Implementasi Klasifikasi Teks Menggunakan Algoritma Naïve Bayes pada Sistem Pengarsipan Surat Masuk - Nur Afifah; Ita Arfyanti; Yunita (Forum Kerjasama Pendidikan Tinggi (FKPT), 2026)
Incoming mail management is an important part of higher education administration because it is related to document storage, classification, and retrieval in a fast and accurate manner. However, the incoming mail archiving process at the General Administration and Finance Bureau (BAUK) of STMIK Widya Cipta Dharma is still carried out manually, making document grouping inefficient, slowing down archive retrieval, and potentially causing inconsistencies in determining mail categories. This study aims to implement the Naïve Bayes algorithm in a web-based incoming mail archiving system to support automatic mail classification. The system was developed using Laravel and Livewire as the main application, and Flask as the classification service. The dataset consisted of 79 incoming mail documents divided into four categories: requests, invitations, notifications, and reports. The preprocessing stage included case folding, text cleaning, tokenizing, stopword removal, and stemming. The results show that the system is able to automatically classify incoming mail and present detailed classification processes through training reports and classification results. Based on testing on 16 incoming mail documents, the model achieved an accuracy of 75.00%, an average precision of 63.89%, and an average recall of 72.22%. These results indicate that the Naïve Bayes algorithm is sufficiently effective in supporting a more structured and efficient incoming mail archiving process.