Recommendation of Vocational Major Recommendation Using Artificial Neural Networks with ADAM Optimization - Fawwaz Dzakwan; Muhammad Hikmat Fathur Rahman; Mardi Hardjianto (Building of Informatics, Technology and Science (BITS), 2026)
This study proposes an Artificial Neural Network (ANN)-based recommendation framework for vocational major selection using student academic-feature profiles and ADAM optimization. The dataset consisted of 9,984 student records from West Java, derived from the school proposal recapitulation linked to DAPODIK. The predictive task was formulated as a six-class classification problem covering major categories in vocational education. Data preprocessing included data checking, target encoding, feature standardization, and train-test splitting. The ANN model used two hidden layers with ReLU activation and a softmax output layer, and was trained for 50 epochs using the ADAM optimizer. To enhance practical usability, predicted class probabilities were combined with major-specific qualification thresholds to enable the system to produce both primary and alternative recommendations. Experimental results showed that the model achieved 82% accuracy, an average precision of 0.88, a recall of 0.82, and an AUC of 0.8748, indicating useful discriminative performance for vocational major recommendation. The study provides a clearer distinction among the recommendation basis, predictive model, and operational qualification screening in educational decision support. However, the system still relies mainly on academic features and has not yet been benchmarked against alternative classifiers. Future work should incorporate interest and aptitude variables, class-wise error analysis, and comparative baseline evaluation.