Penerapan Text Mining Dalam Klasifikasi Judul Skripsi Mahasiswa Pada Universitas Stella Maris Sumba Menggunakan Metode Naïve Bayes - Desti Astari Umbu Zaza; Elfira Umar; Felysitas Ema Ose Sanga (CV. ITTC INDONESIA, 2024)
Student thesis titles in the Informatics Engineering Department at Stella Maris Sumba University (UNMARIS) have not been grouped optimally based on the student's field of expertise or focus of study. So, the grouping procedure that is important in guiding students must choose a thesis title that is relevant to their field of study. One grouping method that can be used is text mining, which is a part of data mining that is used to identify interesting patterns from lots of data in the form of text. In this research, thesis topics are grouped based on student interest or focus using the Naive Bayes method. Research found that the Naive Bayes algorithm had a 100% accuracy rate in determining the thesis title category. And the accuracy results show that UNMARIS students are more interested in choosing titles in the Software Engineering and Expert and Decision System categories, which shows 31 interested in RPL and 36 in Expert Decision Systems.