SEGMENTASI PROBABILISTIK CALON MAHASISWA BERBASIS INDOBERT, GAUSSIAN MIXTURE MODEL, DAN LLM DI ITSNU PEKALONGAN - Mujibul Hakim; Eri Zuliarso (LPPM Universitas Abdurrab, 2026)
Private higher education institutions face challenges in understanding shifting prospective-student characteristics driven by socio-economic dynamics, including the COVID-19 pandemic. ITSNU Pekalongan recorded a non-linear enrollment pattern in 2019–2024 (from 146 to a peak of 680; N = 2,362). This study aims to analyze probabilistic segmentation, measure cross-period structural breaks, build an LLM-based analysis automation system, and validate it through expert judgement. A Research and Development approach follows a ten-stage CRISP-DM framework: IndoBERT (768 dimensions), PCA, Gaussian Mixture Model (GMM) with diag covariance and k-means++ init, Adjusted Rand Index (ARI), and a Hybrid Cognitive Pipeline (local Llama 3.2 and OpenRouter cloud). The results show that 4 of 5 inter-period transitions are structural breaks (ARI < 0.30), identical-entity cosine similarity of IndoBERT embeddings averages 0.9234 (range 0.8932–0.9426), and expert validation reaches 4.0/5.0 for a 2025 projection of 592 applicants. The IndoBERT–GMM–LLM integration yields a replicable recruitment decision-support system.