Implementasi Algoritma Cosine Similarity pada sistem arsip dokumen di Universitas Islam Sultan Agung

Dedy Kurniadi, Sam Farisa Chaerul Haviana, Andika Novianto

Abstract


Archiving in University that have not been well organized will cause a problems, the documents need for structuring and archives properly in the systems for the good standard a universities. The most importance of ease in finding the required archives is an important reason why it is necessary to develop an archive search system that can facilitate and improve the process of searching the archived document. Apllying cosine similarity algorithm in Information Systems is a solution for University to organizing archived documents, results from this reserach is the systems can show the relavant document from database list with precision 88.8% and recall 76.1%  from all the data in database. 


Keywords


Cosine Similarity; Information Systems; precision; recall

References


R. A. Pascapraharastyan, A. Supriyanto, and P. Sudarmaningtyas, “Rancang Bangun Sistem Informasi Manajemen Arsip Rumah Sakit Bedah Surabaya Berbasis Web,” Sist. Inf., vol. 3, no. 1, pp. 72–77, 2014.

M. Rifauddin, “Pengelolaan Arsip Elektronik Berbasis Teknologi,” Khizanah Al- Hikmah J. Ilmu Perpustakaan, Informasi, dan Kearsipan, vol. 4, no. 2, pp. 168–178, 2016.

O. Nurdiana, J. Jumadi, and D. Nursantika, “Perbandingan Metode Cosine Similarity Dengan Metode Jaccard Similarity Pada Aplikasi Pencarian Terjemah Al-Qur’an Dalam Bahasa Indonesia,” J. Online Inform., vol. 1, no. 1, p. 59, 2016.

S. Shum, N. Dehak, R. Dehak, and J. R. Glass, “Unsupervised Speaker Adaptation based on the Cosine Similarity for Text-Independent Speaker Verification,” Proc. Odyssey, 2010.

V. Thada and V. Jaglan, “Comparison of jaccard, dice, cosine similarity coefficient to find best fitness value for web retrieved documents using genetic algorithm,” Int. J. Innov. Eng. Technol., vol. 2, no. 4, pp. 202–205, 2013.

R. Mihalcea, C. Corley, and C. Strapparava, “Corpus-based and knowledge-based measures of text semantic similarity,” Proc. Natl. Conf. Artif. Intell., vol. 1, pp. 775–780, 2006.

M. E. Scholar, N. Engineering, T. Nadu, and T. Nadu, “a S Urvey on S Imilarity M Easures in T Ext M Ining,” vol. 3, no. 1, pp. 19–28, 2016.

J. Ramos, “Using TF-IDF to Determine Word Relevance in Document Queries,” New Educ. Rev., vol. 42, no. 4, pp. 40–51, 2003.

B. Li and L. Han, “Distance weighted cosine similarity measure for text classification,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8206 LNCS, pp. 611–618, 2013.

B. Trstenjak, S. Mikac, and D. Donko, “KNN with TF-IDF based framework for text categorization,” Procedia Eng., vol. 69, pp. 1356–1364, 2014.

G. Sidorov, A. Gelbukh, H. Gómez-Adorno, and D. Pinto, “Soft similarity and soft cosine measure: Similarity of features in vector space model,” Comput. y Sist., vol. 18, no. 3, pp. 491–504, 2014.




DOI: http://dx.doi.org/10.26623/transformatika.v17i2.1613

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