Development of a Web and Mobile Rule-Based Expert System for Vocational School Academic Supervision - Yudi Yansyah Al Basri; Bibin Rubini; Rais Hidayat (Sakura Digital Nusantara, 2026)
Purpose: This study develops a hybrid digital solution named BASRI (Best Academic Supervision with Recommendation using Intelligence) to resolve administrative inefficiencies and delayed feedback in the conventional academic supervision framework at SMK PGRI 1 Kota Bogor. Methods: Methodologically, an algorithmic Research and Development (R&D) pathway framed within the ADDIE framework was executed based on a preliminary needs assessment of 34 educators. Findings: Product validation confirmed the application is highly feasible, scoring 90.0% for material substance and 93.8% for media engineering. Subsequent field testing demonstrated exceptional operational practicality, achieving satisfaction ratings of 95.0% from the principal and 95.6% from 20 participating teachers. Furthermore, a Paired Sample T-Test revealed significant structural improvements in teaching outputs, with Learning Effectiveness increasing from 4.30 to 4.65, and Management Efficiency advancing from 4.32 to 4.73 (Sig. 2-tailed = 0.000 < 0.05). Research Implications: In conclusion, integrating an automated expert logic matrix within the supervisory workflow successfully shifts institutional administrative culture from passive, retrospective reporting to a responsive, real-time mentorship dynamic. Originality: Unlike conventional digital forms that merely digitize paperwork, BASRI introduces a novel Rule-Based AI expert system that automatically analyzes observation scores to diagnose pedagogical weaknesses and instantly delivers personalized professional development video recommendations to teachers. This system provides vocational institutions with an adaptable architectural blueprint to sustain micro-level continuous professional development and high-scientific advancement.