Implementation of Multi-Agent with a Finite State Machine Approach in the Attendance Issue Reporting System at PT. XYZ
Abstract
Operational issues in digital attendance systems such as
recognition errors, network disruptions, or forgotten credentials
are still frequently reported through informal channels, resulting
in unstructured records that are hard to trace and prone to
duplication or fraud. This study proposes a conversational,
report-driven attendance incident system that combines a
multi-agent approach with a finite state machine (FSM) to keep
dialogues structured, preserve conversational context, and ensure
data completeness. The system performs automatic information
extraction from user inputs using a large language model, and
conducts evidence pre-validation via optical character
recognition and multimodal classification prior to human
verification.
The research methods include organizational needs analysis,
design of the data model and interaction flow, formulation of FSM
rules to govern dialogue stages, and integration of conversational
intelligence, information extraction, and evidence validation
components. Evaluation follows User Acceptance Testing (UAT)
grounded in realistic business workflows at PT. XYZ, focusing on
three aspects: (1) time efficiency compared to the manual
reporting process, (2) data consistency and completeness by
comparing chatbot variants with and without FSM control, and
(3) reduction of initial manual workload for administrators during
verification.
Experimental results show the system accelerates reporting by
≥90% relative to the manual method, yields higher completeness
with the FSM-controlled chatbot (approximately 96% complete
versus ~70% without FSM), and achieves an ≈70% reduction in
early administrative intervention. Evidence validation using text
recognition and multimodal classification attains ~90–95%
accuracy for common cases, effectively serving as a pre-screen
before human review. These findings indicate that a multi-agent
approach with FSM control can improve the speed, traceability,
and reliability of attendance incident reporting.
