Application of Convolutional Neural Network Student Reviews for Lecture Facilities at Stella Maris University Sumba - Emilia Kuba; Friden Elefri Neno; Karolus Wulla Rato (novamindpress, 2026)
The development of artificial intelligence technology, particularly in the field of deep learning, has opened up new opportunities in text-based data analysis, including student reviews of lecture facilities. This study aims to apply the Convolutional Neural Network (CNN) method in conducting sentiment analysis of student reviews of lecture facilities at Stella Maris University Sumba. Through this approach, it is hoped that the system can automatically and accurately classify student opinions into positive, negative, or neutral categories. The research data was obtained from surveys and student comments on various internal campus platforms. The data processing involved data preprocessing stages such as text cleaning, tokenization, stopword removal, and word embedding using the Word2Vec method. The CNN model was then built with an architecture involving an embedding layer, convolutional layer, max pooling, and fully connected layer to produce the final prediction. The test results show that the CNN model is capable of achieving a high level of accuracy in identifying sentiment polarity, with an average accuracy value of 90.2%. This performance proves that CNN is effective in extracting semantic features from unstructured student review texts. Analysis of the classification results also provides important insights into aspects of campus facilities that received positive and negative responses, such as classroom quality, internet network, and learning environment comfort. These findings can be used as a basis for universities in making strategic policies for the continuous improvement of lecture facilities. Thus, the application of CNN in student review analysis has been proven to support the evaluation and decision-making processes in data-driven academic environments.