This paper examines euthanasia from ethical, theological, legal, and human rights perspectives, focusing specifically on the Christian faith. Euthanasia, defined as the intentional termination of life to avoid physical suffering, raises significant moral and legal dilemmas. This study applies a qualitative method through literature review to explore expert opinions, Indonesian legal provisions, and theological foundations based on the Bible. The findings indicate that euthanasia contradicts the core principles of Christian ethics, Indonesian positive law, and human rights values that uphold the right to life. From a Christian worldview, life is a divine gift and should not be ended by human will. Suffering is understood as part of a spiritual journey that carries redemptive meaning. Furthermore, euthanasia presents complex implications not only for the individual undergoing the act but also for families, religious communities, and healthcare professionals. In Christian ethics, life and death are matters of divine sovereignty, and human agency in ending life is considered a violation of God's authority. The legal stance in Indonesia, as reflected in national laws and regulations, clearly prohibits any form of euthanasia, emphasizing the protection of human life as a constitutional right. From a human rights viewpoint, although autonomy is important, it must be balanced with the principle of preserving life. This study suggests that interdisciplinary dialogue involving theology, law, bioethics, and pastoral care is urgently needed to build awareness and offer compassionate responses to end-of-life issues. Churches, educators, and policymakers are encouraged to develop ethical guidelines and pastoral counseling approaches rooted in biblical truth and legal integrity. A wise and faithful response to the problem of euthanasia requires not only empathy but also a clear commitment to uphold the sanctity of life.

Pedada leaves (Sonneratia caseolaris), a type of mangrove plant that grows in tropical and subtropical coastal areas, have high ecological and pharmacological value. In addition to functioning as coastal protection from abrasion and seawater intrusion, this plant is also known to contain various secondary metabolite compounds such as flavonoids, tannins, saponins, and alkaloids. These compounds have been widely reported to have important biological activities, one of which is as an antimicrobial agent. This study aims to identify the effectiveness of pedada leaf ethanol extract in inhibiting the growth of pathogenic microorganisms, especially bacteria that cause infections. The extraction process was carried out using 96% ethanol solvent through the maceration method, namely soaking the material in the solvent for a certain time to optimally obtain active compounds. The obtained extract was then tested for its antimicrobial activity using the disc diffusion method against test bacteria, both Gram-positive and Gram-negative. The inhibition zone formed around the disc was measured as an indicator of the antibacterial effectiveness of the extract. The results showed that pedada leaf extract was able to produce inhibition zones with varying diameters depending on the concentration used. The higher the extract concentration, the larger the diameter of the inhibition zone formed. This indicates a positive relationship between extract concentration and its antibacterial strength. This activity is believed to originate from the ability of compounds such as flavonoids and tannins to damage bacterial cell walls or disrupt microbial metabolic processes. This study provides initial evidence that pedada leaves have potential as an environmentally friendly and sustainable source of natural antibacterials. This potential is highly relevant in efforts to develop alternative antibacterial materials based on local plants, especially amidst increasing bacterial resistance to synthetic antibiotics.

The Paiton Steam Power Plant (PLTU) is one of the main sources of electrical energy in East Java, which plays a vital role in maintaining a sustainable electricity supply. The reliability of generator units is a key element in maintaining stable energy distribution. However, the high frequency of sudden generator failures poses serious challenges, such as increased downtime and increased maintenance costs. To address these challenges, this study aims to design a generator maintenance prediction model based on the Naive Bayes algorithm with a predictive maintenance approach. This study uses historical maintenance data and key sensor parameters such as temperature, oil pressure, and vibration as input. The data is analyzed through several stages, namely data preprocessing, selection of relevant features, and labeling generator conditions into three categories: Normal, Warning, and Critical. The Naive Bayes model is trained to classify the data probabilistically to generate predictions of future generator conditions. Model evaluation using accuracy metrics and a confusion matrix shows that the model successfully achieved an accuracy rate of 89% and was able to provide early warnings of potential failures up to 3 days before failure occurs. The implementation of this system is expected to support the shift in maintenance strategies from reactive and scheduled systems to data-driven predictive systems. Implementing failure predictions allows the technical team at the Paiton PLTU to conduct planned maintenance, avoid sudden disruptions, and extend equipment lifespan. Thus, this model has the potential to reduce operational downtime by up to 25%, while providing significant savings in operational and logistics costs. This research also shows that integrating machine learning technology into energy facility management can improve the efficiency and resilience of the overall electric power system.

