Human Resource (HR) Performance Analysis Model in Higher Education Based on Multi-modal Data Using Machine Learning - Reni Utami; Ari Hidayatullah; Anita Ratnasari (Fakultas Teknik Universitas Muhammadiyah Bengkulu, 2025)
This research aimed to develop a machine learning-based model for human resource (HR) performance analysis in higher education institutions using a multi-modal approach, combining static data and text data analysis. For static data analysis, the random forest (RF) algorithm was employed to assess HR performance based on attributes such as years of service, training attended, and performance evaluations. The dataset for this experiment consisted of 250 data points, which were divided into 70% for training, 10% for validation, and 20% for testing. The experimental results with the RF model showed high accuracy in training (90.4%), although there was a performance drop during validation and testing, with accuracies of 85.7% and 82.5%, respectively. For the text data, which contained feedback with negative, positive, and neutral sentiments, the CNN-BiLSTM model achieved an accuracy of 92.6% in training, despite a decrease in validation (87.4%) and testing (84.4%) accuracies. The text dataset comprised 1,000 data points, divided into 70% for training, 10% for validation, and 20% for testing. The study recommends the application of a multi-model approach to assess HR performance using the RF algorithm for static data and the CNN-BiLSTM model for more complex data in future research.