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This study aims to analyze the influence of the work environment on employee productivity at PT Harmoni Reka Cipta. The research employs a descriptive c method using Spradley’s approach, which includes place, actor, and situation. Informants were selected based on inclusion and exclusion criteria, resulting in one key informant and four main informants relevant to the data requirements. Data were collected through in-depth interviews and field observations. The research findings, based on NVIVO analysis visualized through a word cloud, indicate that the most dominant aspect in discussions of the work environment is workplace facilities, followed by work relations, work atmosphere, and operational constraints. Interviews revealed that lighting and coworker relationships are generally in good condition; however, challenges remain, including limited workspace, insufficient facilities, and suboptimal air circulation, especially in the production area. These conditions affect employee comfort, focus, and work effectiveness. NVIVO results related to productivity show that quantity, quality, and timeliness are influenced by a work environment that is not yet fully supportive. Some delays occur due to high production intensity, heavy workloads, and inadequate facilities. This study concludes that improving facilities, reorganizing workspace layouts, and enhancing physical comfort are necessary to support optimal employee productivity

Digital transformation has driven organizations to adopt the metaverse as a 3D virtual workspace that enables employee interaction through avatars, thereby introducing new challenges in managing organizational identity, work culture, and employee loyalty. This study aims to analyze the role of human resource leadership in establishing organizational identity, transforming work culture, and sustaining employee loyalty within a metaverse-based work environment. The study employs an integrative literature review approach by systematically collecting and synthesizing relevant scholarly sources from reputable publications within the last five years. The findings indicate that organizational identity in the metaverse is constructed through avatar representation and virtual environment design, while work culture undergoes a transformation toward flexible digital interaction patterns that still require reinforcement of organizational values. Furthermore, employee loyalty is influenced by the quality of virtual experiences and the effectiveness of leadership communication. In this context, digital-based HR leadership emerges as a critical factor in fostering employee engagement and trust in a non-physical interaction setting. This study contributes conceptually by integrating leadership, identity, culture, and loyalty within the metaverse framework, and provides practical implications for organizations in designing adaptive human resource management strategies in response to the advancement of immersive technologies.

In addition to being programmatically successful in lowering TFR and slowing population growth rates, the Long-Term Contraceptive technique (MKJP) is an affordable technique of preventing conception. This study's objective was to examine variables associated with long-term contraceptive (MKJP) selection at the Gandasuli Community Health Center. The study design is cross-sectional and the research methodology is quantitative. The investigation was carried out in the workspace of the Gandasuli Community Health Center. 300 women of reproductive age made up the study's population, and a straightforward random selection procedure was used to choose 45 participants. A questionnaire was used to gather the data, which were then subjected to univariate and bivariate analysis using the Chi-Square statistical test. Age and MKJP choice did not correlate, according to the data (p- value = 0.09 > 0.05). The choice of MKJP was correlated with education (p-value = 0.003 < 0.05). The choice of MKJP was correlated with occupation (p-value = 0.003 < 0.05). The choice of MKJP and the husband's support are related, with a p value of 0.004 < 0.05. Parity and MKJP selection are related, with a p value of 0.003 < 0.05. Knowledge and MKJP selection are related, with a p-value of 0.003 < 0.05.

The role of the Director of Photography (DoP) is fundamental in translating narrative concepts into visual language within audio-visual productions. This study examines the implementation of cinematographic techniques applied by the DoP in producing the human interest video feature "Porter: Ketidakpastian yang Dikerjakan" (Porter: Uncertainty at Work). The feature documents the daily lives of porters at Bandung Station, shedding light on the occupational uncertainty they face as informal sector workers. The research employs a qualitative descriptive method with a production-based approach, analyzing four key cinematographic elements: type of shot (long shot, medium shot, close up), camera angle (eye level, high angle, low angle), camera movement (panning, tilting, tracking shot, static shot), and framing (rule of thirds, balanced composition). Findings indicate that each technique was applied with deliberate intention to reinforce the narrative, emotional depth, and thematic meaning of the work. Long shots established the station environment as a dynamic, unpredictable workspace; medium shots captured the psychological nuance of waiting and uncertainty; close ups revealed intimate emotional detail; and varied camera angles constructed the power dynamics between porters and their environment. Camera movements supported visual continuity and audience engagement, while framing principles ensured compositional coherence throughout. The study contributes practical insights into the role of cinematography in non-fiction storytelling, particularly in conveying the lived realities of marginalized workers.

The increasing adoption of collaborative robots in modern manufacturing environments requires reliable perception systems that can ensure both safety and operational efficiency during human–robot collaboration. This study proposes a CNN-based real-time computer vision system for object and human detection in shared robotic workspaces. The research focuses on developing and evaluating a single-stage deep learning detection model optimized for real-time performance while maintaining high detection accuracy. The proposed methodology includes dataset preparation, model training using transfer learning, real-time system implementation, and comprehensive performance evaluation. Experimental results demonstrate that the developed system achieves high detection accuracy, as reflected by strong precision, recall, and mean Average Precision (mAP) values, while maintaining low inference latency suitable for real-time operation. The system consistently operates above real-time frame-rate thresholds, ensuring timely perception updates required for safety-related decision-making in collaborative robotic environments. Graphical and quantitative analyses further confirm the stability of inference performance under dynamic interaction scenarios involving human movement and multiple objects. Compared with existing approaches, the proposed system provides a balanced trade-off between accuracy and computational efficiency, making it practical for deployment in safety-aware human–robot collaboration scenarios. Overall, the findings indicate that CNN-based real-time object detection systems can effectively support perception and situational awareness in collaborative robotics, contributing to safer and more efficient industrial automation.

The increasing demand for electrical energy, particularly in offices and commercial buildings, has made energy efficiency a critical aspect of sustainable development. Among various building components, lighting systems are recognized as one of the major consumers of energy. This study investigates the potential for energy savings through the adoption of a smart lighting system incorporating IoT-based sensors, motion detectors, and dimming controls. Employing a quantitative descriptive approach, the research was conducted at the workspace of Indie Light, comparing energy consumption before and after the implementation of the system. Data were collected using direct observation, light and power meters, and real-time monitoring devices to ensure accurate measurement. The results demonstrate that smart lighting systems can substantially reduce energy use without compromising lighting quality or comfort. By integrating intelligent sensors and adaptive control algorithms, the system not only optimizes energy efficiency but also aligns with national policies on energy conservation, supporting broader environmental sustainability efforts. These findings suggest that smart lighting solutions can play a significant role in promoting energy-efficient practices in commercial spaces while contributing to sustainable development goals.