Analisis Sentimen Orang Tua Murid Baru Terhadap SMPN 40 Samarinda pada SPMB 2025 Menggunakan Algoritma Naïve Bayes - Resifa Ananta Putra; Heny Pratiwi; Ahmad Abul Khair (Universitas Methodist Indonesia, 2025)
The New Student Admission Selection (SPMB) plays an essential role in ensuring equal educational access in Indonesia. However, during SPMB 2025 at SMPN 40 Samarinda, many candidates living nearby did not choose the school as their first preference, suggesting that perceptions and school image significantly influenced their choices. This study aims to analyze new student parents sentiments toward SMPN 40 Samarinda using the Naïve Bayes algorithm combined with the Term Frequency–Inverse Document Frequency (TF-IDF) technique. Data were collected from 42 respondents and categorized into positive, neutral, and negative sentiments. The model achieved an accuracy of 86%, precision of 56%, and recall of 63%, showing that Naïve Bayes performs effectively on limited data, though less sensitive to minority classes. The analysis revealed that most parents expressed positive perceptions, indicating growing trust that SMPN 40 Samarinda can support students’ character development. These findings emphasize the importance of strengthening school image and service quality while highlighting the potential of machine learning–based sentiment analysis as a data-driven approach to understanding educational perceptions.