Effect of information gain on document classification using k-nearest neighbor

Authors

  • Rifki Indra Perwira Universitas Pembangunan Nasional “Veteran” Yogyakarta
  • Bambang Yuwono Universitas Pembangunan Nasional “Veteran” Yogyakarta
  • Risya Ines Putri Siswoyo Universitas Pembangunan Nasional “Veteran” Yogyakarta
  • Febri Liantoni Universitas Sebelas Maret http://orcid.org/0000-0003-1084-965X
  • Hidayatulah Himawan Universiti Teknikal Malaysia Melaka

DOI:

https://doi.org/10.26594/register.v8i1.2397

Keywords:

classification, feature selection, information gain, k-Nearest Neighbor, TF-IDF document

Abstract

State universities have a library as a facility to support students’ education and science, which contains various books, journals, and final assignments. An intelligent system for classifying documents is needed to ease library visitors in higher education as a form of service to students. The documents that are in the library are generally the result of research. Various complaints related to the imbalance of data texts and categories based on irrelevant document titles and words that have the ambiguity of meaning when searching for documents are the main reasons for the need for a classification system. This research uses k-Nearest Neighbor (k-NN) to categorize documents based on study interests with information gain features selection to handle unbalanced data and cosine similarity to measure the distance between test and training data. Based on the results of tests conducted with 276 training data, the highest results using the information gain selection feature using 80% training data and 20% test data produce an accuracy of 87.5% with a parameter value of k=5. The highest accuracy results of 92.9% are achieved without information gain feature selection, with the proportion of training data of 90% and 10% test data and parameters k=5, 7, and 9. This paper concludes that without information gain feature selection, the system has better accuracy than using the feature selection because every word in the document title is considered to have an essential role in forming the classification.

Author Biographies

Rifki Indra Perwira, Universitas Pembangunan Nasional “Veteran” Yogyakarta

Department of Informatics Engineering

Bambang Yuwono, Universitas Pembangunan Nasional “Veteran” Yogyakarta

Department of Informatics Engineering

Risya Ines Putri Siswoyo, Universitas Pembangunan Nasional “Veteran” Yogyakarta

Department of Informatics Engineering

Febri Liantoni, Universitas Sebelas Maret

Department of Informatics Education

Hidayatulah Himawan, Universiti Teknikal Malaysia Melaka

Faculty of Information & Communication Technology

References

[1] M. B. Line, "The Functions Of The University Library," in University and Research Library Studies, W. L. Saunders, Ed., Pergamon, The University of Sheffield, 1968, pp. 148-158.

[2] M. Azam, T. Ahmed, F. Sabah and M. Hussain, "Feature Extraction based Text Classification using K-Nearest Neighbor Algorithm," IJCSNS Int. J. Comput. Sci. Netw. Secur., vol. 18, p. 95–101, 2018.

[3] B. Azhagusundari and A. S. Thanamani, "Feature Selection based on Information Gain," International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 2, no. 2, pp. 18-21, 2013.

[4] F. Liantoni, R. I. Perwira, S. Muharom, R. A. Firmansyah and A. Fahruzi, "Leaf classification with improved image feature based on the seven moment invariant," IOP Conf. Series: Journal of Physics: Conf. Series., vol. 1175, 2019.

[5] F. Fanny, Y. Muliono and F. Tanzil, "A Comparison of Text Classification Methods k-NN, Naïve Bayes, and Support Vector Machine for News Classification," Jurnal Informatika: Jurnal Pengembangan IT, vol. 3, no. 2, pp. 157-160, 2018.

[6] R. Jodha, S. B. C. Gaur, K. R. Chowdhary and A. Mishra, "Text Classification using KNN with different Features Selection Methods," International Journal of Research Publications, vol. 8, no. 1, 2018.

[7] A. Moldagulova and R. B. Sulaiman, "Using KNN Algorithm for Classification of Textual Documents," in 8th International Conference on Information Technology (ICIT), 2017.

