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Flower disease detection is a common challenge in modern agriculture. Various factors, such as changes in leaf color, shape, petal structure, and environmental conditions, make it difficult to achieve high accuracy with conventional models. Transfer learning is an effective solution to improve model performance in image detection, especially when available data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The process included data processing, increasing the data volume, model training, and result verification. The results showed that the EfficientNet-B0 model provided the highest accuracy of 97.2%, significantly better than the CNN model created from scratch with an accuracy of 85.1%. This study proves that the transfer learning method is very effective in improving the accuracy of flower disease detection. These results confirm that transfer learning is effective for detecting plant diseases with higher accuracy, especially when the dataset is limited.  

This study aims to analyze the effect of workload and work stress on the performance of teachers at Madrasah Aliyah Negeri Tana Toraja. The study uses a quantitative approach with a population of 38 teachers, all of whom were selected as the sample using the total sampling technique. Data collection was carried out using questionnaires, and data analysis was performed using multiple linear regression assisted by SPSS version 26. The results show that workload has a significant positive effect on teacher performance, with a t-value of 2.081, which is greater than the t-table value of 2.030, indicating a significant positive relationship between the two. In addition, work stress also has a significant positive effect on teacher performance, with a t-value of 8.046, which is greater than the t-table value of 2.030. Simultaneously, both variables have a significant effect on teacher performance at Madrasah Aliyah Negeri Tana Toraja. In conclusion, an increase in workload and work stress, when managed properly, can improve teacher performance. Therefore, it is important for the school to pay attention to managing workload and work stress to optimize teacher performance.

This article aims to provide a comprehensive literature review on how professional ethics can serve as an effective strategy to prevent fund misuse within organizational financial management. Professional ethics is viewed as a set of moral values, behavioral norms, and professional standards that guide financial managers to perform their duties with honesty, responsibility, and without conflicts of interest. In the context of financial management, these duties include recording, budgeting, monitoring, and reporting financial activities, all of which require accuracy and transparency. The study highlights five main principles of professional ethics: integrity, objectivity, professional competence, confidentiality, and professional behavior. These principles clarify rules, strengthen accountability, and ensure that financial processes comply with established standards. The literature review shows that applying professional ethics not only encourages individuals to act correctly but also enhances responsibility, improves performance, and strengthens financial oversight. Integrity and objectivity play a crucial role in preventing report manipulation, budget inflation, and fund misuse, as these principles demand moral courage and fair decision-making. Professional competence ensures that every financial process is carried out accurately and in accordance with regulations, while confidentiality protects sensitive information from misuse. Professional behavior emphasizes adherence to laws, organizational policies, and professional standards. The article also identifies several supporting factors that enable the effective implementation of professional ethics, such as strong internal policies, leadership commitment to integrity, an ethical workplace culture, layered supervision systems, and continuous ethics training. Conversely, common challenges include weak internal controls, limited understanding of ethics, organizational pressure, conflicts of interest, and inconsistent application of ethical standards. Therefore, this article underscores that integrating professional ethics into organizational financial policies, procedures, and management systems is a key step in preventing fund misuse and strengthening stakeholder trust in the organization’s transparency and accountability.

The decline in the performance of the die casting machine in 1998 after a long period of producing copper terminals showed dimensional defects and instability in product quality, especially in nozzle clogging, reduced copper flow, crust buildup on the gooseneck, plunger movement obstruction, and hydraulic pressure leaks. The purpose of planning the replacement and repair of die-casting machine components is to obtain replacement and repair costs, replacement and repair schedules for the period 2026, and the ratio of maintenance costs to profits. The replacement and repair planning method includes collecting previous maintenance data, applying the inspection-replace-repair-overhaul (IRRO) method, evaluating component conditions, predicting component service life, predicting labor costs, predicting supporting equipment to be used in maintenance, predicting the time to replace spare parts or reinstall repaired components, estimating replacement and repair costs for the period 2026, and calculating the ratio of replacement and repair costs to profits. The planning results obtained replacement and repair costs for the 2026 period are 75.770.000,- IDR with an estimated die casting machine rental rate of  1,500,000 IDR/hour which has the potential to be rented for 1,200 hours/year, and the ratio of maintenance costs to profits is 10,02 % which implies that the die casting machine with a capacity of 40 units/hour is still suitable for use and has the prospect of generating profits for the next few years.

