REDUKSI DIMENSI FITUR MENGGUNAKAN ALGORITMA ALOFT UNTUK PENGELOMPOKAN DOKUMEN

Authors

  • Mamluatul Hani’ah Jurusan Teknik Informatika, Fakultas Teknologi Informasi, ITS
  • Chastine Fatichah Jurusan Teknik Informatika, Fakultas Teknologi Informasi, ITS
  • Diana Purwitasari Jurusan Teknik Informatika, Fakultas Teknologi Informasi, ITS
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DOI:

https://doi.org/10.12962/j24068535.v14i2.a573

Abstract

Pengelompokan dokumen masih memiliki tantangan dimana semakin besar dokumen maka akan menghasilkan fitur yang semakin banyak. Sehingga berdampak pada tingginya dimensi dan dapat menyebabkan performa yang buruk terhadap algoritma clustering. Cara untuk mengatasi masalah ini adalah dengan reduksi dimensi. Metode reduksi dimensi seperti seleksi fitur dengan metode filter telah digunakan untuk pengelompokan dokumen. Akan tetapi metode filter sangat tergantung pada masukan pengguna untuk memilih sejumlah n fitur teratas dari keseluruhan dokumen. Algoritma ALOFT (At Least One FeaTure) dapat menghasilkan sejumlah set fitur secara otomatis tanpa adanya parameter masukan dari pengguna. Karena sebelumnya algoritma ALOFT digunakan pada klasifikasi dokumen, metode filter yang digunakan pada algoritma ALOFT membutuhkan adanya label pada kelas sehingga metode filter tersebut tidak dapat digunakan untuk pengelompokan dokumen. Pada penelitian ini diusulkan metode reduksi dimensi fitur dengan menggunakan variasi metode filter pada algoritma ALOFT untuk pengelompokan dokumen. Sebelum dilakukan proses reduksi dimensi langkah pertama yang harus dilakukan adalah tahap preprocessing kemudian dilakukan perhitungan bobot tfidf. Proses reduksi dimensi dilakukan dengan menggunakan metode filter seperti Document Frequency (DF), Term Contribution (TC), Term Variance Quality (TVQ), Term Variance (TV), Mean Absolute Difference (MAD), Mean Median (MM), dan Arithmetic Mean Geometric Mean (AMGM). Selanjutnya himpunan fitur akhir dipilih dengan algoritma ALOFT. Tahap terakhir adalah pengelompokan dokumen menggunakan dua metode clustering yang berbeda yaitu k-means dan Hierarchical Agglomerative Clustering (HAC). Dari hasil ujicoba didapatkan bahwa kualitas cluster yang dihasilkan oleh metode usulan dengan menggunakan algoritma k-means mampu memperbaiki hasil dari metode VR.

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References

[1] A. Leuski, "Evaluating document clustering for interactive information retrieval," in Proceedings of the tenth international conference on Information and knowledge management, ACM, 2001.

[2] D. G. Roussinov and H. Chen, "Document clustering for electronic meetings: an experimental comparison of two techniques," Decision Support Systems, vol. 27, no. 1, pp. 67-79, 1999.

[3] F. Rozi, S. H. Wijoyo, S. A. Isanta, Y. Azhar and P. Diana, "Pelabelan Klaster Fitur Secara Otomatis pada Perbandingan Review Produk," Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 1, no. 2, pp. 55-61, 2014.

[4] N. Hayatin, C. Fatichah and D. Purwitasari, "Pembobotan Kalimat Berdasarkan Fitur Berita dan Trending Issue untuk Peringkasan Multi Dokumen Berita," JUTI: Jurnal Ilmiah Teknologi Informasi, vol. 13, no. 1, pp. 38-44, 2015.

[5] I. Lukmana, D. Swanjaya, A. Kurniawardhani, A. Z. Arifin and D. Purwitasari, "Multi-Document Summarization Based On Sentence Clustering Improved Using Topic Words," JUTI: Jurnal Ilmiah Teknologi Informasi, vol. 12, no. 2, pp. 1-8, 2014.

[6] A. Z. Arifin, I. P. A. K. Mahendra and H. T. Ciptaningtyas, "Enhanced confix stripping stemmer and ants algorithm for classifying news document in indonesian language," 2009.

[7] G. Salton, A. Wong and C.-S. Yang, "A vector space model for automatic indexing," Communications of the ACM, vol. 18, p. 613–620, 1975.

[8] S. Tabakhi, P. Moradi and F. Akhlaghian, "An unsupervised feature selection algorithm based on ant colony optimization," Engineering Applications of Artificial Intelligence, vol. 32, pp. 112-123, 2014.

[9] W. Song and S. C. Park, "Genetic algorithm for text clustering based on latent semantic indexing," Computers & Mathematics with Applications, vol. 57, no. 11, pp. 1901-1907, 2009.

[10] K. K. Bharti and P. K. Singh, "A three-stage unsupervised dimension reduction method for text clustering," Journal of Computational Science, vol. 5, no. 2, pp. 156-169, 2014.

[11] K. K. Bharti and P. K. Singh, "Hybrid dimension reduction by integrating feature selection with feature extraction method for text clustering," Expert Systems with Applications, vol. 42, p. 3105–3114, 2015.

[12] L. Liu, J. Kang, J. Yu and Z. Wang, "A comparative study on unsupervised feature selection methods for text clustering," 2005.

[13] A. J. Ferreira and M. A. Figueiredo, "Efficient feature selection filters for high-dimensional data," Pattern Recognition Letters, vol. 33, no. 13, pp. 1794-1804, 2012.

[14] H. Liu, H. Motoda and eds, Computational methods of feature selection, CRC Press, 2007.

[15] R. H. Pinheiro, G. D. Cavalcanti, R. F. Correa and T. I. Ren, "A global-ranking local feature selection method for text categorization," Expert Systems with Applications, vol. 39, no. 17, pp. 12851-12857, 2012.

[16] C. C. Aggarwal and C. Zhai, Mining text data, Springer Science & Business Media, 2012.

[17] F. Z. Tala, "A study of stemming effects on information retrieval in Bahasa Indonesia," Institute for Logic, Language and Computation Universeit Van Amsterdam, 2003.

[18] I. Dhillon, J. Kogan and C. Nicholas, "Feature selection and document clustering," New York, 2004.

[19] P. J. Rousseeuw, "Silhouettes: a graphical aid to the interpretation and validation of cluster analysis," Journal of computational and applied mathematics, vol. 20, pp. 53-65, 1987.

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Published

2016-07-01

Issue

Vol 14, No. 2, Juli 2016

Section

Articles

How to Cite

[1]
M. Hani’ah, C. Fatichah, and D. Purwitasari, “REDUKSI DIMENSI FITUR MENGGUNAKAN ALGORITMA ALOFT UNTUK PENGELOMPOKAN DOKUMEN”, JUTI, vol. 14, no. 2, pp. 207–216, Jul. 2016, doi: 10.12962/j24068535.v14i2.a573.

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