Exploring Final Project Trends Utilizing Nuclear Knowledge Taxonomy.
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 B...
| Publicado en: | Information Technology & Libraries Vol. 42; no. 1; pp. 1 - 20 |
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| Autor principal: | |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
American Library Association
Mar2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=162528315&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162528315 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07309295 ITL jtl: Information Technology & Libraries issn: 07309295 maglogo: N pubinfo: dt: Mar2023 vid: 42 iid: 1 pid: 55 pub: American Library Association place: Chicago, Illinois artinfo: ui: 162528315 162528315 162528315 10.6017/ital.v42i1.15603 162528315 ppf: 1 ppct: 19 formats: fmt: @attributes: type: P tig: atl: Exploring Final Project Trends Utilizing Nuclear Knowledge Taxonomy. aug: au: Santosa, Faizhal Arif affil: Academic Librarian, Polytechnic Institute of Nuclear Technology, National Research and Innovation Agency sug: subj: Documentation Classification Data Mining Utilization Students Academic Achievement Indonesia Nuclear Energy Economics Human Indonesia Libraries, Academic Validity Data Analysis Software Comparative Studies Models, Statistical ab: 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. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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