Roll-Off) vessel and vehicle traffic at Tanjung Perak Port, Surabaya, has exposed critical inefficiencies in port operations, particularly at key berths such as Jamrud Utara and Jamrud Selatan. These inefficiencies manifest as prolonged queuing times for Ro-Ro vessels and land-based vehicle congestion, leading to delays in cargo handling and increased operational costs. This study aims to evaluate the operational performance of Ro-Ro terminals based on queue time indicators for both Ro-Ro vessels and vehicles. Employing a quantitative descriptive approach, the research integrates the M/M/c queuing model and Fishbone Diagram analysis to identify systemic causes of operational delays and overcapacity. The study reveals that berth utilization rates exceed 75%, indicating saturation, while limited buffer zones and disorganized traffic flow exacerbate vehicle queues. As a case study, the findings offer data-driven insights and practical recommendations, including schedule optimization, dedicated traffic lanes, and infrastructural upgrades to mitigate congestion. The proposed framework not only enhances service efficiency at the port but also contributes to broader logistics performance in Eastern Indonesia. By combining classical queuing theory with root cause analysis, this research introduces a methodological synergy that can be replicated across other Ro-Ro terminals facing similar challenges. The findings underscore the urgent need for integrated port traffic management and infrastructural adaptability in response to growing maritime logistics demands.

PT Perkebunan Nusantara I (PTPN I) is an Indonesian State-Owned Enterprise (BUMN) specializing in plantation operations, particularly the cultivation and processing of primary commodities, including rubber, coffee, and tea. Additionally, the company engages in secondary ventures such as tobacco production, plastic sack manufacturing, and asset optimization. As of December 1, 2023, PTPN I functions as a subholding entity with regional offices strategically located in Sumatra, Java, and Sulawesi to enhance operational efficiency. The company was originally established in Aceh in 1961 as PPN Kesatuan Aceh, marking the beginning of its long-standing presence in the industry.This study aims to analyze the impact of transformational leadership, internal communication, and work ability on both organizational commitment and organizational performance at PTPN I’s Surabaya region. Utilizing a quantitative causal-explanatory approach, primary data was collected through structured questionnaires. The target population comprised employees of PTPN I in Surabaya, with 100 respondents selected via purposive sampling. Data analysis techniques included descriptive statistics and SEM-PLS (Structural Equation Modeling-Partial Least Squares). Findings reveal that transformational leadership, internal communication, and work ability significantly and positively influence organizational commitment. Likewise, transformational leadership, work ability, and organizational commitment exhibit a significant positive relationship with organizational performance. However, although internal communication has a favorable effect, its impact on organizational performance is not statistically significant in this context.

This study aims to determine the influence of company size, sales growth, independent operating costs on financial performance as measured byreturn on asset(ROA) in cosmetic companies listed on the Indonesia Stock Exchange (IDX) in 2019-2024. This research is a quantitative research, namely data in the form of numbers. The data source in this study is secondary data, namely data that is not obtained indirectly, data obtained from annual reports obtained from the company's website or from the official website of the Indonesia Stock Exchange. The population in this study is all cosmetic companies listed on the Indonesia Stock Exchange totaling 7 companies. The sample of this study uses saturated sampling where all members of the population are used as samples and the sample in this study is 42 samples. The analysis method in this study uses multiple linear regression analysis which is processed using the programSPSS versi 26.0 for windows. The results of this study indicate that company size significantly affects financial performance (ROA), sales growth does not affect financial performance (ROA), while operational costs significantly affect financial performance (ROA).