Sentiment Analysis of NU Online Applications Using Artificial Neural Network - Lusia, Dwi Ayu; Anuraga, Gangga; Rahman, Fathur (Southeast Asian Journal of Islamic Education, 2024)
The NU Online app on the Playstore serves the needs of Muslims, especially those in Islamic boarding schools, by providing information and services. Its success is gauged not just by the number of downloads or popularity but by the quality of user interactions and how well it meets user needs. Sentiment analysis of user reviews provides deeper insights into these aspects. This research focused on finding words influencing sentiment from NU online and producing the best performance of artificial neural networks. This study collected user reviews from the NU Online app between February 9, 2021, and May 31, 2024, totalling 12613 reviews. After preprocessing, 8546 reviews remained. Using the Indonesian Sentiment Lexicon (INSET), 66% of the reviews showed positive sentiment, 21% were neutral, and 13% were negative. The words "aplikasi" (application) and "nya" (its) appeared in the top three across all sentiment classes, while "fitur" (feature) was common in both positive and negative sentiments. For neutral sentiments, "nan" was frequently mentioned. The data were split into training and testing sets in an 80:20 ratio, preserving the proportions of each sentiment class. Sentiment analysis was performed using a neural network, with input neurons ranging from the top 10 words from each sentiment class to all words. Accuracy improved as more words were used, peaking at 0.95 for the top 1690 words, compared to 0.71 for the top 10 words. The findings highlight the importance of using a comprehensive set of words to train the ANN. Including more words significantly enhances the model's performance, indicating that a richer vocabulary captures sentiment nuances better.