Data Mining untuk Memprediksi Animo Masyarakat terhadap Proses Penerimaan Peserta Didik Baru - Berek, Anggelina Bete; Isa, Sani Muhamad (LPPM STIKOM Tunas Bangsa, 2025)
Data Mining techniques can now be implemented in all aspects of society because of development in technology. Data Mining can be used in education. There is the possibility of being employed for educational purposes to predict achievements. The involvement of the community is one of the factors that influence the quantity of students at the school. The research project is using Data Mining to predict the factors that impact social engagement. The demographic information used in this study came from the parents of potential candidates. The techniques used are Decision tree, Naïve bayes, and Support Vector Machine. Their accuracy scores are evaluated by a confusion matrix. The results of this study are below: Decision tree 80.16%, Naïve Baye 79.94%, and Support Vector Machine 86.02%. Based on the comparison results, it can be concluded that the highest accuracy is achieved by using the Support Vector Machine algorithm, while the factor that affects public sentiment is ayah penghasilan.