The problem with a 4-stroke gasoline engine-driven electric generator is a decrease in tool performance due to wear on important components for the stator, cooling fan, air filter, oil filter, and gasket. The purpose of component replacement planning is to obtain replacement costs, maintenance schedules in 2027, and the ratio of maintenance costs to profits. The component replacement planning method includes collecting maintenance data from previous years, applying the inspection-replace-repair-overhaul (IRRO) method, assessing component conditions, predicting component lifespan, predicting labor costs, predicting supporting equipment to be used in maintenance, predicting spare part replacement times, predicting maintenance costs in 2027, and calculating the ratio of maintenance costs to profits. The results of the replacement planning obtained maintenance costs in 2027 amounting to IDR 570,007,- with an estimated electric generator rental rate of IDR 30,000,-/hour which has the potential to be rented for 128 hours/year, a profit of IDR 3,840,000,- was obtained, and the ratio of maintenance costs to profits was 14.84% which implies that a 2.5 kW electric generator that uses gasoline-pertalite fuel of around 1.5 liters/hour at maximum power is still suitable for use in the next few years and has the potential to generate profits.

This study aims to analyze the role of human resource management (HRM) in improving performance and retention among healthcare professionals in health service facilities. Using a systematic literature review approach based on the PRISMA method, this study reviewed 25 international and national journal articles published between 2015 and 2025. The findings indicate that strategic HRM practices—such as competency-based recruitment, continuous professional development, fair compensation, and transformational leadership—positively influence both performance and retention of healthcare workers. Additionally, factors like organizational culture, work-life balance, and psychological well-being play a mediating role in enhancing job satisfaction and organizational commitment. The results highlight that HRM practices are not merely administrative functions but strategic elements that determine healthcare organizations’ sustainability and competitiveness. The study contributes to the development of theoretical frameworks in strategic HRM and provides practical recommendations for healthcare managers to design more adaptive, fair, and human-centered HR policies.

Background: The development of industrial technology in the Industry 4.0 era has encouraged the implementation of intelligent monitoring systems to improve machine reliability and operational efficiency. However, machine fault diagnosis systems based on artificial intelligence often face limitations in terms of interpretability because the models used are complex and difficult to explain. Objective: This study aims to develop a deep learning-based industrial machine fault diagnosis system integrated with an Explainable Artificial Intelligence (XAI) approach to improve diagnostic accuracy while providing interpretable insights for users. Method: The research method involves collecting data from industrial machine sensors consisting of vibration signals, temperature measurements, and acoustic signals, followed by data preprocessing and feature extraction processes. The processed data are then used to train a deep learning-based diagnostic model, after which explainability methods such as SHAP or LIME are applied to analyze the contribution of each feature to the model’s prediction results. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results: The results indicate that the proposed deep learning model achieves better performance compared to conventional machine learning methods such as Support Vector Machine and Random Forest. Furthermore, the explainability analysis reveals that vibration amplitude, increases in machine component temperature, and anomalies in acoustic signals are the main factors influencing machine fault detection. Therefore, the proposed system not only improves the accuracy of machine fault diagnosis but also provides transparency in the decision-making process, thereby supporting the implementation of predictive maintenance in smart manufacturing environments.

This study explores community social activities within the tradition of Wayang Golek performances as a form of living hadith in the city of Bogor. The Wayang Golek tradition reflects the practical implementation of the teachings of the Prophet Muhammad SAW in the daily lives of the Sundanese community. Using a qualitative approach, the study involved participant observation and in-depth interviews with puppeteers and audience members. The findings indicate that Wayang Golek performances function not only as cultural entertainment but also as an indirect medium for strengthening moral values, mutual cooperation, and Islamic preaching. Social activities surrounding the performances include communal interaction, preservation of local culture, and reinforcement of religious identity amid the challenges of modernization. The study further reveals that the concept of living hadith is embodied in wayang narratives that convey ethical messages aligned with the sunnah of the Prophet. These findings highlight the importance of revitalizing local cultural traditions as an effective medium for contemporary Islamic education and value transmission.

