Penggunaan Metode Naïve Bayes untuk Klasifikasi Topik Tweet Bidang dan Non-Bidang Rektorat Telkom University pada Akun Telyufess - Mukhammad Hafiz Bima Ibrahim; Anisa Dzulkarnain; Alqis Rausanfita (Forum Kerjasama Pendidikan Tinggi (FKPT), 2025)
Student complaints submitted through the Telyufess account on the social media platform X have not been optimally utilized as input for evaluating campus services at Telkom University. This study aims to classify tweets from the Telyufess account into two categories: domain-related (linked to official university units such as academics, finance, and campus services) and non-domain (general complaints unrelated to specific units). The main issue addressed is the need for an automated mapping system of student complaints to support campus service evaluations. The classification method used is Naïve Bayes, involving manual labeling by the researcher and assistant annotators (with inter-rater validation), text preprocessing (normalization using a standard dictionary and the Sastrawi library, removal of special characters, stop word filtering based on Indonesian language lists augmented with the unique term “telyu!”), tokenization, stemming, TF-IDF weighting, and dataset splitting in ratios of 65:35, 70:30, 80:20, and 90:10. A total of 1,090 tweets were collected between January 1, 2023 and January 1, 2025 using the Tweet Harvest API, based on criteria including complaints, opinions, and suggestions (retweets were excluded). The highest accuracy was achieved at 87.27% with a 90:10 split, followed by 81.74% (80:20), 78.35% (70:30), and 76.96% (65:35). Although the model showed signs of overfitting on training data (accuracy >99%), the results demonstrate that Naïve Bayes is effective for automated tweet classification and contributes to the use of social media as a data source for evaluating campus services.