Pembobotan TF-IDF pada Judul Penelitian Dosen Sebagai Dasar Klasifikasi Menggunakan Algoritma K-NN (Studi Kasus: Universitas Siliwangi) - Agus Supriatman (Fakultas Teknik, 2021)
The rapid and inexpensive development of digital storage media technology has led to an increase in the number of electronic documents stored on storage systems such as those in universities. Various academic scientific works, such as articles , research reports, etc., are available in digitally . In addition to teaching activities, lecturers are also required to research to deepen their knowledge. With so much research, of course, the resulting research will be very diverse, which is why it is deemed necessary to have groupings related to the title or topic of the research carried out so that it can support the management of Siliwangi University in achieving its goals. Using TF-IDF weighting in text mining on a research title data set, it is known that the optimal number of k in this study is k = 4 with an accuracy rate of 90.7% and the resulting number of each group is 115 scientific titles, 142 social titles, and 98 educational titles for a total of 355 research titles.