The Analysis of Proximity Between Subjects Based on Primary Contents Using Cosine Similarity on Lective
Corresponding Author(s) : Muhammad Andi Al-rizki
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol 2, No 4, November-2017
Abstract
In education world, recognizing the relationship between one subject and another is imperative. By recognizing the relationship between courses, performing sustainability mapping between subjects can be easily performed. Moreover, detecting and reducing any duplicated contents in several subjects will be also possible to execute. Of course, these conveniences will benefit lecturers, students and departments. It will ease the analysis and discussion processes between lecturers related to subjects in the same domain. In addition, students will conveniently choose a group of subjects they are interested in. Furthermore, departments can easily create a specialization group based on the similarity of the subjects and combine the courses possessing high similarity. In this research, given a good database, the relationship between subjects was calculated based on the proximity of the primary contents of the subjects. The feature used was term feature, in which value was determined by calculating TF-IDF (Term Frequency Inverse Document Frequency) from each term. In recognizing the value of proximity between subjects, cosine similarity method was implemented. Finally, testing was done utilizing precision, recall and accuracy method. The research results show that the precision and accuracy values are 90,91% and the recall value is 100%.
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- Menristekdikti, "Guide of Curriculum Plan for Higher Education, 2nd ed.," Jakarta: Indonesian Directorate General of Learning and Student Affairs Ministry of Research, Technology and Higher Education, 2016.
- Sutrisno & Suyadi, "Curriculum Design of Higher Education; Referring to Indonesian National Framework," Bandung: PT Remaja Rosdakarya, 2016.
- O. Karmayasa, "Implementation of Vector Space Model and Some Notation Methods on Term Frequency Inverse Document Frequency (TF-IDF),“ JELIKU (Electronic Journal of Computer Science of Universitas Udayana, 2012.
- M. Fitri, “Designing Information Retrieval System using Combination of TF-IDF Weighing Method in Document Searching based on Bahasa Indonesia," Jurnal Sistem dan Teknologi Informasi, 2013.
- M. N. Saadah, R. W. Atmagi, D. S. Rahayu, and A. Z. Arifin, “Text Document Retrieval System using TF-IDF and LCS Weighing," JUTI: Scientific Journal for Information Technology, Vol. 11, No. 1, Pp. 19, Jan. 2013.
- M. Nurjannah, H. Hamdani, and I. F. Astuti, “Application of Term Frequency-Inverse Document Frequency (TF-IDF) Algorithm for Mining Text," Informatics Journal of Mulawarman, Vol. 8, No. 3, pp. 110–113, Jun. 2016.
- R. V. Imbar et al., “Implementation of Cosine Similarity dan Algoritma Smith-Waterman to Detect Text Similarity," Informatics Journal, Vol. 10, No. 1, Pp. 31–42, 2014.
- Sugiyamto, B. Surarso, and A. Sugiharto, “Performance Analisys of Cosine Method and Jacard on Document Similarity testing," Journal of Informatics Society, Vol. 5, No. 10, Pp. 1–8, 2014.
- W. H. Gomaa and A. A. Fahmy, “A Survey of Text Similarity Approaches,” International Journal of Computer Applications, Vol. 68, No. 13, Pp. 975–8887, 2013.
- S. M. Weiss, N. Indurkhya, F. J. Damerau, and T. Zhang, "Text Mining: Methods for Analyzing Unstructured Information." Springer : New York, 2004.
- B. Liu, "Web Data Mining." Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.
- R. V. Imbar, A. Adelia, M. Ayub, and A. Rehatta, “Implementation Cosine Similarity and Smith-Waterman Algorithm to Detect Text Similarity," Informatics Journal, Vol. 10, No. 1, 2015.
- M. Ridwan, H. Suyono, and M. Sarosa, “Application Data Mining to Evaluate College Students' Academic Performance using Naïve Bayes Classifier Algorithm," EECCIS, Vol. 7, Pp. 59–64, 2013.
References
Menristekdikti, "Guide of Curriculum Plan for Higher Education, 2nd ed.," Jakarta: Indonesian Directorate General of Learning and Student Affairs Ministry of Research, Technology and Higher Education, 2016.
Sutrisno & Suyadi, "Curriculum Design of Higher Education; Referring to Indonesian National Framework," Bandung: PT Remaja Rosdakarya, 2016.
O. Karmayasa, "Implementation of Vector Space Model and Some Notation Methods on Term Frequency Inverse Document Frequency (TF-IDF),“ JELIKU (Electronic Journal of Computer Science of Universitas Udayana, 2012.
M. Fitri, “Designing Information Retrieval System using Combination of TF-IDF Weighing Method in Document Searching based on Bahasa Indonesia," Jurnal Sistem dan Teknologi Informasi, 2013.
M. N. Saadah, R. W. Atmagi, D. S. Rahayu, and A. Z. Arifin, “Text Document Retrieval System using TF-IDF and LCS Weighing," JUTI: Scientific Journal for Information Technology, Vol. 11, No. 1, Pp. 19, Jan. 2013.
M. Nurjannah, H. Hamdani, and I. F. Astuti, “Application of Term Frequency-Inverse Document Frequency (TF-IDF) Algorithm for Mining Text," Informatics Journal of Mulawarman, Vol. 8, No. 3, pp. 110–113, Jun. 2016.
R. V. Imbar et al., “Implementation of Cosine Similarity dan Algoritma Smith-Waterman to Detect Text Similarity," Informatics Journal, Vol. 10, No. 1, Pp. 31–42, 2014.
Sugiyamto, B. Surarso, and A. Sugiharto, “Performance Analisys of Cosine Method and Jacard on Document Similarity testing," Journal of Informatics Society, Vol. 5, No. 10, Pp. 1–8, 2014.
W. H. Gomaa and A. A. Fahmy, “A Survey of Text Similarity Approaches,” International Journal of Computer Applications, Vol. 68, No. 13, Pp. 975–8887, 2013.
S. M. Weiss, N. Indurkhya, F. J. Damerau, and T. Zhang, "Text Mining: Methods for Analyzing Unstructured Information." Springer : New York, 2004.
B. Liu, "Web Data Mining." Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.
R. V. Imbar, A. Adelia, M. Ayub, and A. Rehatta, “Implementation Cosine Similarity and Smith-Waterman Algorithm to Detect Text Similarity," Informatics Journal, Vol. 10, No. 1, 2015.
M. Ridwan, H. Suyono, and M. Sarosa, “Application Data Mining to Evaluate College Students' Academic Performance using Naïve Bayes Classifier Algorithm," EECCIS, Vol. 7, Pp. 59–64, 2013.