Komparasi Ekstraksi Fitur dalam Klasifikasi Teks Multilabel Menggunakan Algoritma Machine Learning
Keywords:
Ekstraksi Fitur, Klasifikasi Teks Multilabel, Machine Learning, Perbandingan Kinerja Model
Abstract
Ektraksi fitur dan algoritma klasifikasi teks merupakan bagian penting dari pekerjaan klasifikasi teks, yang memiliki dampak langsung pada efek klasifikasi teks. Algoritma machine learning tradisional seperti Na¨ıve Bayes, Support Vector Machines, Decision Tree, K-Nearest Neighbors, Random Forest, Logistic Regression telah berhasil dalam melakukan klasifikasi teks dengan ektraksi fitur i.e. Bag ofWord (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Documents to Vector (Doc2Vec), Word to Vector (word2Vec). Namun, bagaimana menggunakan vektor kata untuk merepresentasikan teks pada klasifikasi teks menggunakan algoritma machine learning dengan lebih baik selalu
menjadi poin yang sulit dalam pekerjaan Natural Language Processing saat ini. Makalah ini bertujuan untuk membandingkan kinerja dari ekstraksi fitur seperti BoW, TF-IDF, Doc2Vec dan Word2Vec dalam melakukan klasifikasi teks dengan menggunakan algoritma machine learning. Dataset yang digunakan sebanyak 1000 sample yang berasal dari tribunnews.com dengan split data 50:50, 70:30, 80:20 dan 90:10. Hasil dari percobaan menunjukkan bahwa algoritma Na¨ıve Bayes memiliki akurasi tertinggi dengan menggunakan ekstraksi fitur TF-IDF sebesar 87% dan BoW sebesar 83%. Untuk ekstraksi fitur Doc2Vec, akurasi tertinggi pada algoritma SVM sebesar 81%. Sedangkan ekstraksi fitur Word2Vec dengan algoritma machine learning (i.e. i.e. Na¨ıve Bayes, Support Vector Machines, Decision Tree, K-Nearest Neighbors, Random Forest, Logistic Regression) memiliki akurasi model dibawah 50%. Hal ini menyatakan, bahwa Word2Vec kurang optimal digunakan bersama algoritma machine learning, khususnya pada dataset tribunnews.com.
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Swarm Optimization dan Term Frequency,” Jurnal Informatika Universitas Pamulang, vol. 2, no. 1, p. 14, 2017.
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Conference Proceeding Series, vol. Part F1318, no. 319, 2017.
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Support Vector Machine,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya,
vol. 2, no. 11, pp. 4704–4713, 2018.
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Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 1, pp. 111–124, 2021.
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Bahasa Indonesia,” Journal of Information System,Graphics, Hospitality and Technology, vol. 3, no. 01, pp. 29–38, 2021.
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2019, vol. 11434 LNCS.
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untuk Menganalisis Sentimen pada Interaksi Netizen danPemeritah,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa
Komputer, vol. 21, no. 1, pp. 139–150, 2021.
[27] D. R. Java,W.Wijaya, J. Hendry, and B. Sumanto, “Seleksi Fitur Terhadap Performa Kinerja Sistem E-Nose untuk Klasifikasi Aroma
Kopi Gayo Features Selection on E-Nose System Performance for Classification of Gayo Coffee Aroma,” MATRIK J. Manajemen,
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Sciences, vol. 471, pp. 216–232, 2019.
[30] Y. Shao, S. Taylor, N. Marshall, C. Morioka, and Q. Zeng-Treitler, “Clinical Text Classification with Word Embedding Features vs.
Bag-of-Words Features,” Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, pp. 2874–2878, 2019.
[31] X. Wang, D. Gao, G. Zhang, X. Zhang, Q. Li, Q. Gao, R. Chen, S. Xu, L. Huang, Y. Zhang, L. Lin, C. Zhong, X. Chen, G. Sun,
Y. Song, X. Yang, L. Hao, H. Yang, L. Yang, and N. Yang, “Exposure to multiple metals in early pregnancy and gestational diabetes
mellitus: A prospective cohort study,” Environment International, vol. 135, p. 105370, 2020.
[32] G. Wang, C. Li, W. Wang, Y. Zhang, D. Shen, X. Zhang, R. Henao, and L. Carin, “Joint embedding of words and labels for text
classification,” ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
(Long Papers), vol. 1, pp. 2321–2331, 2018.
[33] F. Sun and H. Chen, “Feature extension for Chinese short text classification based on LDA and Word2vec,” Proceedings of the 13th
IEEE Conference on Industrial Electronics and Applications, ICIEA 2018, no. 1, pp. 1189–1194, 2018.
[34] H. Xu, A. Kotov, M. Dong, A. I. Carcone, D. Zhu, and S. Naar-King, “Text classification with topic-based word embedding and
Convolutional Neural Networks,” ACM-BCB 2016 - 7th ACM Conference on Bioinformatics, Computational Biology, and Health
Informatics, no. April 2019, pp. 88–97, 2016.
Published
2022-07-31
How to Cite
Efrizoni, L., Defit, S., Tajuddin, M., & Anggrawan, A. (2022). Komparasi Ekstraksi Fitur dalam Klasifikasi Teks Multilabel Menggunakan Algoritma Machine Learning. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(3), 653-666. https://doi.org/https://doi.org/10.30812/matrik.v21i3.1851
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