PENERAPAN ALGORITMA NAIVE BAYES PADA KLASIFIKASI KELAYAKAN PENURUNAN UKT BERDASARKAN KONDISI EKONOMI MAHASISWA - Wanga, Fahmi Beddu Saleh; Pamungkas , Anang Ahmad Ajie; Lampeng, Nirwanda G.; Hunawa, Nazri Pri Andini Z.; Pobela, Rizaldie; Poha, Wahid Asmidin (Program Vokasi Universitas Negeri Gorontalo, 2023)
Higher education in Indonesia can promote competent human resources. A single tuition fee (STF) constitutes a payment scheme based on students’ economic conditions and significantly contributes to access to quality education. However, many students are confronting financial difficulties which decrease their ability to pay STF, demanding a more reliable and accurate mechanism for assessing STF reduction assessment. This study proposes the application of the Naïve Bayes algorithm to assess STF reduction reliability based on students’ economic conditions. We selected the algorithm because of its quick and efficient data classification by considering probabilistic distribution. The system implementation deployed the Python programming language, which supported the development of the learning machine application and integration with the information system. Results pointed out that the Naïve Bayes model came with an accuracy of 98.00% in predicting with Rapidminer and an accuracy of 96.67% with Python. It presented its effectiveness in predicting STF reduction reliability. Through factors which affected STF-related decisions significantly, the system could elevate accuracy in decision-making related to STF reduction reliability based on students’ economic conditions. As a result, universities could provide more target-effective support for students in need.