BibTex Citation Data :
@article{JSINBIS71821, author = {Esti Mulyani and Munengsih Bunga and Fauzan Ishlakhuddin and Kastuti Kastuti}, title = {Book Classification System Based on Dewey Decimal Classification by Multinomial Naïve Bayes Method}, journal = {Jurnal Sistem Informasi Bisnis}, volume = {15}, number = {3}, year = {2025}, keywords = {Dewey Decimal Classification; Book Classification System; Multinomial Naïve Bayes; Automatic Classification; CodeIgniter Framework}, abstract = { Libraries have the main task of processing library materials by classifying books according to certain methods. Dewey Decimal Classification (DDC) is the most widely used method in the world to determine book classification in libraries. However, the classification process using DDC is inefficient because it takes a long time for the large number of books in the library. This is a serious problem experienced by all libraries, so a solution is needed to bridge the problem. automatic classification system can be the right alternative to overcome the problem. In this research, an automatic classification system based on DDC using the Multinomial Naive Bayes Method so that it can speed up the classification process. This system was created using the CodeIgniter framework with the PHP programming language and MariaDB. Test results from 100 training data and 30 test data show that there are 24 test data with correct classification results and 6 test data with incorrect classification results. So it can be concluded that the accuracy rate of the test is 80%. }, issn = {2502-2377}, doi = {10.21456/vol15iss3pp252-258}, url = {https://ejournal.undip.ac.id/index.php/jsinbis/article/view/71821} }
Refworks Citation Data :
Libraries have the main task of processing library materials by classifying books according to certain methods. Dewey Decimal Classification (DDC) is the most widely used method in the world to determine book classification in libraries. However, the classification process using DDC is inefficient because it takes a long time for the large number of books in the library. This is a serious problem experienced by all libraries, so a solution is needed to bridge the problem. automatic classification system can be the right alternative to overcome the problem. In this research, an automatic classification system based on DDC using the Multinomial Naive Bayes Method so that it can speed up the classification process. This system was created using the CodeIgniter framework with the PHP programming language and MariaDB. Test results from 100 training data and 30 test data show that there are 24 test data with correct classification results and 6 test data with incorrect classification results. So it can be concluded that the accuracy rate of the test is 80%.
Article Metrics:
Last update:
Last update: 2025-07-16 20:17:12
Authors who submit the manuscripts to Journal JSINBIS must understand and agree that if the manuscript is accepted for publication, the copyright of the article belongs to JSINBIS and Diponegoro University as the journal publisher.
Copyright includes the exclusive right to reproduce and provide articles in all forms and media, including reprints, photographs, microfilm and any other similar reproductions, as well as translations. The author reserves the rights to the following:
JSINBIS and Diponegoro University and the Editors make every effort to ensure that no false or misleading data, opinions or statements are published in this journal. The content of articles published in JSINBIS is the sole and exclusive responsibility of the respective authors.
Copyright transfer agreement can be found here: [Copyright transfer agreement in doc] and [Copyright transfer agreement in pdf].
JSINBIS (Jurnal Sistem Informasi Bisnis) is published by the Magister of Information Systems, Post Graduate School Diponegoro University. It has e-ISSN: 2502-2377 dan p-ISSN: 2088-3587 . This is a National Journal accredited SINTA 2 by RISTEK DIKTI No. 48a/KPT/2017.
Journal JSINBIS which can be accessed online by http://ejournal.undip.ac.id/index.php/jsinbis is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats