Penerapan Data Mining Untuk Mengukur Kepuasan Mahasiswa Terhadap Pembelajaran dengan Menggunakan Algoritma Naïve Bayes - Agung Triayudi; Gatot Soepriyono (Journal of Computer System and Informatics (JoSYC), 2022)
Education is very important for life, with education can help in improving Human Resources (HR). Human Resources (HR) in universities are students. Increased competence in students carried out in higher education is carried out by learning. The process carried out in learning is very influential on the results obtained from learning both competencies and abilities possessed by students. Based on this, it is necessary to improve learning in higher education in order to support good results. Measuring the level of student satisfaction with learning can measure the extent to which the learning process has been carried out. The process of measuring the level of student satisfaction with learning is done first by collecting data. After collecting data, the data processing process is carried out to get the expected results. Errors in the data processing process, the results obtained are also not in accordance with the objectives carried out. Therefore, to solve the problem it is necessary to do it with the right process by using a separate method or technique where the method is data mining. Data mining is a method or technique used for data processing. The data processing process carried out in data mining is carried out on large data. The Naïve Bayes algorithm is an algorithm that is included in the classification of data mining techniques. Where the process in the nave Bayes algorithm is very dependent on the grouping process carried out on each attribute and also the target class of each object. The results of the study show that the probability value of PUAS is 0.034108116 and the probability value of NOT SATISFIED is 0. This indicates that the result of decision making is SATISFIED.