Background: The development of modern manufacturing systems requires production scheduling strategies that not only improve productivity but also optimize energy utilization. Multi-machine production systems with job-shop configurations exhibit high complexity due to dynamic interactions between machines, job queues, and varying processing times, making conventional scheduling methods less effective in handling changing operational conditions. Objective: This study aims to develop and evaluate a reinforcement learning based production scheduling approach to improve production efficiency while reducing energy consumption in multi-machine manufacturing systems. Methods: This research employs a job-shop based multi-machine production simulation model as the experimental environment. The scheduling problem is formulated as a Markov Decision Process, enabling the implementation of reinforcement learning algorithms, namely Q-learning and Deep Q-Network, to learn optimal scheduling policies through interaction with the simulation environment. Energy consumption parameters are incorporated into the reward function so that the learning agent can consider energy efficiency in the scheduling decision-making process. System performance is evaluated using three main metrics, namely energy consumption, throughput, and makespan. Results: The experimental results show that the reinforcement learning based scheduling approach achieves better performance compared to conventional scheduling methods, resulting in lower energy consumption, higher job completion rates, and shorter production completion times within the multi-machine manufacturing system.

The objective is to analyze the competitiveness of modern retail through the integration of SWOT analysis as a basis for formulating more targeted strategies using qualitative descriptive methods. This paper identifies the strengths, weaknesses, opportunities, and threats that drive the performance of modern retail based on literature and empirical findings from various previous studies. The results of the analysis show that the main strengths of modern retail lie in product completeness, competitive prices, strategic locations, and operational efficiency. Meanwhile, weaknesses arise from suboptimal digitization, minimal online promotion, limited service innovation, and outdated inventory management. Meanwhile, opportunities arise from changes in digital-based shopping behavior, while threats emerge from the dominance of large retailers, e-commerce competition, and economic fluctuations. Based on the SWOT integration, a strategy is formulated that includes strengthening digitalization, optimizing online marketing, improving service quality, and modernizing operational systems to support long-term competitiveness.

This research investigates the effect of fundamental factors, namely the current ratio, debt to equity ratio, and return on equity on stock returns of mining firms listed on the Indonesia Stock Exchange (IDX) during 2021–2023. The research highlights the utility of understanding a firm’s financial performance in guiding investment selection within the capital market. Although the mining industry contributes significantly to Indonesia’s economy, stock movements in this sector are often subject to uncertainty due to market fluctuations and commodity price volatility. This research utilizes secondary data from annual financial statements and stock price records of 51 IDX-listed mining companies over the study period. Panel data regression, combined with descriptive and quantitative statistical techniques, was employed using E-Views 12 software. The findings reveal that stock returns are significantly influenced by the current ratio, debt to equity ratio, and return on equity. These results provide useful insights for investors, financial analysts, and corporate management by emphasizing the function of fundamental indicators in assessing stock performance, particularly within the mining sector.

This study aims to analyze the influence of work discipline and work motivation on employee performance at Bank Sulutgo Tondano. The research method used is a quantitative approach with multiple linear regression analysis. The population in this study consists of all 76 employees of Bank Sulutgo Tondano, and the sample was obtained using a saturated sampling technique. Data collection was carried out through questionnaires. The results of the study indicate that partially, work discipline has a positive and significant effect on employee performance, and work motivation also has a positive and significant effect on employee performance. Simultaneously, work discipline and work motivation have a significant effect on employee performance, with a coefficient of determination of 44.4%, indicating that these two variables are able to explain the variation in employee performance. Meanwhile, 55.6% is influenced by other factors outside the scope of this study. Therefore, it can be concluded that improving work discipline and work motivation will have an impact on employee performance at Bank Sulutgo Tondano.  

