Exploring Final Project Trends Utilizing Nuclear Knowledge Taxonomy
An Approach Using Text Mining
DOI:
https://doi.org/10.6017/ital.v42i1.15603Keywords:
document classification, nuclear knowledge management, academic library, text miningAbstract
The National Nuclear Energy Agency of Indonesia (BATAN) taxonomy is a nuclear competence field organized into six categories. The Polytechnic Institute of Nuclear Technology, as an institution of nuclear education, faces a challenge in organizing student publications according to the fields in the BATAN taxonomy, especially in the library. The goal of this research is to determine the most efficient automatic document classification model using text mining to categorize student final project documents in Indonesian and monitor the development of the nuclear field in each category. The kNN algorithm is used to classify documents and identify the best model by comparing Cosine Similarity, Correlation Similarity, and Dice Similarity, along with vector creation binary term occurrence and TF-IDF. A total of 99 documents labeled as reference data were obtained from the BATAN repository, and 536 unlabeled final project documents were prepared for prediction. In this study, several text mining approaches such as stem, stop words filter, n-grams, and filter by length were utilized. The number of k is 4, with Cosine-binary being the best model with an accuracy value of 97 percent, and kNN works optimally when working with binary term occurrence in Indonesian language documents when compared to TF-IDF. Engineering of Nuclear Devices and Facilities is the most popular field among students, while Management is the least preferred. However, Isotopes and Radiation are the most prominent fields in Nuclear Technochemistry. Text mining can assist librarians in grouping documents based on specific criteria. There is also the possibility of observing the evolution of each existing category based on the increase of documents and the application of similar methods in various circumstances. Because of the curriculum and courses given, the growth of each discipline of nuclear science in the study program is different and varied.
References
Ananta Pandu Wicaksana, “Nolimitid/Nolimit-Kamus,” 2015, https://github.com/nolimitid/nolimit-kamus.
Budi Prasetyo and Anggiana Rohandi Yusuf, “Pengelolaan Pengetahuan Eksplisit Berbasis Teknologi Informasi di BATAN,” in Prosiding Seminar Nasional SDM teknologi Nuklir (Seminar Nasional SDM Teknologi Nuklir, Yogyakarta: Sekolah Tinggi Teknologi Nuklir, 2018), 126–32, https://inis.iaea.org/collection/NCLCollectionStore/_Public/50/062/50062856.pdf?r=1.
David Antons et al., “The Application of Text Mining Methods in Innovation Research: Current State, Evolution Patterns, and Development Priorities,” R&D Management 50, no. 3 (June 2020): 329–51, https://doi.org/10.1111/radm.12408.
Devid Haryalesmana, “Masdevid/ID-Stop words,” 2019, https://github.com/masdevid/ID-Stop words.
Fábio Figueiredo et al., “Word Co-Occurrence Features for Text Classification,” Information Systems 36, no. 5 (July 2011): 843–58, https://doi.org/10.1016/j.is.2011.02.002.
Guozhong Feng et al., “A Probabilistic Model Derived Term Weighting Scheme for Text Classification,” Pattern Recognition Letters 110 (July 2018): 23–29, https://doi.org/10.1016/j.patrec.2018.03.003.
Hanan Aljuaid et al., “Important Citation Identification Using Sentiment Analysis of In-Text Citations,” Telematics and Informatics 56 (January 2021): 101492, https://doi.org/10.1016/j.tele.2020.101492.
Jae-Ho Kim and Key-Sun Choi, “Patent Document Categorization Based on Semantic Structural Information,” Information Processing & Management 43, no. 5 (September 2007): 1200–15, https://doi.org/10.1016/j.ipm.2007.02.002.
Joanna Yi-Hang Pong et al., “A Comparative Study of Two Automatic Document Classification Methods in a Library Setting,” Journal of Information Science 34, no. 2 (April 2008): 213–30, https://doi.org/10.1177/0165551507082592.
