Sistem Pendukung Keputusan Pemilihan Minat Skripsi Berbasis Kesiapan AI Menggunakan Metode SAW - Reza Ilham Ananditya; Arief Jananto (PPPM Institut Teknologi Pagar Alam, 2026)
Selecting a thesis interest area requires a structured assessment of student readiness, AI technology utilization, and personal interest. This study develops a web-based decision support system for Information Systems students at Universitas Stikubank. AI is measured as the C2 evaluation criterion rather than used as the computational algorithm, while the Simple Additive Weighting (SAW) method performs the entire ranking process. The researcher proposed the criterion weights and verified them through interviews with the thesis supervisor and Head of the Information Systems Study Program: student readiness 0.40, AI technology utilization 0.35, and student interest 0.25. The evaluation involved 40 students from the 2022 and 2023 cohorts. Validity testing retained six C1 items and seven C2 items, with Cronbach’s alpha values of 0.700 and 0.654. The recommendations were Artificial Intelligence & Machine Learning for 12 students (30.0%), Data Science & Analytics for 9 students (22.5%), Management Information Systems for 8 students (20.0%), Web Development for 6 students (15.0%), and Mobile Development for 5 students (12.5%). System outputs agreed 100% with manual calculations and all 18 functional scenarios worked properly. The system supports transparent academic guidance, while future work should develop readiness indicators that are specific to each alternative.