Bullying is a recurring issue and has become a widespread concern, particularly among children and adolescents in school environments. Schools, which should ideally be safe spaces for learning, building friendships, and experiencing personal growth, often turn into places of fear and psychological distress for some students. This is largely due to the frequent violation of human rights (HR) that often goes unaddressed or is underestimated. Bullying can take many forms, including verbal, psychological, physical abuse, and increasingly, cyberbullying. Each of these forms can cause serious harm not only to the victims—who may suffer from emotional and mental trauma—but also to the perpetrators, who may experience long-term psychological consequences and develop deviant or aggressive behavior patterns. In Indonesia, bullying remains a significant issue and a major concern in the education sector. Nearly every school has faced incidents of bullying in one form or another. Cyberbullying, in particular, is harder to monitor and control since it often occurs outside the school’s jurisdiction and is carried out through social media platforms. Efforts to tackle bullying must go beyond merely protecting and supporting the victims. A comprehensive approach is necessary, one that also focuses on preventive education. Students, teachers, and parents must be consistently educated on the negative impacts of bullying, the importance of empathy, and a deeper understanding of human rights. Moreover, strict and clear sanctions must be imposed on perpetrators as a form of legal enforcement and deterrence. With a holistic strategy that involves all elements of the school and the wider community, it is hoped that educational institutions can truly become safe, inclusive, and supportive environments. Such conditions are essential for fostering not only academic growth but also the psychological well-being and character development of the nation's children.

Fatigue is a critical issue in labour-intensive small industries, especially in traditional food production such as tofu manufacturing. This study aims to develop a fatigue classification model using a decision tree algorithm by integrating subjective assessments of the work system through the Macroergonomic Organizational Questionnaire Survey (MOQS) and objective physiological indicators, specifically Cardiovascular Load (CVL). The research was conducted in a tofu home industry located in Kalisari Village, Banyumas, Indonesia. Primary data were collected from 10 workers through MOQS questionnaires and heart rate measurements taken at rest and during work. CVL values were calculated and used as labels for classification into three categories: low, moderate, and high fatigue. Meanwhile, MOQS dimension scores (organization, job, personal, environment, and technology) were transformed into interval data and used as classification features. A decision tree model was built using the CART algorithm and visualized for interpretability. The results show that all workers experienced at least moderate fatigue, with 20% categorized as high fatigue. The decision tree revealed that the dimensions of organizational and personal factors were the most influential in predicting fatigue levels. The model provides a practical and interpretable tool to support decision-making in scheduling, workload balancing, and ergonomic interventions. This study demonstrates a novel approach to combining macroergonomic assessments and physiological data with machine learning for practical fatigue risk management in small-scale food production environments.

This study aims to identify and measure the influence of customer relationships, customer satisfaction, and brand image on customer loyalty at Ryandini’s Sweet Chicken Claw business, located in Sei Sijenggi Village, Hamlet III, Perbaungan District, Serdang Bedagai Regency. A quantitative research method was employed, with data collected through observation, documentation, and questionnaires. The questionnaires used a Likert scale as a measurement tool to assess respondents’ opinions accurately. Data analysis was conducted using several stages, including validity and reliability tests, classical assumption testing, and multiple linear regression analysis, processed through SPSS version 22. These methods were used to ensure the validity of the instrument and the consistency of the findings. The research findings demonstrate that customer relationships have a significant and positive effect on customer loyalty. Customers who feel connected and valued by the business are more likely to remain loyal. Likewise, customer satisfaction—reflecting the extent to which customers' expectations are met or exceeded—has a strong and positive influence on loyalty. In addition, the brand image of the business, which encompasses customer perceptions and reputation of the product, also significantly influences loyalty in a positive direction. Furthermore, the simultaneous analysis of customer relationships, customer satisfaction, and brand image shows that all three variables jointly have a significant and positive impact on customer loyalty. This means that enhancing these three factors can strengthen customer retention and long-term commitment. These results provide valuable insights for small business owners in the food and beverage industry. Focusing on building strong relationships with customers, consistently delivering satisfaction, and maintaining a positive brand image are essential strategies to increase loyalty. For Ryandini’s Sweet Chicken Claw and similar businesses, these findings offer practical guidance for developing customer-centered marketing strategies in an increasingly competitive market.

This study aims to examine the mediating role of Organizational Citizenship Behavior (OCB) in the relationship between Perceived Organizational Support (POS), Perceived Organizational Commitment (POC), and employee performance in a state-owned enterprise in Indonesia. In the context of growing public expectations for improved service quality, organizations are challenged to enhance employee performance not only in formal roles but also in discretionary behaviors. Based on Social Exchange Theory (SET), this research proposes that POS and POC can affect performance both directly and indirectly through OCB. Data were collected through a census of 138 permanent employees at PT Jasa Raharja, East Java Branch. The analysis employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the hypothesized relationships. The findings show that POS has a significant positive influence on both OCB and employee performance. On the other hand, POC significantly influences OCB but does not have a direct impact on performance. The results also reveal that OCB plays a partial mediating role in the relationship between POS and performance, while it acts as a full mediator in the relationship between POC and performance. These findings emphasize the importance of fostering OCB within the organization to optimize the impact of support and commitment on employee performance. Encouraging voluntary, extra-role behaviors can bridge the gap between how employees perceive organizational treatment and how they perform. The study suggests practical implications for human resource management in public sector institutions. Policies should focus on building a supportive and engaging work climate that enhances affective commitment and acknowledges employee contributions beyond formal duties. Doing so will not only improve individual performance but also contribute to broader organizational effectiveness in a competitive and service-oriented environment.

