Exploring Final Project Trends Utilizing Nuclear Knowledge Taxonomy

Abstract

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.