ANALISIS PENENTUAN DOSEN PEMBIMBING SKRIPSI MAHASISWA MENGGUNAKAN NAIVE BAYES CLASSIFIER
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Date
2020-09
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Abstract
Determination of the mentor lecturer for students is very influential in completing the
final task. Lecturers who are competent and in accordance with the field of student's
final assignment able to direct of the student's final assignment. In this thesis, text
mining is used to classify the appropriate guidance lecturers for students in completing
the final assignment. Classification is applied to the title of the student's final
assignment based on experience and field of mentor lecturers. The algorithm used naive
bayes classifier algorithm. Before the data classification processed at the text
preprocessing stage, which consist of case folding, stopcharacter removal, stopword,
stemming, and weighted. Evaluation to measure the accuracy of this system using
confussion matrix which resulted in 78% accuracy in mentor lecturers and generate
acuration of 95% in field of expertise.
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Keywords
Mentor Lecturer, Naive Bayes Classifier, Text preprocessing, final project