Penerapan Algoritma Naïve Bayes Untuk Klasifikasi Kelulusan Mahasiswa Pendidikan Tekniknik Informatika Dan Komputer UIN SMDD Bukittinggi

Sri Atiqah Elvidamayan; Liza Efriyanti; Sarwo Derta; Tasnim Rahmat

doi:10.33633/joins.v11i1.14649

Authors

Sri Atiqah Elvidamayan UIN Sjech M Djamil Djambek Bukittinggi
Liza Efriyanti UIN Sjech M.Djmail Djambek Bukittinggi
Sarwo Derta UIN Sjech M.Djmail Djambek Bukittinggi
Tasnim Rahmat UIN Sjech M.Djmail Djambek Bukittinggi

DOI:

https://doi.org/10.33633/joins.v11i1.14649

Keywords:

Naive bayes, classification, student, data mining

Abstract

Timeliness of graduation is one of the indicators of university quality, and the utilisation of student data can provide valuable information to support decision-making. Quantitative data from the university's TIPD department, including gender, school of origin, Semester Grade Point Average (IPS), and Grade Point Average (GPA), are used as prediction attributes. Through the stages of data collection, attribute determination, data mining (cleaning, selection, transformation), and application of the Naive Bayes algorithm, a prediction model was built and tested. The results showed an accuracy of 87.5%, precision of 57.2%, and recall of 80%. It is concluded that the Naive Bayes algorithm is effective in classifying student graduation, with the funding source attribute identified as one of the influential factors. This study recommends the use of feature filtering such as information gain in future research to improve prediction accuracy.

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