A manual attendance system was previously used to manage the attendance list of each employee. However, this application causes attendance fraud. Therefore, the attendance system was changed into a digital form to reduce attendance fraud and improve discipline. This qualitative study was conducted through the phenomenological method by examining the implementation of the Hadirr.com digital attendance system in a 72-branch retail network in Banten-Bogor. Data in this study was collected through in-depth interviews with 45 informants (HRD managers, branch supervisors, and employees) and participatory observation for 6 months. The findings revealed three main themes, namely the absenteeism fraud patterns that developed before and after implementation, changes in work culture related to attendance and discipline post-implementation, and social interaction patterns formed due to implementation. This study provides a conceptual framework on the determinants of a successful digital time attendance system in a multi-site retail environment.

This qualitative literature review explores the realm of histogram-based image forensics for recaptured image detection, addressing the challenges posed by advancements in display technology and the subsequent need for robust forensic techniques. The research methodology involves a systematic approach, including defined research objectives, thorough literature search, data extraction, thematic analysis, and ethical considerations. The focal point is the proposed method utilizing Local Ternary Count (LTC) histograms normalized from residue maps, demonstrating exceptional performance across various databases. The methodology involves residue map calculation, LTC histogram extraction, and experiments showcasing the method's efficiency in both single and mixed databases. The discussion emphasizes emerging frontiers in recaptured image forensics, presenting innovative algorithms categorized by the medium used during the recapture process. The shift towards deep learning methods is noted, with a focus on a proposed algorithm for detecting images recaptured from LCD screens based on quality-aware features and histogram features. The RID field has witnessed advancements, with a detailed overview of methods categorically addressing recapture from LCD screens. Ethical considerations are integrated into the discussion, and the conclusion emphasizes the need for constant adaptation, innovation, and collaboration in the fight against evolving manipulation techniques. Looking ahead, the fusion of features, standardized datasets, and advanced deep learning architectures are identified as key elements for future research in ensuring image authenticity