Klasifikasi Algoritma Naïve Bayes dan SVM Berbasis PSO Dalam Memprediksi Spam Email Pada Hotline-Sapto - Merio Hengki; Mochamad Wahyudi (LPPM Universitas Bina Sarana Informatika, 2020)
Accreditation can be interpreted as an effort by the government to standardize and guarantee the quality of college alumni so that the quality of verification between universities is not too varied and in accordance with work needs. SAPTO or Online Higher Education Accreditation System is a system organized by BAN-PT for the online higher education accreditation process. Developed to improve the efficiency and quality of higher education accreditation processes. At Sapto, the University acts as an entity that submits accreditation proposals for both the Higher Education Accreditation and the Study Program Accreditation. BAN-PT has approved a complaint service related to technical issues in SAPTO that can be addressed to the hotline-sapto email account. BAN-PT has a question and answer service through the e-mail hotline sapto which can be used by universities to facilitate related to the accreditation process with sapto. Submission and questions about the accreditation process with Sapto are still responded more quickly by staff who work as public relations BAN-PT. This relates to direct question and answer questions and also a lot of time spent reading or sending spam messages as well as unwanted irrelevant questions. Email technology is also used a lot not for positive purposes so as to benefit from spam email. On this occasion the research that will be conducted is the classification of spam emails from the hotline-sapto account and preprocessing and the calculation of its accuracy, AUC with various data mining classification methods, including the Naïve Bayes algorithm, Support Vector Machine (SVM), this method is used to predict spam emails with that is the purpose of the algorithm chosen is the most accurate algorithm that can predict spam emails. From the test results obtained by the calculation of the SVM method with PSO get an accuracy value of 85.25% with AUC of 0.892