Public health administration plays a strategic role in strengthening primary health care governance amid increasingly complex health system challenges. This study aimed to synthesize recent evidence on the development, challenges, and implications of public health administration during the period 2020–2025. A literature review with a systematic approach was conducted by analyzing peer-reviewed articles retrieved from major scientific databases and selected through a PRISMA 2020 flow process. The findings indicate that effective public health administration—characterized by strong governance, adaptive leadership, and the integration of digital health information systems—contributes to improved performance of primary health care services in terms of service quality, efficiency, and sustainability. However, the review also reveals persistent gaps, particularly the limited empirical evidence examining causal relationships between administrative capacity and health service performance, as well as challenges related to human resource readiness and data governance in digital transformation. These findings highlight the need to strengthen administrative capacity, leadership competencies, and integrated information systems to support resilient primary health care. The study provides theoretical and practical implications for policymakers, health managers, and researchers in advancing public health administration toward more effective and sustainable health systems.

This study aims to examine the effect of green innovation on company value, with financial performance as a mediating variable, in the mining industry. This study uses a systematic literature review approach by examining various relevant previous studies. The results of the study indicate that green innovation plays a significant role in improving environmental performance and operational efficiency of companies, which in turn positively impacts financial performance. Good financial performance is a key factor in strengthening company value and stakeholder trust. These findings confirm that the implementation of green innovation not only supports environmental sustainability but also provides long-term economic benefits for mining companies. This study also found that companies that successfully implement green innovation tend to have a better image in the eyes of investors and the public, which contributes to increasing the company's market value. These findings confirm that the implementation of green innovation not only supports environmental sustainability but also provides long-term economic benefits for mining companies, strengthening their position in an industry that increasingly prioritizes sustainability and social responsibility.

This study aims to provide a comprehensive overview of the implementation of green finance and its relationship with the financial performance and profitability of banking institutions in Indonesia. Although sustainable finance policies have been continuously strengthened by regulators and stakeholders, the contribution of green financing to overall banking performance is still developing gradually, making it important to conduct a more focused and systematic analysis of its effectiveness. This research specifically aims to describe the application of green financing practices, assess financial performance conditions, and analyze bank profitability during the 2020–2024 period. The study employs a descriptive quantitative approach using secondary data on green financing distribution, financial performance indicators such as the Capital Adequacy Ratio (CAR), Non-Performing Loans (NPL), and Loan to Deposit Ratio (LDR), as well as profitability measured through Return on Assets (ROA). The findings indicate that the implementation of green finance has the potential to enhance long-term financial stability and improve profitability in the banking sector. This study implies that expanding green financing can serve as a relevant and sustainable business strategy for the banking industry while simultaneously supporting national sustainability and environmental development objectives.

This research was conducted at PT PLN Nusantara Power UP Paiton, one of the largest coal-fired power plants (PLTU) in Indonesia, which focuses on reducing the generation of hazardous and toxic oil waste through the implementation of an oil purification system. The use of large amounts of lubricating oil in the Electro-Hydraulic System (EHS) has the potential to produce high amounts of used oil waste. To address this, the company implemented two main technologies, namely Water Content and Varnish Removal, which function to reduce water content and varnish layers so that the oil can be reused without reducing engine performance. This research used a quantitative descriptive method with data collection techniques through field observations, interviews, and operational documentation from 2021 to 2024. The results showed that the oil purification system was able to reduce hazardous and toxic waste generation by 11.46 tons over four years. In addition to providing environmental benefits, the implementation of this system also resulted in savings in oil waste costs of approximately Rp6,200,560,000. Technically, purification maintains engine performance by reducing water and varnish content, while from an environmental perspective, this activity supports the principle of reduce in hazardous and toxic waste management. Overall, the oil purification system has proven effective in improving operational efficiency, extending oil life, and supporting sustainable waste management for industrial operations.