Judit Tamas and Zsolt Toth, “Classification-Based Symbolic Indoor Positioning over the Miskolc IIS Data-Set,” Journal of Location Based Services 12, no. 1 (January 2, 2018): 2–18, https://doi.org/10.1080/17489725.2018.1455992.
Kanish Shah et al., “A Comparative Analysis of Logistic Regression, Random Forest and KNN Models for the Text Classification,” Augmented Human Research 5, no. 1 (December 2020): 12, https://doi.org/10.1007/s41133-020-00032-0.
Khusbu Thakur and Vinit Kumar, “Application of Text Mining Techniques on Scholarly Research Articles: Methods and Tools,” New Review of Academic Librarianship (May 12, 2021): 1–25, https://doi.org/10.1080/13614533.2021.1918190.
Kiri L. Wagstaff and Geoffrey Z. Liu, “Automated Classification to Improve the Efficiency of Weeding Library Collections,” The Journal of Academic Librarianship 44, no. 2 (March 2018): 238–47, https://doi.org/10.1016/j.acalib.2018.02.001.
Man Lan et al., “Supervised and Traditional Term Weighting Methods for Automatic Text Categorization,” IEEE Transactions on Pattern Analysis and Machine Intelligence 31, no. 4 (April 2009): 721–35, https://doi.org/10.1109/TPAMI.2008.110.
Manika Lamba and Margam Madhusudhan, “Mapping of Topics in DESIDOC Journal of Library and Information Technology, India: A Study,” Scientometrics 120, no. 2 (August 2019): 477–505, https://doi.org/10.1007/s11192-019-03137-5.
Mowafy Mona, Rezk Amira, and Hazem M. El-Bakry, “An Efficient Classification Model for Unstructured Text Document,” American Journal of Computer Science and Information Technology 06, no. 01 (2018), https://doi.org/10.21767/2349-3917.100016.
Muhammad Arshad et al., “Next Generation Data Analytics: Text Mining in Library Practice and Research,” Library Philosophy and Practice (2020): 1–12.
Najat Ali, Daniel Neagu, and Paul Trundle, “Evaluation of K-Nearest Neighbour Classifier Performance for Heterogeneous Data Sets,” SN Applied Sciences 1, no. 12 (December 2019): 1559, https://doi.org/10.1007/s42452-019-1356-9.
Qiang Wang, Rongrong Li, and Gang He, “Research Status of Nuclear Power: A Review,” Renewable and Sustainable Energy Reviews 90 (July 2018): 90–96, https://doi.org/10.1016/j.rser.2018.03.044.
Roiss Alhutaish and Nazlia Omar, “Arabic Text Classification Using K-Nearest Neighbour Algorithm,” The International Arab Journal of Information Technology 12, no. 2 (2015): 190–95.
Ronald Barnett, “Knowing and Becoming in the Higher Education Curriculum,” Studies in Higher Education 34, no. 4 (June 2009): 429–40, https://doi.org/10.1080/03075070902771978.
Snezhana Sulova et al., “Using Text Mining to Classify Research Papers,” in 17th International Multidisciplinary Scientific GeoConference SGEM 2017, vol. 17, International Multidisciplinary Scientific GeoConference-SGEM (17th International Multidisciplinary Scientific GeoConference SGEM, Sofia: Surveying Geology & Mining Ecology Management (SGEM), 2017), 647–54, https://doi.org/10.5593/sgem2017/21/S07.083.
Yen-Hsien Lee et al., “Use of a Domain-Specific Ontology to Support Automated Document Categorization at the Concept Level: Method Development and Evaluation,” Expert Systems with Applications 174 (July 2021): 114681, https://doi.org/10.1016/j.eswa.2021.114681.
Yousif A. Alhaj et al., “A Study of the Effects of Stemming Strategies on Arabic Document Classification,” IEEE Access 7 (2019): 32664–71, https://doi.org/10.1109/ACCESS.2019.2903331.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Faizhal Arif Santosa
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors that submit to Information Technology and Libraries agree to the Copyright Notice.
