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This Author published in this journals
All Journal Journal of Embedded Systems, Security and Intelligent Systems Artificial Intelligence in Educational Decision Sciences Journal of Vocational, Informatics and Computer Education
Mushaf
Universitas Negeri Makassar
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences

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Computer Vision-Driven Classroom Analytics: Real-Time Attendance Verification and Student Focus Monitoring for Data-Informed Teaching Decisions Nurhikma; Aril; Mushaf; Muh. Yusril Anam
Artificial Intelligence in Educational Decision Sciences Vol 1 No 1 (2026): Artificial Intelligence in Educational Decision Sciences
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aieds.v1i1.7

Abstract

Purpose – Student attendance and learning activity monitoring are essential for ensuring instructional quality and academic accountability. However, conventional attendance methods remain inefficient, error-prone, and vulnerable to manipulation, while existing Computer Vision-based solutions often require high computational resources and focus on attendance or engagement separately. This study aims to develop an integrated, lightweight Computer Vision-based system for automatic student attendance recording and real-time focus monitoring suitable for resource-limited educational environments.Methods – This study employs a classical Computer Vision approach integrating Haar Cascade for face detection, Local Binary Patterns Histogram (LBPH) for face recognition, and rule-based eye detection for focus classification. The system automatically records attendance, tracks focus duration, and generates real-time digital reports. System performance was evaluated under controlled classroom conditions using accuracy, precision, recall, and F1-score.Findings – Experimental results demonstrate that the proposed system achieves high recognition reliability, with face detection and recognition accuracy reaching 100% in small-scale testing. The system operates efficiently with low latency and minimal computational requirements, while successfully monitoring multiple students simultaneously and generating structured attendance and focus duration reports in real time. Research limitations – The evaluation was conducted on a limited number of students under controlled conditions, which may restrict generalisability. Further testing in larger, more diverse classroom settings is required to validate system robustness.Originality – This study presents a unified and resource-efficient solution that integrates attendance validation and real-time focus monitoring within a single platform, offering practical value for schools seeking scalable and affordable learning analytics systems.
The Concern Over Brain Rot from Generative AI Use Among Preservice Teachers: A UTAUT Approach Ummul Khaeri Masna; Udin Sidik Sidin; Mushaf; Stephen Amukune
Journal of Vocational, Informatics and Computer Education Vol 4, No 1 (2026): March 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i1.347

Abstract

Purpose – The increasing use of generative AI on campus has raised concerns about a potential decline in students’ critical thinking skills. While the UTAUT theory is widely used to examine technology adoption, its relationship with the phenomenon of brain rot remains underexplored, particularly among preservice teachers. This study aims to analyze the factors associated with preservice teachers’ intention to use generative AI within the UTAUT framework, as well as to examine its association with tendencies toward brain rot.Method – A quantitative cross-sectional design was conducted with 243 preservice teachers from Universitas Negeri Makassar. Data were collected via a validated 30 item questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships between technology adoption constructs and brain rot tendencies.Findings – Social influence was the only significant predictor of behavioral intention to use AI (β = 0.269, p = 0.002). Behavioral intention, in turn, showed a strong positive association with brain rot tendencies (β = 0.817, p < 0.001), explaining 66.7% of the variance (R² = 0.667). Other UTAUT constructs, including performance expectancy and effort expectancy, were not significant predictors. However, given the cross-sectional design, these findings reflect statistical associations rather than causal relationships.Research Implication : Socially driven AI adoption is strongly linked to cognitive passivity, highlighting the need to extend UTAUT with cognitive risk factors and rethink how technology use impacts higher-order thinking.Conclusion – This study indicates that the adoption of AI among preservice teachers is associated with perceptions of declining cognitive abilities. These findings highlight the importance of promoting critical AI literacy and developing assessment approaches that emphasize deep cognitive engagement. Future research is recommended to employ longitudinal designs or incorporate control variables such as digital self-efficacy.
The Role of Enterprise Resource Planning in Improving Data Integration and Managerial Decision Making Ayu Hasnining; Mushaf
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 2 (2025): June 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i2.9247

Abstract

This study aims to examine the role of Enterprise Resource Planning (ERP) in improving the integration of operational data and managerial decisions at IFAH Group. The main problem raised is the non-optimal utilization of ERP as a strategic tool in supporting data-based decision making. Using a quantitative approach with a correlational design, 50 respondents were selected through purposive sampling. Data were collected through a Likert-scale closed questionnaire and analyzed with multiple linear regression using SPSS. Results show ERP has a significant effect on data integration and managerial decisions (R² = 0.574; p < 0.001), with data integration as a mediator. This study confirms the importance of ERP in the digital transformation of organizations. However, this research is limited to one case study and user perceptions, so further research with a multi-method approach or in different industry contexts is recommended
Development of a Web-Based Scheduling Information System with Agile Methodology Using Next.js and Supabase Mushaf; Muh Dimas Januardi Nur; Nurul Ilmi; Nurfadilah; Ahmad Khairul Shiddiq
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 2 (2025): June 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i2.9588

Abstract

This study analyzes the development and impact of FOKUS!, a web-based scheduling application designed to help individuals, particularly students and professionals, manage time and tasks effectively. The system was built using an Agile approach across three sprints, involving UI/UX design, backend setup with Next.js and Supabase, and implementation of task management and notification features. Results from White Box testing indicated 100% frontend code coverage, with stable component rendering and logic validation. Black Box testing confirmed that core features—registration, login, task CRUD, and reminders— functioned as intended. Feasibility studies showed that the system is viable technically, economically, and organizationally. Users appreciated the intuitive interface and real-time synchronization. The system is expected to positively impact productivity and time efficiency. Future improvements include enhancing backend testing, integrating external calendar services, and conducting user acceptance testing to ensure a better user experience.
Co-Authors Ahmad Khairul Shiddiq Aril Ayu Hasnining Muh Dimas Januardi Nur Muh. Yusril Anam Nurfadilah Nurhikma Nurul Ilmi Stephen Amukune Udin Sidik Sidin Ummul Khaeri Masna