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The Importance Increasing Attendance Efficiency Accuracy with Presence System in Era Industrial Revolution 4.0 Tri Hartono; Bintang Nandana Henry; Sirje Nurm; Lukita Pasha; Dwi Julianingsih
International Journal of Cyber and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.168

Employee attendance management systems have become a major focus of the Industrial Revolution 4.0 era due to their significant role in increasing organizational productivity and performance. This study demonstrates the importance of the SmartPLS methodology in analyzing the impact of IoT based attendance technology and big data analytics on the efficiency and accuracy of employee attendance. Both the assessments reviewed show that the use of IoT based attendance technology and the implementation of big data analytics systems have a significant positive impact on the efficiency and accuracy of employee attendance. IoT based attendance technology enables real-time attendance data collection with high accuracy, while big data analytics enables organizations to derive valuable insights from the large volume of collected attendance data. These findings provide a better understanding of the contribution of technology in increasing organizational productivity and performance in today digital age. This study provides valuable insights for business professionals and academics to develop adaptive and effective attendance management strategies. Using IoT based attendance technology and big data analytics, organizations can improve operational efficiency, increase payroll accuracy, and optimize overall human resource utilization. Furthermore, the study also highlights the importance of adapting and innovating in the face of technological developments. By incorporating knowledge of the latest technology and industry trends, organizations can continuously enhance their attendance management strategies to remain relevant and competitive in the ever changing business environment.

The Importance Increasing Attendance Efficiency Accuracy with Presence System in Era Industrial Revolution 4.0 Tri Hartono; Bintang Nandana Henry; Sirje Nurm; Lukita Pasha; Dwi Julianingsih
International Journal of Cyber and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.168

Employee attendance management systems have become a major focus of the Industrial Revolution 4.0 era due to their significant role in increasing organizational productivity and performance. This study demonstrates the importance of the SmartPLS methodology in analyzing the impact of IoT based attendance technology and big data analytics on the efficiency and accuracy of employee attendance. Both the assessments reviewed show that the use of IoT based attendance technology and the implementation of big data analytics systems have a significant positive impact on the efficiency and accuracy of employee attendance. IoT based attendance technology enables real-time attendance data collection with high accuracy, while big data analytics enables organizations to derive valuable insights from the large volume of collected attendance data. These findings provide a better understanding of the contribution of technology in increasing organizational productivity and performance in today digital age. This study provides valuable insights for business professionals and academics to develop adaptive and effective attendance management strategies. Using IoT based attendance technology and big data analytics, organizations can improve operational efficiency, increase payroll accuracy, and optimize overall human resource utilization. Furthermore, the study also highlights the importance of adapting and innovating in the face of technological developments. By incorporating knowledge of the latest technology and industry trends, organizations can continuously enhance their attendance management strategies to remain relevant and competitive in the ever changing business environment.

Advanced Cyber Threat Detection: Big Data-Driven AI Solutions in Complex Networks Agung Rizky; Muhammad Zaki Firli; Nur Aulia Lindzani; Sipah Audiah; Lukita Pasha
CORISINTA Vol 1 No 2 (2024): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v1i2.42

In the rapidly evolving digital landscape, cybersecurity has become increasingly critical, especially within complex network environments. This research presents the development of a cyber threat detection system that leverages Artificial Intelligence (AI) and Big Data analytics to enhance accuracy and speed in identifying and responding to cyber threats. The system was evaluated through rigorous testing, demonstrating a high detection accuracy of 95\% for malware and unauthorized access attempts, along with an impressive detection speed of 2 seconds on average for most threats. Additionally, the system exhibited strong scalability, maintaining optimal performance even with increasing network complexity. These findings underscore the system's robustness and practical applicability in real-world scenarios. However, further refinement is suggested to improve anomaly detection and reduce response times for more complex threats. This study contributes valuable insights into the integration of AI and Big Data in cybersecurity, providing a scalable and effective solution for protecting critical network infrastructures.

Vision-Based Pattern Recognition Models for Intelligent Human Robot Interaction in Smart Spaces Muhamad Faizal Fazri; Konita Lutfiyah; Lukita Pasha; Lily Maria
International Transactions on Artificial Intelligence Vol. 4 No. 2 (2026): May
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i2.1101

The rapid expansion of smart spaces has increased the need for robotic systems capable of interpreting visual cues, recognizing human behavior, and responding safely in real time. However, existing vision-based models often struggle with occlusion, lighting variation, latency constraints, and limited contextual understanding in dynamic human-centered environments. This study develops a hybrid vision-based pattern recognition framework that integrates Convolutional Neural Networks (CNNs), Transformer-based attention mechanisms, multi-scale feature fusion, supervised learning, and reinforcement learning. The model is trained and validated using publicly available human–robot interaction datasets and simulated smart space scenarios involving gesture recognition, object detection, activity recognition, and intention prediction. The objective is to enhance intelligent human–robot interaction by improving visual perception accuracy, contextual interpretation, adaptive decision-making, and real-time responsiveness in smart environments. The proposed framework achieves stronger performance than baseline CNN-only and Vision Transformer models, with improved accuracy in gesture recognition, object detection, activity recognition, and intention prediction while maintaining low-latency inference suitable for real-time robotic interaction. The model also demonstrates better adaptability under dynamic lighting, occlusion, and multi-person interaction scenarios. This study concludes that combining CNN-based local feature extraction, Transformer-based global attention, and reinforcement learning-based policy optimization provides a reliable, adaptive, and context-aware framework for intelligent robotic systems. The findings support safer and more efficient human–robot collaboration in healthcare, smart homes, collaborative workplaces, and smart city environments.

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