Classification of Book Collections Based on DDC 23 Using Text Mining Algorithm at UNIDA Gontor Library
Klasifikasi Koleksi Buku Berbasis DDC 23 Menggunakan Algoritma Teks Mining di Perpustakaan UNIDA Gontor
-
(1) * Muhammad Alwi
Universitas Darussalam Gontor
Indonesia
(2) Oddy Virgantara Putra Universitas Darussalam Gontor
Indonesia
(3) Dihin Muriyatmoko1 Universitas Darussalam Gontor
Indonesia
(*) Corresponding Author
Abstract
The collection of books in a library is a means of information that has become the main actor as a supporter of the existence of a library. UNIDA Gontor library uses the 23rd edition of the Dewey Decimal Classification (DDC 23) classification system, as a reference for the classification numbering system for each book collection. However, in the classification numbering there is no automatic system that helps librarians in assigning classification numbering to each collection. So it is necessary to select a suitable model system to be applied to the automatic classification system. The data used in this study is in the form of blurb data on each collection of Indonesian public books in the UNIDA Gontor Library. In this study, four methods of text mining algorithms were applied to be tested and compared. The algorithm used in testing this research is Multinomial Nb, Logistic Regression, Random Forest, and Support Vector Classifier. From the test results, the highest accuracy results are the Support Vector Classifier algorithm of 72%, while the Logistic Regression algorithm is 69%, Random Forest 69%, and Multinomial Nb 59%. Further research is recommended to apply the support vector classifier algorithm into the UNIDA Gontor library information system.
Downloads
References
T. Yuliani, “Analisis kebutuhan pemustaka pada kegiatan layanan pengembangan koleksi buku Perpustakaan IAIN Batusangkar,” Al-Kuttab J. Kaji. Perpustakaan, Inf. dan kearsipan, vol. 2, no. 1, pp. 41–52, 2020, doi: 10.24952/ktb.v2i1.2328. DOI: https://doi.org/10.24952/ktb.v2i1.2328
B. L. H. Harahap and J. Husna, “Penerapan Sistem Klasifikasi Mandala Di Perpustakaan Gelaran Indonesia Buku Yogyakarta,” J. Ilmu Perpust., vol. 7, no. 1, pp. 181–190, 2018, [Online]. Available: https://ejournal3.undip.ac.id/index.php/jip/article/view/22829.
R. Pajaransyah, T. Darmanto, and D. E. Saputra, “Penambahan Fitur Buku Tamu pada Aplikasi Open Source Slims di Perpustakaan STMIK ‘AMIK BANDUNG,’” MIND J., vol. 3, no. 2, pp. 1–15, 2019, doi: 10.26760/mindjournal.v3i2.1-15. DOI: https://doi.org/10.26760/mindjournal.v3i2.1-15
M. Kadafi, “Klasifikasi Text Judul Buku Perpustakaan untuk Menentukan Kategori Buku Menggunakan K-Nearest Neighbor,” Syst. Inf. Syst. Informatics J., vol. 6, no. 2, pp. 47–53, 2020. DOI: https://doi.org/10.29080/systemic.v6i2.1056
E. Mulyani, F. Pralienka, B. Muhamad, and K. A. Cahyanto, “Pengaruh N-Gram terhadap Klasifikasi Buku menggunakan Ekstraksi dan Seleksi Fitur pada Multinomial Naïve Bayes,” J. Media Inform. Budidarma, vol. 5, pp. 264–272, 2021, doi: 10.30865/mib.v5i1.2672. DOI: https://doi.org/10.30865/mib.v5i1.2672
N. M. Samsudin, C. F. B. Mohd Foozy, N. Alias, P. Shamala, N. F. Othman, and W. I. S. Wan Din, “Youtube spam detection framework using naïve bayes and logistic regression,” Indones. J. Electr. Eng. Comput. Sci., vol. 14, no. 3, pp. 1508–1517, 2019, doi: 10.11591/ijeecs.v14.i3.pp1508-1517. DOI: https://doi.org/10.11591/ijeecs.v14.i3.pp1508-1517
O. V. Putra, F. M. Wasmanson, T. Harmini, and S. N. Utama, “Sundanese Twitter Dataset for Emotion Classification,” CENIM 2020 - Proceeding Int. Conf. Comput. Eng. Network, Intell. Multimed. 2020, no. Cenim 2020, pp. 391–395, 2020, doi: 10.1109/CENIM51130.2020.9297929. DOI: https://doi.org/10.1109/CENIM51130.2020.9297929
J. Ipmawati, Kusrini, and E. Taufiq Luthfi, “Komparasi Teknik Klasifikasi Teks Mining Pada Analisis Sentimen,” Indones. J. Netw. Secur., vol. 6, no. 1, pp. 28–36, 2017.
A. Tanari, A. Handojo, and J. Andjarwirawan, “Aplikasi Pencarian Jurnal Ilmiah dengan Term Frequency- Inverse Document Frequency,” J. Infra, vol. 7, no. 1, 2019.
S. W. U. Vitandy, A. A. Supianto, and F. A. Bachtiar, “Analisis Sentimen Evaluasi Kinerja Dosen menggunakan Term Frequency-Inverse Document Frequency dan Naïve Bayes Classifier,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 6, pp. 6082–6083, 2019, [Online]. Available: http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/5645.
M. Sulehu, Juhar, W. Rimalia, and A. Iskandar, “Implementasi Metode Term Frequency-Inverse Document Frequency- Class Frequency untuk Peringkasan Berita Online,” Celeb. Eng. J., vol. 1, no. April, pp. 54–61, 2019, [Online]. Available: https://journal.lldikti9.id/CEJ/article/view/294.
D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” J. Sains Komput. Inform., vol. 5, no. 2, pp. 697–711, 2021.
D. Putra and A. Wibowo, “Prediksi Keputusan Minat Penjurusan Siswa SMA Yadika 5 Menggunakan Algoritma Naïve Bayes,” SENARIS 2020, vol. 2, pp. 84–92, 2020.
