Classification of the Village Development Index in East Java Province 2024 Using Backpropagation Neural Network and Naïve Bayes
Klasifikasi Indeks Desa Membangun Provinsi Jawa Timur 2024 Menggunakan Jaringan Saraf Tiruan Backpropagation dan Naïve Bayes
DOI:
https://doi.org/10.21070/ups.9592Keywords:
Village Development Index (IDM), Classification, Naïve Bayes, Backpropagation, Machine LearningAbstract
The Village Development Index (Indeks Desa Membangun/IDM) is an important instrument for measuring village development. This study compares two classification methods, Backpropagation and Naïve Bayes, in determining village status using the 2024 IDM dataset of East Java. The evaluation was carried out using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results indicate that Naïve Bayes is more stable, achieving an accuracy of 0.80 on the training set and slightly increasing to 0.81 on the test set, which demonstrates good generalization ability. In contrast, Backpropagation achieved 0.7802 accuracy on the training set but dropped to 0.7540 on the test set, showing less consistent performance, particularly across several categories. In conclusion, simpler methods such as Naïve Bayes can outperform more complex models in regional datasets that are relatively small with linear features, while Backpropagation is more suitable for national-scale data with larger size and more complex non-linear patterns.
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