Employee shift scheduling in the hospitality industry remains a critical yet complex task due to fluctuating operational demands, fairness requirements, and labour regulations. Many hotels still rely on manual scheduling methods, which are time-consuming and prone to biases, particularly in ensuring fair workload distribution across employees. Despite numerous studies on workforce scheduling, limited attention has been given to integer linear programming (ILP) models that address gender-based restrictions and operational fairness simultaneously in real-world hotel contexts, especially in developing regions such as Central Java. This study proposes an Integer Linear Programming (ILP) model to generate optimal shift schedules for hotel staff over a 31-day planning horizon. The model incorporates operational constraints, including one shift per day, gender-based restrictions (which prevent female staff from working night shifts), availability, minimum staffing levels, and fairness in workload distribution. Key parameters and binary decision variables were defined to ensure compliance with the hotel's specific requirements. Empirical data were collected from a hotel in Central Java involving 20 employees, and the model was implemented using Python with a Gurobi solver. The ILP model successfully generated optimal schedules in under 10 seconds, significantly outperforming the manual method, which required over 4 hours. While the manual schedule resulted in an imbalance where some employees worked over 27 days and others only 22, the ILP approach enforced a strict maximum of 26 working days for all staff. Furthermore, the fairness index (FI) improved from 19.2% in the manual method to 0% in the ILP-generated schedule, indicating complete equity in workload allocation. The proposed ILP model demonstrates its effectiveness in improving scheduling fairness, operational efficiency, and compliance with labour policies. This work not only addresses a critical research gap in hospitality scheduling practices in Indonesia but also offers a replicable framework for other labour-intensive service sectors. Future research may explore multi-objective extensions incorporating employee preferences, satisfaction, and dynamic demand fluctuations.

The purpose of this paper is to examine the role of Green Intellectual Capital (GIC) in the development of waste-to-energy (WtE) technologies, which is of special relevance for the incineration plants in the Gulf Cooperation Council (GCC) countries. Although previous studies have focused primarily on technological and infrastructural aspects, this paper investigates the contribution of intangible assets such as green human, structural, and relational capital towards the performance and sustainability of WtE systems. Adopting a mixed-method approach, the results show that Green Structural Capital has the first place in supporting operational efficiency, followed by Green Human Capital and Green Relational Capital. The statistical analysis based on Structural Equation Modeling (SEM) shows that all GIC dimensions are positively related to plant performance, with institutionalized knowledge and systems as the most significant enablers of innovation. Qualitative findings also highlight constraints such as lack of training, unhandy knowledge systems, and poor public-private interaction. The findings recommend policy suggestions for promoting GIC assimilation within environmental infrastructure and provide a region-based theoretical model connecting knowledge-based resources and sustainable waste management practices. The study emphasizes the importance of integrating intellectual capital into the strategic decision-making process of waste-to-energy plants to enhance their operational effectiveness and long-term sustainability. Additionally, the research underlines that fostering a knowledge-sharing culture and enhancing collaboration between various stakeholders, including governmental bodies, private firms, and research institutions, is critical for the success of green initiatives. Training programs aimed at developing green human capital and improving public-private partnerships are essential to overcoming the current barriers to innovation in the WtE sector. This study has practical and scholarly implications in unifying the focus from just technology to the facilitating role of human and intellectual capital in green transformation, further bridging the gap between environmental sustainability and technological advancements in waste management.

This study was conducted at Bio Live Healthy Home, located in Sei Bamban District, Serdang Bedagai Regency. The primary objective of this research is to determine how lifestyle, reference groups, and consumer attitudes influence the decision to use services offered by the institution. In a rapidly growing health and wellness industry, understanding consumer behavior is essential to improve service quality and increase customer engagement. To achieve the research objectives, the study employed a survey method with a quantitative approach. The target population consisted of service users, and a sample of 92 respondents was selected. Data collection was carried out through a combination of observation, interviews, documentation, and the distribution of structured questionnaires. The questionnaire was designed to capture variables related to lifestyle, reference groups, attitudes, and service usage decisions. Before conducting the main analysis, the data were tested for validity and reliability to ensure that the instrument accurately measured what it intended to measure. Furthermore, classical assumption tests, including normality, multicollinearity, and heteroscedasticity tests, were conducted to confirm the feasibility of using a multiple linear regression model. The analysis was performed using the Statistical Package for the Social Sciences (SPSS) software. The results of the study revealed that lifestyle, reference groups, and attitudes each had a significant and positive influence on the decision to use services at Bio Live Healthy Home. This indicates that individuals with health-conscious lifestyles, influenced by their social environment and holding positive attitudes toward wellness services, are more likely to engage with the services offered. Moreover, these three factors collectively contributed to a meaningful explanation of the variation in consumer decisions. In conclusion, the findings highlight the importance for service providers to consider psychological and social factors when designing marketing strategies.