Development of M.E.G.A: A self-assessment expert system to improve English correspondence skills for job-seeking university students - Megawaty, Merti; Sulistiyo, Urip; Wachyuni, Sri; Sofyan (Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) Universitas PGRI Mahadewa Indonesia, 2025)
This study highlights the low English writing and speaking skills of students of the Information Systems Study Program at Nurdin Hamzah University. To address this challenge, a rule-based expert system application named M.E.G.A for Job Seekers was developed using the ADDIE model approach (Analysis, Design, Development, Implementation, and Evaluation). This application functions as a self-assessment tool to improve students' professional communication readiness, especially in compiling CVs, job application letters, and interview preparation. The sampling technique used was purposive sampling, with subjects consisting of language experts, instructional media experts, and educational technology experts to validate the product. Meanwhile, 100 second-semester students of the Information Systems Study Program at Nurdin Hamzah University were used as the sample for testing the practicality, attractiveness, and effectiveness of the application. This study used quantitative data from questionnaires and tests, as well as qualitative data from open-ended responses by experts and students. The quantitative data were analyzed descriptively, while the qualitative data were validated through triangulation and content analysis to support the development of the application. The results showed high scores in the aspects of functionality, attractiveness of appearance, and improvement of learning outcomes. Pre-test and post-test analysis showed significant improvements, while statistical analysis supported the pedagogical reliability of this system. The application has proven effective in supporting independent learning that is relevant to the needs of the world of work and has the potential to be adopted by other study programs. This study contributes to the development of digital learning through the integration of AI-based expert systems for the development of 21st-century skills.