• I Putu Yoga Indrawan Universitas Pendidikan Ganesha
  • I Gede Indrawan Universitas Pendidikan Ganesha
  • I Made Candiasa Universitas Pendidikan Ganesha
Keywords: Multilabel, Classifier, K-Nearest Neighbors, Vector Space Model, Distance


The final project is one of the requirements of graduation students. Students who want to do the final project need to see the final project result on the same topic that has been done before. With a large number of end-task documents, it certainly takes a great effort to find the final project document on the same topic. The final grouping can be automated using the document classification method. The methods that can be used to classify documents are K-Nearest Neighbors as classifier and Vector Space Model to measure the distance between documents From the initial observation, the multilabel classification in the final abstract using Vector Sapce Model and K-Nearest Neighbors has not been evaluated. Because some previous studies have led to the testing of single labels and only lead to one method, as the method is tested. Classification of abstract document final task consists of 2 stages of making distance table using vector space model and multilabel classification using KNN. This method has not been able to predict the label accurately because the exact exact ratio of its optimum value is only 0.57 when m = 4 and k = 8. This method is good enough in predicting the label even though not precisely. Can be seen from the accuracy value of its optimum which is 0.74 when m = 4 and k = 9. The exact match ratio and accuracy value of this method has the optimum value at m = k / 3.


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Adriani, M., Asian, J., Nazief, B., Tahaghoghi, S. M., & Williams, H. E. Stemming Indonesian: A confix-stripping approach. ACM, 33, 2007.

Arifin, A. D., Arieshanti, I., & Arifin, A. Z. (8, Mei 2017). Implementasi Algoritma K-Nearest Neighbour Yang Berdasarkan One Pass Clustering Untuk Kategorisasi Teks. Retrieved from Digital Library Institut Teknologi Sepuluh November : http://digilib.its.ac.id/public/ITS-paper-20008-5108100132-Paper.pdf

Baeza, R., & Neto, R. Modern Information Retrieval. Boston,: Addison Wesley-Pearson International Edition, 1999.

Goller. Automatic Document Classification: A Thorough Evaluation of Various Methods. Proceedings of International Symposium on Information Theory and Its Application, pp. 145-162, 2000.

Hariri, F. R., Utami, E., & Amborowati, A. Learning Vector Quantization untuk Klasifikasi Abstrak Tesis. Citec Journal Vol. 2, 2015.

Manning, C. D., Raghavan, P., & Schütze, H. Introduction to Information Retrieval,.

How to Cite
I. P. Y. Indrawan, I Gede Indrawan, and I Made Candiasa, “KLASIFIKASI MULTILABEL PADA ABSTRAK TUGAS AKHIR MENGGUNAKAN VECTOR SPACE MODEL DAN K-NEAREST NEIGHBORS ”, SINTECH Journal, vol. 2, no. 2, pp. 91-97, Oct. 2019.
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