ANALISIS PENGGUNAAN ALGORITMA KLASIFIKASI DALAM PREDIKSI KELULUSAN MENGGUNAKAN ORANGE DATA MINING

  • Dinda Safitri Universitas Buana Perjuangan Karawang
  • Shofa Shofiah Hilabi Universitas Buana Perjuangan Karawang
  • Fitria Nurapriani Universitas Buana Perjuangan Karawang

Abstract

Timely graduation in Higher Education is the expectation of students. One of the requirements to graduate in their studies, students must take the final stage, namely completing the final project or thesis. But the time of graduation is not always able to predict when college students will graduate. Many factors cause student graduation such as GPA, credits, employment status and so on. Seeing this, it is important to have a method that can predict student graduation, but some universities do not have their own method to be able to estimate student graduation whether the student can graduate on time or not. To overcome this, a model is needed to be able to predict student graduation. In this study, an analysis of 3 methods was carried out, namely Naive Bayes, K-NN and Neural Network. The purpose of this study is to find out which method is more appropriate to use in predicting graduation. In this study, a comparison was also made between the three methods, and the best method was obtained, namely the K-NN method with an accuracy value of 89%.

Keywords: Graduated, Clasification, Naive Bayes, K-NN, Neural Network

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Published
2023-01-10
How to Cite
[1]
D. Safitri, S. Hilabi, and F. Nurapriani, “ANALISIS PENGGUNAAN ALGORITMA KLASIFIKASI DALAM PREDIKSI KELULUSAN MENGGUNAKAN ORANGE DATA MINING”, rabit, vol. 8, no. 1, pp. 75-81, Jan. 2023.
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Articles
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