This study aims to examine the strengthening of school literacy culture through the establishment of a Literacy Corner at SMPN 4 Mangarabombang as a strategy to enhance students’ reading interest and engagement in literacy activities. The research problem arises from students’ low reading intensity and the limited availability of literacy-supporting facilities at school. A qualitative descriptive method was employed, utilizing observation, interviews, and documentation as data collection techniques. The findings indicate that the Literacy Corner significantly improves students’ access to reading materials, fosters more consistent reading habits, and creates a more conducive learning environment. Moreover, the involvement of teachers and students in managing the Literacy Corner strengthens their sense of ownership and increases active participation in school literacy activities. By facilitating a space dedicated to reading, the initiative addresses the challenges related to the scarcity of resources and encourages students to make reading a regular activity. Teachers also reported increased student motivation and engagement in literacy programs, which translates into greater academic performance. These results highlight that establishing a Literacy Corner is an effective and strategic approach to reinforcing school literacy culture. Furthermore, it can serve as a model for other schools aiming to enhance their literacy programs and improve student engagement in reading activities.

Private vehicles are a frequently used mode of transportation because they are considered more practical. However, using private vehicles carries several risks, such as traffic accidents due to drivers losing focus on the road due to other activities, such as making calls on smartphones, drinking, or operating the radio. Approximately 90% of accidents are caused by human error. Convolutional Neural Network (CNN) is a type of neural network commonly used on image data. CNN is often used for image classification due to its high performance and accuracy. Therefore, this study aims to analyze the performance of CNN for the classification of distracted driving activities. The results show that the CNN model is able to effectively classify images of distracted driving activities, with an accuracy of approximately 99% across all datasets and across all input image size variations. Furthermore, the results of this study also show that differences in right-hand and left-hand drive datasets do not significantly affect model accuracy. Variations in input image size also do not significantly affect model accuracy, but do affect the training duration.

This study aims to determine and analyze service waiting times, identify the root causes of long queues, and develop a strategy to improve service performance at the 5361137-gas station (SPBU) at the Surabaya-Gresik Toll Rest Area. The research method used is a mixed-methods approach with an exploratory sequential design. This study combines quantitative analysis using Queuing Theory to measure system performance (arrival rates and service times) and descriptive qualitative analysis using a Fishbone Diagram. Data were collected through direct observation, interviews, and g-form techniques. The results indicate that the current queuing system performance is in a critical or severe condition, indicated by a server utilization rate of 0.94 to 1.02 during peak hours. The average time spent by vehicles in the system is 14.3 minutes, of which 9.6 minutes (67%) is spent waiting in the queue. Fishbone diagram analysis revealed that the root cause of the main problem lies in the complex interaction of factors: Machine factors (EDC signal failure and pump repair downtime), Human and Method factors (implementation of static shifts and reactive maintenance), and Environmental factors (narrow layouts that hinder large vehicle maneuvers). As a solution, this study formulated a hybrid improvement strategy that includes short-term business process engineering (the use of Floating Staff and lane segregation) and long-term investment in additional pumps to change the queuing model from Single Channel to Dual Channel. This strategy is expected to reduce the utility level to a safe zone below 0.80 with a target waiting time of 3–5 minutes.

In this paper, we study the use of linear programming for the performance enhancement of fortified queues systems in which resources are unreliable in terms of variation and a case study on Marjan Hospital is considered. The quality management maturity level in the hospital was measured by an integrated inspection tool; senior management was found to be concerned for quality of service improvement and implementation of modern technology systems. Strengths and weaknesses in that activity are shown, and it is evidenced the need of boosting the quality of service activities and promoting the coordination between the different areas. From the findings, the study recommends the use of linear programming technique,Orgers should as well enhance proper training and development and Acquisition of modern day technology for effective performance improvement and quality of service. The study demonstrates that the adoption of linear programming techniques could be a viable solution to optimize resource allocation and enhance performance in the hospital's queue systems. Additionally, the research underscores the importance of investing in staff training and development programs, as well as the acquisition of state-of-the-art technology, to further improve service delivery and ensure sustainable quality improvement in the long term.