Analisis Algoritma Naive Bayes Untuk Prediksi Kepuasan Layanan Akademik Berbasis Data Multibahasa - Oktafiani, Dewi; Putra, Tommy Dwi; Kusumastuti, Rajnaparamitha (Algoritme Jurnal Mahasiswa Teknik Informatika, 2025)
The quality of academic services greatly influences student satisfaction. This study predicts student satisfaction with academic services using a Naïve Bayes algorithm based on multilingual data. Data from 213 students across three departments at STMIK AMIKOM Surakarta cover five key service aspects. Student comments were processed through text preprocessing and TF-IDF weighting, then tested on both Indonesian and English-translated texts. The results showed a significant difference: the Indonesian model achieved 67.44% accuracy, 0.68 precision, 0.65 recall, and 0.66 F1-score, while the English version improved to 83.72% accuracy, 0.84 precision, 0.82 recall, and 0.83 F1-score. Statistical tests confirmed this difference as significant. The findings highlight that English NLP tools are more mature and provide empirical contributions to improving the quality of academic services in higher education.