Analisis Perbandingan Naïve Bayes dan Neural Network dalam Klasifikasi Minat Masyarakat pada Kursus Komputer - Fitria, Nabila Syah; Suryadi, Sudi; Nasution, Fitri Aini (Building of Informatics, Technology and Science (BITS), 2025)
In the digital era, the use of technology in education is growing, especially in improving people's digital literacy through computer courses. To analyze people's interest in courses, a data mining-based approach is needed that can process large amounts of data and identify certain patterns. Naïve Bayes and Neural Network are two widely used classification methods, where Naïve Bayes works based on independent probabilities between features, while Neural Network uses artificial neural networks to capture more complex patterns. This study aims to compare the two methods in classifying people's interest in LKP Ibay Komputer and evaluate the accuracy of each model. The classification results show that both methods produce the same predictions, namely 53 data are categorized as interested and 20 data as not interested. The model accuracy reaches 100%, indicating very high classification performance. Although these results seem ideal, perfect accuracy like this often raises questions regarding the validity and robustness of the model in real-world scenarios. Factors such as relatively small dataset sizes, overly structured data patterns, or lack of variation in training data can cause results that appear too good. Therefore, it is important to conduct additional evaluations such as cross-validation or testing on different datasets to ensure that the model does not experience overfitting and remains reliable in broader predictions. With these results, it can be concluded that both Naïve Bayes and Neural Networks have optimal performance in classifying people's interest in computer courses, but the choice of method can be adjusted according to needs, where Naïve Bayes excels in computational efficiency, while Neural Networks are more adaptive to more complex data.