Graduation Prediction Application using Naive Bayes Algorithm in Sukabumi Muhammadiyah University

Indra Griha Tofik Isa(1*), Indra Satriadi(2),

(1) Politeknik Negeri Sriwijaya
(2) Politeknik Negeri Sriwijaya
(*) Corresponding Author


The use of data mining has become a trend in data processing because of the availability of large amounts of data and the increasing need to convert this data into useful information and knowledge. Besides as a tool in extracting data, data mining is also used as decision support, both in the commercial and non-commercial fields. Of the many algorithms used in data mining, one of them is the Naïve Bayes Algorithm, which in this algorithm is one of the methods in Probabilistic Reasoning which aims to classify data in certain classes. The study conducted by applying the stages of the Naïve Bayes Algorithm in application design to predict student graduation on time based on the parameters contained in the Enrolment Student (PMB), Grade Point Average (IP) and the Finance Department. The data processed were 436 datasets from the Informatics Engineering Study Program, Muhammadiyah University of Sukabumi. The system design uses UML modeling with implementation using the PHP programming language and MySQL database. The results of the study are in the form of graduation prediction applications with an accuracy rate of 78%

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I. G. T. Isa, “Implementasi Pendekatan Kerangka Kerja NIST 800-34 dalam Perancangan Disaster Recovery Plan pada Sistem Informasi Akademik Universitas Implementasi Pendekatan Kerangka Kerja NIST 800-34 dalam Perancangan Disaster Recovery Plan pada Sistem Informasi Akadem,” Inform. Mulawarman J. Ilm. Ilmu Komput., vol. 15, no. 2, pp. 103–113, 2020, doi: 10.30872/jim.v15i2.3724.

C. C. Aggarwal, “An Introduction to Data Classification,” in Data Classification: Algorithms and Applications, C. C. Aggarwal, Ed. New York, USA: CRC Press, 2014, pp. 1–31.

A. Smola and S. V. N. Vishwanathan, Introduction to Machine Learning. 2014.

D. A. A. AlHammadi and M. S. Aksoy, “Data Mining in Higher Education,” Period. Eng. Nat. Sci., vol. 1, no. 2, pp. 1–4, 2013, doi: 10.21533/pen.v1i2.17.

A. Suad A. and B. Wesam S., “Review of data preprocessing techniques in data mining.pdf,” J. Eng. Appl. Sci., vol. 12, no. 16, pp. 4102–4107, 2017, doi: doi=jeasci.2017.4102.4107.

L. W. Santoso and Yulia, “The Analysis of Student Performance Using Data Mining,” Adv. Intell. Syst. Comput., vol. 924, no. June, pp. 559–573, 2019, doi: 10.1007/978-981-13-6861-5_48.

M. Sabransyah, Y. N. Nasution, and F. D. T. Amijaya, “Aplikasi Metode Naive Bayes dalam Prediksi Risiko Penyakit Jantung,” J. EKSPONENSIAL, vol. 8, no. 2, pp. 111–118, 2017.

I. G. T. Isa, “Aplikasi Asesmen Calon Debitur menggunakan Naive Bayes di Koperasi Mitra Sejahtera SMK Negeri 1 Kota Sukabumi,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 10, no. 1, pp. 31–39, 2021, doi: 10.32736/sisfokom.v10i1.1013.

Bustami, “Penerapan Algoritma Naive Bayes untuk Mengklasifikasi Data Nasabah Asuransi,” J. Inform., vol. 8, no. 1, pp. 884–898, 2014.

M. K. Anam, B. N. Pikir, and M. B. Firdaus, “Penerapan Na ̈ıve Bayes Classifier, K-Nearest Neighbor (KNN) dan Decision Tree untuk Menganalisis Sentimen pada Interaksi Netizen danPemeritah,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 21, no. 1, pp. 139–150, 2021, doi: 10.30812/matrik.v21i1.1092.

J. D. Novaković, A. Veljović, S. S. Ilić, Ž. Papić, and T. Milica, “Evaluation of Classification Models in Machine Learning,” Theory Appl. Math. Comput. Sci., vol. 7, no. 1, p. Pages: 39 – 46, 2017, [Online]. Available:



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