Data Mining Analysis to Predict Student Skills Using Naïve Bayes Method

Yaslinda Lizar(1*), Alya Sahira Firrizqi(2), Asriwan Guci(3), Joko Sunadi(4)
(1) UIN Imam Bonjol Padang
(2) UIN Imam Bonjol
(3) STIKes MERCUBAKTIJAYA Padang
(4) Universitas Putra Indonesia "YPTK" Padang
(*) Corresponding Author
DOI : 10.30983/knowbase.v3i2.7481

Abstract

The possession of specific skills by students not only has a positive impact on the students themselves but also on the Study Program within a Faculty and the University as a whole. However, Study Programs sometimes face difficulties in determining the skills of numerous students even after they have completed 7 semesters of study. Therefore, a method to extract available data in order to determine student skills quickly and accurately is essential. This research aims to apply a data mining method to predict student skills in the Information Systems Study Program at UIN Imam Bonjol Padang. The study focuses solely on predicting student skills in the fields of data processing and programming. The method employed in this data mining analysis is the Naïve Bayes method. Data will be collected from student course grades related to data processing and programming. The data will be processed using an application and subsequently tested using a Confusion Matrix. The research results indicate that predicting the determination of student skills in the Information Systems Study Program at UIN Imam Bonjol can be achieved using the Naïve Bayes algorithm, which yielded a Naïve Bayes model accuracy of 93%, precision of 81%, and recall of 81%. The obtained model can be implemented in the form of an application to determine decision-making strategies for students.

Keywords


Data Mining; Naïve Bayes Method; Data Classification; Students; Skills

References


R. Ningsih, “Perubahan Dunia Kerja The Identity Status Profiles of Late Adolescents in Yogyakarta (Indonesia) and Its Microsystem Context Influence View project”, doi: 10.13140/RG.2.2.11172.01922.

H. A. R. Tilaar, Perubahan Sosial dan Pendidikan. PT. Gramedia Widiasarana Indonesia, 2002.

J. H. Shindo, M. M. Mjahidi, and M. D. Waziri, “Data Mining Algorithms for Prediction of Student Teachers’ Performance in Ict: a Systematic Literature Review,” Inf. Technol. Learn. Tools, vol. 96, no. 4, pp. 29–45, 2023, doi: 10.33407/itlt.v96i4.5246.

J. O. Ong, “Implementasi Algotritma K-means clustering untuk menentukan strategi marketing president university,” J. Ilm. Tek. Ind., vol. vol.12, no, no. juni, pp. 10–20, 2013.

O. Yuda and S. Nugroho, “Data Mining Menggunakan Algoritma Naïve Bayes Untuk Klasifikasi Kelulusan Mahasiswa Universitas Dian Nuswantoro.”

Kusrini and E. T. Luthfi, Algoritma Data Mining. Yogyakarta: Penerbit Andi, 2009.

B. M. M. Alom and M. Courtney, “Educational Data Mining: A Case Study Perspectives from Primary to University Education in Australia,” Int. J. Inf. Technol. Comput. Sci., vol. 10, no. 2, pp. 1–9, 2018, doi: 10.5815/ijitcs.2018.02.01.

M. Ardiansyah Sembiring, M. Fitri Larasati Sibuea, A. Sapta, P. Studi Sistem Informasi, and S. Royal, “Analisa Kinerja Algoritma C.45 Dalam Memprediksi Hasil Belajar,” J. Sci. Soc. Res., vol. 1, no. February, pp. 73–79, 2018, doi: http://dx.doi.org/10.54314/jssr.v1i1.110.

R. Kumara and C. Supriyanto, “Klasifikasi Data Mining Untuk Penerimaan Seleksi Calon Pegawai Negeri Sipil 2014 Menggunakan Algoritma Decision Tree C4.5,” 2013.

F. Nurhuda, S. W. Sihwi, and A. Doewes, “Analisis Sentimen Masyarakat terhadap Calon Presiden Indonesia 2014 berdasarkan Opini dari Twitter Menggunakan Metode Naive Bayes Classifier,” ITSmart J. Teknol. dan Inf., vol. 2, no. 2, pp. 35–42, 2013, doi: doi:10.20961/its.v2i2.630.

F. Gorunescu, Data Mining Concept, Models and Techniques, 1st ed. Springer Berlin, Heidelberg, 2011. [Online]. Available: https://link.springer.com/book/10.1007/978-3-642-19721-5

D. Putra and A. Wibowo, “Prediksi Keputusan Minat Penjurusan Siswa SMA Yadika 5 Menggunakan Algoritma Naïve Bayes,” Pros. Semin. Nas. Ris. Dan Inf. Sci., vol. 2, pp. 84–92, 2020, doi: DOI:10.30645/SENARIS.V2I0.147.

M. F. Rifai, H. Jatnika, and B. Valentino, “Penerapan Algoritma Naïve Bayes Pada Sistem Prediksi Tingkat Kelulusan Peserta Sertifikasi Microsoft Office Specialist (MOS),” Petir, vol. 12, no. 2, pp. 131–144, 2019, doi: 10.33322/petir.v12i2.471.


Article Statistic

Abstract view : 88 times
PDF views : 38 times

The PDF file you selected should load here if your Web browser has a PDF reader plug-in installed (for example, a recent version of Adobe Acrobat Reader).

If you would like more information about how to print, save, and work with PDFs, Highwire Press provides a helpful Frequently Asked Questions about PDFs.

Alternatively, you can download the PDF file directly to your computer, from where it can be opened using a PDF reader. To download the PDF, click the Download link above.

Fullscreen Fullscreen Off

Full Text: PDF

How To Cite This :

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Yaslinda Lizar

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.