@thesis{thesis,
		author={Caesar  Miko and Djunaidi  Karina and Praptini  Puji Catur Siswi},
		title ={PENERAPAN ALGORITMA COSINE SIMILARITY UNTUK MENGANALISIS KESESUAIAN MATA KULIAH PEMINATAN PRODI TEKNIK INFORMATIKA DENGAN PEKERJAAN DI BIDANG TEKNOLOGI INFORMASI},
		year={2022},
		url={http://156.67.221.169/5843/},
		abstract={The current condition of students currently does not have a guide in choosing 
specialization courses that are suitable for work in the Information Technology (IT) field, 
especially students of the IT-PLN Informatics Engineering Study Program. In 
determining students, it is necessary to know what job categories are in accordance with 
job vacancies that are increasingly rapidly developing. Students also have to adapt to the 
needs of the industry and can choose specialization courses that have been provided by
the campus. Therefore, this study analyzes a job vacancy website that is in accordance 
with industry needs and Semester Learning Plans (RPS) using the cosine similarity 
method to produce similarities between study materials for specialization courses and job 
vacancies in the field of information technology. Cosine similarity can be done and get 
an average of 7.5% similarity between specialization courses and job vacancies starting 
from the stage of collecting data from study materials in the Semester Learning Plan 
(RPS), then processed in preprocessing text consisting of case folding, stopword removal, 
lematizer, stemming, and tokenizing. followed by analyzing the text, namely the 
countvectorizer, TF-IDF data weighting, cosine similarity classification, and k-fold cross 
validation data validation. The datasets used are 15 RPS for specialization courses and 
the results of the classification of 10 types of work. The results of this test have an 
accuracy value of 25% using k-fold cross validation.}
		}