Keywords
classification
information gain
multinomial naive bayes
How to Cite
Abstract
This research explores optimization in book classification activities at Politeknik Negeri Indramayu Library. The method commonly used by librarians to classify books is Dewey Decimal Classification (DDC). The DDC method allows librarians to classify book universally and systematically. However, it takes more effort and time to obtain a book classification label. This is not efficient, considering a large number of books in the library. For this reason, we propose an automatic book classification model using the text mining method. Based on the previous research, book classification model using the Multinomial Naïve Bayes (MNB) method has been conducted. The results of these studies indicate an accuracy value of 65.4%. However, the accuracy value still depends on the number of features of the dataset, so that the greater the number of features, the smaller the accuracy value. In this study, Information Gain (IG) method is proposed to select the features of a dataset in the pre- processing stage. MNB accuracy measurements are carried out based on before and after feature selection. 10-fold cross-validation is used to validate the classification model. The results showed an increase in the accuracy of MNB by 6.6%
References
[1] Ibrahim A, Pengantar Ilmu Perpustakaan dan Arsiparis. Jakarta: Gunadarma Ilmu, 2017.
[2] Watthananon, “The relationship of text categorization using Dewey Decimal Classification Techniques,” Int. Conf. ICT Knowl. Eng, Jan. 2015.
[3] Alalyani and Marie-Sainte, “NADA: New Arabic dataset for text classification,” Int. J. Adv. Comput. Sci. Appl, vol. 9, no. 9, pp. 206–212, 2018.
[4] A. Frisilya and W. Yunanto, “Klasifikasi Kompetensi Tugas Akhir Secara Otomatis Berdasarkan Deskripsi Singkat Menggunakan Perbandingan Algoritma K-NN dan Naive Bayes,” Jurnal Aksara Komputer Terapan, 2016.
[5] Pane, Mubarok, Huda, and Adiwijaya, “A multi-lable classification on topics of Quranic verses in English translation using multinomial naive bayes,” Conf. Inf. Commun. Technol. ICoICT, 2019.
[6] G. Singh, B. Kumar, L. Gaur, and A. Tyagi, “Comparison between Multinomial and Bernoulli Naïve Bayes for Text Classification,” Int. Conf. Autom. Comput. Technol. Manag. ICACTM, 2019.
[7] M. Abbas, K. Ali Memon, and A. Aleem Jamali, “Multinomial Naive Bayes Classification Model for Sentiment Analysis,” IJCSNS Int. J. Comput. Sci. Netw. Secur, vol. 19, no. 3, 2019.
[8] R. Ghaniy and K. Sihotang, “Penerapan Metode Naïve Bayes Classifier Untuk Penentuan Topik Tugas Akhir,” Teknois: Jurnal Ilmiah Teknologi Informasi dan Sains, vol. 9, no. 1, pp. 63–72, Sep. 2019, doi: 10.36350/jbs.v9i1.7.
[9] D. J. Lubis and A. I. Ningtiyas, “Penerapan Metode Naïve Bayes Untuk Rekomendasi Pemilihan Asisten Laboratorium Komputer Di Perguruan Tinggi,” Teknois: Jurnal Ilmiah Teknologi Informasi dan Sains, vol. 12, no. 2, pp. 127–138, Jul. 2022, doi: 10.36350/jbs.v12i2.138.
[10] C. Yin and J. Xi, “Maximum entropy model for mobile text classification in cloud computing using improved information gain algorithm,” Multimed. Tools Appl, vol. 76, no. 16, 2017.
[11] S. Kannan, “Preprocessing Techniques for Text Mining,” Int. J. Comput. Sci. Commun. Networks, vol. 5, no. 1, 2015.
[12] M. Z. Asghar, A. Khan, S. Ahmad, and F. M. Kundi, “A Review of Feature Extraction in Sentiment Analysis Muhammad,” J. Basic. Appl. Sci. Res, vol. 4, no. 3, 2014.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright (c) 2025 TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains