The Utilization of Naive Bayes and C.45 in Predicting The Timeliness of Students’ Graduation

Agung Wibowo, Danny Manongga, Hindriyanto Dwi Purnomo

Abstract


An assessment of the success of a college is if the student's graduation rate is on time and high every year. The timeliness of students' graduation can be influenced by several factors. This study aims to determine the profile of the students who graduated both on time and not on time given a certain graduation predicate set by the institution and to know the factors influencing students’ gradution. The model used in this study using the NBC to determine the graduation pattern and the Decision tree to determine the influencing factors. In calculating the NBC algorithm using rapidminer, it was found that the profiles of students who graduated on time and late with the predicate of less satisfactory, satisfactory, very satisfactory and cumlaude. In the Desicion Tree calculation, the highest gain values are obtained in the IPK3, IPS1 and IPK2 attributes. This research needs to be developed further by increasing the number of attributes and data, and it is necessary to make a system to determine the accuracy of students’ graduation from the patterns that have been produced so that it can help universities to increase the level of students’ graduation every year.


Keywords


Information Systems, Data Mining, UNW.

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References


Nurliana Nasution, K. D. (2015). Evaluasi Kinerja Akademik Mahasiswa Menggunakan. Jurnal Teknologi Informasi & Komunikasi Digital Zone, Volume 6, Nomor 2,, 1-11. [2] Romadhona, A., Suprapedi, & Himawan, H. (2017). Prediksi Kelulusan Mahasiswa Tepat Waktu Berdasarkan Usia, Jenis Kelamin, Dan Indeks Prestasi Menggunakan Algoritma Decision Tree . Jurnal Teknologi Informasi, 13(1), 69-83. [3] Astuti, I. P. (2017). Prediksi Ketepatan Waktu Kelulusan Dengan Algoritma Data Mining C4.5. Fountain of Informatics, II, No. 2, 41-45. [4] Andri, Kunang, Y. N., & Murniati, S. (2013). Implementasi Teknik Data Mining Untuk Memprediksi Tingkat Kelulusan Mahasiswa Pada Universitas Bina Darma Palembang. Seminar Nasional Informatika . Yogyakarta. [5] Erdogan, S. M. (2005). A Data Mining Application In A Student Database. Journal Of Aeronautics And Space Technologies, Volume 2 Number 2: 53-57. [6] Sidik, M., Rasminto, H., Iriani, A., & Manongga, D. (2017). Implementasi Data Mining Untuk Prediksi Kelulusan Menggunakan Metode Klasifikasi Naive Bayes. Jurnal Teknologi Informasi dan Komunikasi, 8(2), 13-20. [7] Daniel, L. T. (2006). Data Mining Methods dan Models. John Wiley & Sons, Inc Publication. [8] Cahyaningtyas, C., Purnomo, H. D., & Kristianto, B. (2019). The Use of Naive Bayes for Broiler Digestive Tract Disease Detection. JITCE (Journal of Information Technology and Computer Engineering), 03, 1-7. [9] Kusrini, &. E. (2009). Algoritma Data Mining. Yogyakarta: Andi Publishing. [10] Saputra, M. F., widiyaningtyas, T., & Wibawa, A. P. (2018). Illiteracy Classification Using K Means - Naive Bayes Algorithm. nternational Journal On Informatics Visualization, 2 No 3, 153. [11] Lungu, I., & Pirjan, A. (2010). Research Issues Concerning Algorithms Used For Optimizing The Data Mining Process. IDEAS. [12] Mujib Ridwan, H. S. (2013). Penerapan Data Mining Untuk Evaluasi Kinerja Akademik Mahasiswa Menggunakan Algoritma Naive Bayes Classifier. Jurnal EECCIS, 59-64. [13] Garcia, E. P. (2011). Model Prediction of Academic Performance for First Year Students. IEEE Computer Society. [14] Yusuf Sulistyo Nugroho, S. (2014). Klasifikasi Masa Studi Mahasiswa Fakultas Komunikasi Dan Informatika Universitas Muhammadiyah Surakarta Menggunakan Algoritma C4.5. KomuniTi, 84-91. [15] Zahroh, F. (2016). Pengaruh Gender Terhadap Motivasi Memilih Sekolah dan Prestasi Belajar. Journal of Accounting and Business Education .




DOI: https://doi.org/10.15294/sji.v7i1.24241

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