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Quiet quitting refers to a phenomenon in which employees do not formally resign from their jobs but intentionally limit their work efforts to the minimum required. This study aims to adapt the Quiet Quitting Scale developed by Galanis et al. (2023) into the Indonesian context. A quantitative approach was employed involving 100 respondents drawn from both private and government sectors. The adaptation process included forward translation by three linguists, synthesis of the translated versions, and cultural adjustment to ensure contextual relevance. Data were analyzed using Confirmatory Factor Analysis (CFA) based on a three-factor model consisting of detachment, lack of initiative, and lack of motivation. The results indicate that the Indonesian version of the Quiet Quitting Scale demonstrates acceptable construct validity across the proposed factors. However, several items still require refinement, suggesting that further development and validation with larger and more diverse samples are necessary to strengthen the scale’s psychometric properties.

Video steganography faces fundamental challenges in balancing embedding capacity, imperceptibility, and robustness, where conventional Least Significant Bit (LSB) methods often produce visual artifacts such as flickering. To address this, this research proposes an advanced method named Adaptive Multi-layer LSB, which dynamically adjusts the number of embedded bits in each pixel based on a multi-factor analysis of the video's spatial and temporal characteristics. This adaptation mechanism is evaluated through three primary criteria: brightness level, local texture complexity, and inter-frame motion stability. Quantitative evaluation using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Frame Difference Stability Index (FDSI) metrics demonstrates that the proposed method achieves high visual quality, with an average PSNR of 42.15 dB and SSIM of 0.985. These results significantly outperform the non-adaptive approach, which only recorded a PSNR of 38.5 dB. More importantly, the FDSI value of this method (1.25) is much lower compared to the non-adaptive approach (3.40), demonstrating its superiority in maintaining temporal stability. Thus, this approach provides a significant contribution to enhancing security and quality in video steganography practices. Abstract: Video steganography faces fundamental challenges in balancing embedding capacity, imperceptibility, and robustness, where conventional Least Significant Bit (LSB) methods often produce visual artifacts such as flickering. To address this, this research proposes an advanced method named Adaptive Multi-layer LSB, which dynamically adjusts the number of embedded bits in each pixel based on a multi-factor analysis of the video's spatial and temporal characteristics. This adaptation mechanism is evaluated through three primary criteria: brightness level, local texture complexity, and inter-frame motion stability. Quantitative evaluation using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Frame Difference Stability Index (FDSI) metrics demonstrates that the proposed method achieves high visual quality, with an average PSNR of 42.15 dB and SSIM of 0.985. These results significantly outperform the non-adaptive approach, which only recorded a PSNR of 38.5 dB. More importantly, the FDSI value of this method (1.25) is much lower compared to the non-adaptive approach (3.40), demonstrating its superiority in maintaining temporal stability. Thus, this approach provides a significant contribution to enhancing security and quality in video steganography practices.

Social support is defined as an individual’s perception of the availability and presence of assistance derived from three primary sources, namely family, friends, and individuals considered significant others. This concept emphasizes the subjective evaluation of the quantity and quality of support perceived by individuals, rather than the actual support provided. This study aims to examine the construct validity of the Multidimensional Scale of Perceived Social Support (MSPSS) developed by Zimet et al. (1988), which has been adapted into the Indonesian language. The MSPSS instrument is designed to measure the extent to which individuals perceive social support received from family, friends, and significant others in their lives. Research participants were selected using a purposive sampling technique, with inclusion criteria consisting of active university students aged between 18 and 27 years. A total of 279 students participated in this study. The research employed a quantitative approach, with data collected through an online questionnaire administered via Google Forms and distributed through various social media platforms. Data analysis was conducted using Confirmatory Factor Analysis (CFA) with the assistance of Jamovi software.