Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Metode Naive Bayes di Program Studi Teknik Informatika UHAMKA

Authors

  • Dwi Anugrah Putra Universitas Muhammadiyah Prof. Dr. Hamka
  • Mia Kamayani Universitas Muhammadiyah Prof. Dr. Hamka

Keywords:

Naïve Bayes, prediksi kelulusan, k-Fold Cross-Validation

Abstract

Based on observations and existing data in the UHAMKA Informatics Engineering Study Program, the number of students who do not graduate on time (8 semesters) in each generation will cause an accumulation of the number of students, lack of classrooms, and lack of parking space. One of the ways to increase student graduation on time is to predict from begin which students have the potential to didn’t graduate on time, so that preventive action can be taken by the study program management or faculty. Prediction can be done using data mining by utilizing data from students who have graduated. The data mining method used in this study is Naive Bayes using gender attributes, achievement index from semester one to semester four and semester one to semester four credits. The Naive Bayes algorithm will be made several models and the highest accuracy value will be sought from the model. The model evaluation uses K-fold Cross Validation and the prediction results will be used by the academic supervisor to evaluate students whose prediction results are unsatisfactory. The model with the best results is the 3rd model with an accuracy rate of 80.19%, a recall of 80.26%, precision 92.75% and F-Measure 86.05% which will be used for implementation in the student graduation prediction application.

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Author Biographies

Dwi Anugrah Putra, Universitas Muhammadiyah Prof. Dr. Hamka

Department of Informatics Engineering
Faculty of Engineering

Mia Kamayani, Universitas Muhammadiyah Prof. Dr. Hamka

Department of Informatics Engineering
Faculty of Engineering

References

E. P. Rohmawan, “Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Metode Decision Tree Dan Artificial Neural Network,” 2013.

R. Yanto and R. Khoiriah, “Implementasi Data Mining dengan Metode Algoritma Apriori dalam Menentukan Pola Pembelian Obat,” Creat. Inf. Technol. J., vol. 2, no. 2, pp. 102–113, 2015.

S. Salmu and A. Solichin, “Prediksi Tingkat Kelulusan Mahasiswa Tepat Waktu Menggunakan Naïve Bayes : Studi Kasus UIN Syarif Hidayatullah Jakarta Prediction of Timeliness Graduation of Students Using Naïve Bayes : A Case Study at Islamic State University Syarif Hidayatullah Jakarta,” no. April, pp. 701–709, 2017.

S. Syarli and A. A. Muin, “Metode Naive Bayes Untuk Prediksi Kelulusan (Studi Kasus: Data Mahasiswa Baru Perguruan Tinggi),” J. Ilmu Komput., vol. 2, no. 1, Sep. 2018.

H. Widayu, S. D. Nasution, N. Silalahi, and M. Mesran, “DATA MINING UNTUK MEMPREDIKSI JENIS TRANSAKSI NASABAH PADA KOPERASI SIMPAN PINJAM DENGAN ALGORITMA C4.5,” J. MEDIA Inform. BUDIDARMA, vol. 1, no. 2, Jun. 2017.

A. Y. Saputra and Y. Primadasa, “Penerapan Teknik Klasifikasi Untuk Prediksi Kelulusan Mahasiswa Menggunakan Algoritma K-Nearest Neighbor,” Techno.Com, vol. 17, no. 4, pp. 395–403, 2019.

F. Tempola, M. Muhammad, and A. Khairan, “Perbandingan Klasifikasi Antara Knn Dan Naive Bayes Pada Penentuan Status Gunung Berapi Dengan K-Fold Cross Validation Comparison of Classification Between Knn and Naive Bayes At the Determination of the Volcanic Status With K-Fold Cross,” J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 5, pp. 577–584, 2018.

J. Junaedy and A. Munir, “Rancang Bangun Sistem Pengelolaan Data Kuliah Kerja Lapang Plus Memanfaatkan Framework Codeigniter Dengan Menggunakan Metode Waterfall,” Ilk. J. Ilm., vol. 9, no. 2, pp. 203–210, 2017.

D. Dahri, F. Agus, and D. M. Khairina, “Metode Naive Bayes Untuk Penentuan Penerima Beasiswa Bidikmisi Universitas Mulawarman,” Inform. Mulawarman J. Ilm. Ilmu Komput., vol. 11, no. 2, p. 29, 2016.

A. R. C and Y. Lukito, “Deteksi Komentar Spam Bahasa Indonesia Pada Instagram Menggunakan Naive Bayes,” J. Ultim., vol. 9, no. 1, pp. 50–58, Jun. 2017.

G. A. Buntoro, “Analisis Sentimen Hatespeech Pada Twitter Dengan Metode Naïve Bayes Classifier Dan Support Vector Machine,” J. Din. Inform., vol. 3, no. 1, p. 56, 2016.

A. Muzakir and R. A. Wulandari, “Model Data Mining sebagai Prediksi Penyakit Hipertensi Kehamilan dengan Teknik Decision Tree,” Sci. J. Informatics, vol. 3, no. 1, pp. 19–26, Jun. 2016.

J. Pardede, M. G. Husada, A. N. Hermana, and S. A. Rumapea, “Fruit Ripeness Based on RGB, HSV, HSL, L∗a∗b∗ Color Feature Using SVM,” 2019 Int. Conf. Comput. Sci. Inf. Technol. ICoSNIKOM 2019, pp. 2–6, 2019.

S. Mawarni and P. N. Bengkalis, “SistemPrediksi Pengunduran Diri Calon Mahasiswa Baru Menggunakan Algoritma C45,” pp. 227–236, 2018.

A. Rohman et al., “Implementasi Data Mining Dengan Algoritma Decision Tree C4 . 5 Untuk Prediksi Kelulusan Mahasiswa Di Universitas,” pp. 134–139, 2019.

Published

2021-01-01

How to Cite

Putra, D. A., & Kamayani, M. (2021). Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Metode Naive Bayes di Program Studi Teknik Informatika UHAMKA. Prosiding Seminar Nasional Teknoka, 5, 34–40. Retrieved from https://journal.uhamka.ac.id/index.php/teknoka/article/view/10238