Analisis Sentimen terhadap Ulasan Pembeli Smartphone IOS di Platform Shopee Mengunakan Metode Naïve Bayes - Ali Akbar Rismayadi; Serly Agustin (Lembaga Pengembangan Kinerja Dosen, 2025)
The development of digital technology has significantly shifted consumer behavior, especially in online shopping through e-commerce platforms such as Shopee. One of the most sought-after products is smartphones, including the iPhone, which is well-known for its quality and durability. This study aims to analyze user sentiment toward iOS smartphone products from the iBox store on Shopee using the Naïve Bayes method. This method is chosen for its simplicity in handling text classification and relatively high accuracy. The dataset used consists of customer reviews that have been preprocessed and manually labeled. The evaluation results show that the Naïve Bayes algorithm achieves an accuracy of 82.6%, with the best performance on the positive sentiment class (precision 0.95, recall 0.85, f1-score 0.90). However, the model performs poorly on the negative sentiment class, with a precision of only 0.33 and an f1-score of 0.44. These findings indicate that while the model is highly effective at identifying positive sentiment, further improvement is needed to enhance its ability to detect negative sentiment. This research is expected to serve as a reference for businesses such as iBox in understanding customer opinions automatically and making more informed strategic decisions.