Abstract:
Manual human resource management systems in small and medium-sized enterprises
(SMEs) present significant operational challenges, including attendance recording errors,
payroll calculation inefficiencies, and fraud vulnerabilities such as "buddy punching."
This thesis presents the development of SmartAttend Pro, an integrated IoT and AI-based
attendance management system designed to address these challenges at Regatta Property,
a representative SME facing typical HR management constraints.
The research methodology involved comprehensive analysis of existing manual
processes, evaluation of alternative solutions, and implementation of a cost-effective
integrated system utilizing ESP8266 microcontrollers, AS608 fingerprint scanners, and
machine learning algorithms. The system architecture incorporates biometric
authentication, real-time data synchronization, automated payroll processing, and
predictive analytics using TensorFlow LSTM neural networks.
Key findings demonstrate significant improvements in operational efficiency through the
elimination of manual attendance recording, reduction of payroll processing time from
2-3 days to automated real-time calculations, and implementation of streamlined approval
workflows for leave, permit, and overtime requests. The system successfully integrates
AI chatbot functionality using Google Dialogflow to reduce HR administrative burden
and provides comprehensive analytics for strategic decision-making.
The implementation results show exceptional cost-effectiveness with a one-time
investment of Rp 253,000 compared to cloud-based alternatives requiring ongoing
subscriptions of Rp 5-15 million. Qualitative testing with 95 potential users achieved an
overall satisfaction rating of 4.46/5.0 with 88.6% recommendation rate. The system
demonstrates robust security features including JWT authentication, PBKDF2-SHA256
password encryption, and fingerprint hash encryption.
SmartAttend Pro transforms traditional HR management through automation, reducing
human error, preventing attendance fraud, and providing intelligent insights for
workforce planning. The system's modular design enables scalability and customization
without vendor dependency, making it a sustainable solution for SMEs seeking digital
transformation in human resource management. This research contributes to the body of
knowledge on cost-effective IoT implementation in small business environments and
demonstrates the practical application of AI technologies in HR optimization.
