<![CDATA[The increasing number of scientific publications in the computer science field poses challenges for automatic classification of article titles, which tend to be short, ambiguous, and contain domain-specific technical terms. This study aims to compare the performance of three machine learning algorithms—k-Nearest Neighbor (kNN), Naïve Bayes, and Random Forest—in classifying the titles of computer science journal articles into five main topics: AI & Machine Learning, UI/UX, IT Framework, IoT, and Cybersecurity. A dataset of 2,449 article titles was collected from the SINTA portal and underwent text preprocessing steps including tokenization, case normalization, stopword removal, and stemming, followed by numerical representation using TF-IDF weighting. Models were trained and tested using separate datasets and evaluated based on accuracy, precision, recall, and F1-score. The results show that kNN (k=51) achieved the highest accuracy of 89.61% with consistent precision, recall, and F1-score across all categories. Random Forest ranked second with 81.46% accuracy, showing strong performance in categories such as Cybersecurity and AI & Machine Learning, although it suffered a significant drop in recall for Cybersecurity. Naïve Bayes recorded the lowest accuracy (71.35%) but achieved the fastest execution time (12 seconds). These findings emphasize that algorithm selection should consider data characteristics, particularly for short-text classification such as article titles, and indicate that kNN is the most stable and accurate choice in this context.]]>
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