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.

The development of competent human resources (HR) is a top priority for improving the quality of life in society. In education, teachers' ability to enhance their skills is key to successful learning. However, many teachers struggle to adapt teaching methods to the demands of the digital era, especially in utilizing Artificial Intelligence (AI) technology. AI offers innovative solutions such as personalized learning and adaptive platforms that enhance the effectiveness of teaching and learning processes. This study examines the implementation of AI-based learning models and teacher training programs in Muhammadiyah schools in Semarang. Using the Training of Trainer (ToT) approach, the program equips teachers with skills such as using adaptive learning tools and AI-based evaluation systems. The study employs a mixed-methods approach involving practical training and result evaluation. The findings reveal improved digital literacy and confidence among teachers in using AI for interactive learning. In conclusion, AI has great potential to transform education, but its success depends on comprehensive, ethical, and sustainable strategies. The ToT program serves as a strategic step to empower teachers to face educational challenges in the digital era.

The need for student attendance information on campus is the main factor of success academic process. Often there are validation and identification errors in student attendance process (human error or manipulation process) and the slowness of the information obtained, because attendance is recorded manually in every class. This has an impact on the success of the academic process directly and society at large. Due to the importance of attendance information, various research has offered a system in the student attendance process, with various technology. One of the popular technologies and believed to validate and identify well are fingerprints. Therefore, this paper proposes the introduction of absenteeism system on campus by utilizing fingerprint technology that is integrated with PHP programming and MySQL database. This research uses Fingerprint FP Personnel Revo V4.0 as a means of identifying student data when absent. The system will display student attendance quickly so that the attendance recapitulation process can be obtained in a short time. This system is applied to the Informatics study program at the Faculty of Engineering, University of Muhammadiyah Semarang

Chronic kidney failure is the failure of kidney function in maintaining metabolism and fluid and electrolyte balance in the body. Chronic kidney disease initially does not show significant symptoms and signs but can develop rapidly into kidney failure. Kidney disease can be prevented and treated if known earlier. One way to find out chronic kidney failure is to detect using data mining.
Iterative Dichotomiser 3 (ID3) algorithm is one of the classification methods and is a type of method that can map or separate two or more different classes. Based on the measurement of performance classification of 80% of training data from 400 data used, it shows that the accuracy value reached 96.25%. It can be concluded that the ID3 Algorithm method is feasible to be used in research predictions for chronic kidney failure.