Pengembangan Chatbot Pelayanan Akademik Berbasis Arsitektur Hybrid Laravel dan Flask dengan Pattern Matching Regular Expression

Abstrak

Academic information services demand high precision and near-instant responsiveness. While chatbot adoption in higher education is growing, existing approaches face a technical dilemma: deep learning-based Natural Language Processing (NLP) models often require significant computational resources and are prone to "hallucinations" (inaccurate answers), whereas traditional rule-based systems are rigid and difficult to maintain. This study addresses these limitations by proposing a Smart Chatbot utilizing a Hybrid Architecture that decouples the user interface (Laravel-based) from the processing logic (Python Flask-based) via REST API. The core intelligence relies on an Optimized Hierarchical Regular Expression (Regex) method Deterministicigned to provide deterministic and accurate responses for administrative queries without the computational overhead of complex AI models. Performance testing using Black Box methodology on 50 query samples demonstrated a 92% accuracy rate with an exceptionally low average latency of 0.7 seconds. These results confirm that a hybrid approach combined with deterministic pattern matching offers a superior balance of speed, accuracy, and resource efficiency for academic environments compared to resource-intensive NLP models