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Dadang Aribowo
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KOMPARASI ALGORITMA UNTUK KLASIFIKASI HEREGISTRASI CALON MAHASISWA Dadang Aribowo; Aris Ekyanto Heru Setiadi; Ivandari Ivandari
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2018: SEMINAR NASIONAL PENDIDIKAN SAINS DAN TEKNOLOGI
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Students are the most valuable asset in a private university (PTS). Because most of PTS's revenues and operating costs are obtained from students. The number of students who do registration clearly will be a breath of fresh air for the institution. In the last 5 years, around 20% of STMIK Widya Pratama students did not register. Early knowledge of prospective students who might not register will be a reference for the institution to take action to maintain students. The recording of student data that is neatly arranged can be used by management to analyze the characteristics and causes of students not registering. Data mining can process past data into new information or knowledge. In data mining, there is one major function, namely the classification that processes training data to calculate new data / data testing. Methods or algorithms that can be used in the classification process are numerous with various characteristics of each. Some of the best classification algorithms include naive bayes, knn, and C4.5. The results showed that the three algorithms, namely, naive bayes and the C45 decission tree can be used to classify prospective student registrations. The accuracy of the C45 decission tree algorithm is the best, 80.72% followed by the algorithm with an accuracy rate of 80.46%. While the accuracy of naive bayes is the lowest with 74.49%. Keywords: KNN, Naive Bayes, Decission Tree C45
Co-Authors Aris Ekyanto Heru Setiadi Ivandari Ivandari