[8] R. Andrian, D. Maharani, M. A. Muhammad and A. Junaidi, "Butterfly identification using gray level co-occurrence matrix (glcm) extraction feature and k-nearest neighbor (knn) classification," Register: Jurnal Ilmiah Teknologi Sistem Informasi, vol. 6, no. 1, pp. 11-21, 2020.

[9] H. C. Rustamaji, O. S. Simanjuntak, S. F. Luhrie, B. Yuwono and J. Juwairiah, "Categorical Data Classification based on Fuzzy K-Nearest Neighbor Approach," in 5th International Conference on Science in Information Technology (ICSITech), 2019.

[10] V. Kalra and R. Aggarwal, "Importance of Text Data Preprocessing & Implementation in RapidMiner," in The First International Conference on Information Technology and Knowledge Management, 2018.

[11] L. A. Mullen, K. Benoit, O. Keyes, D. Selivanov and J. Arnold, "Fast, Consistent Tokenization of Natural Language Text," Journal of Open Source Software, vol. 3, no. 23, p. 655, 2018.

[12] N. Chandra, S. K. Khatri and S. Som, "Anti social comment classification based on kNN algorithm," in 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2017.

[13] K. Kowsari, K. J. Meimandi, M. Heidarysafa, S. Mendu, L. Barnes and D. Brown, "Text Classification Algorithms: A Survey," Information, vol. 10, p. 150, 2019.

[14] B. Trstenjak, S. Mikac and D. Donko, "KNN with TF-IDF based Framework for Text Categorization," Procedia Engineering, vol. 69, pp. 1356-1364, 2014.

[15] Y. Doen, M. Murata, R. Otake, M. Tokuhisa and Q. Ma, "Construction of concept network from large numbers of texts for information examination using TF-IDF and deletion of unrelated words," in 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS), Kitakyushu, Japan, 2014.

[16] W. Zhang, T. Yoshida and X. Tang, "A comparative study of TF*IDF, LSI and multi-words for text classification," Expert Systems with Applications, vol. 38, no. 3, pp. 2758-2765, 2011.

[17] R. Andrian, M. A. Naufal, B. Hermanto, A. Junaidi and F. R. Lumbanraja, "k-Nearest Neighbor (k-NN) Classification for Recognition of the Batik Lampung Motifs," IOP Conf. Series: Journal of Physics: Conf. Series, vol. 1338, 2019.

[18] R. T. Wahyuni, D. Prastiyanto and E. Supraptono, "Penerapan Algoritma Cosine Similarity dan Pembobotan TF-IDF pada Sistem Klasifikasi Dokumen Skripsi," Jurnal Teknik Elektro, vol. 9, no. 1, pp. 18-23, 2017.

[19] M. Ali, D.-H. Son, S.-H. Kang and S.-R. Nam, "An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy," Energies, vol. 10, no. 11, p. 1830, 2017.

[20] T. M. Mohamed, "Pulsar selection using fuzzy knn classifier," Future Computing and Informatics Journal, vol. 3, no. 1, 2018.

[21] C.-z. Liu, Y.-x. Sheng, Z.-q. Wei and Y.-Q. Yang, "Research of Text Classification Based on Improved TF-IDF Algorithm," in International Conference of Intelligent Robotic and Control Engineering (IRCE), 2018.

[22] F. S. Al-Anzi and D. AbuZeina, "Toward an enhanced Arabic text classification using cosine similarity and Latent Semantic Indexing," Journal of King Saud University - Computer and Information Sciences, vol. 29, no. 2, pp. 189-195, 2017.

Downloads

Published

2022-01-05

How to Cite

[1]
R. I. Perwira, B. Yuwono, R. I. P. Siswoyo, F. Liantoni, and H. Himawan, “Effect of information gain on document classification using k-nearest neighbor”, Register: Jurnal Ilmiah Teknologi Sistem Informasi, vol. 8, no. 1, pp. 50–57, Jan. 2022.

Issue

Section

Article