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The Semantic web is a technology that can understand contexts as humans do. Many fields can take advantage of the technology in solving the problems it faces, one of which is higher education. This research aims to find out the most popular topic(s) discussed in Bachelor’s theses of the Informatics Engineering Study Program of Universitas Padjadjaran. Many of the Bachelor’s theses have discussed similar topic areas, but with different terminologies. It is difficult to determine whether a topic has been discussed too often and what algorithms are often used in the research. The Study Program can use the results of data processing in curriculum design and evaluation. The steps taken in this research are data collection, entity creation, ontology implementation, creating RDF, and performing SPARQL queries. In creating an entity, several terms with similar meanings were obtained and then made into unique master data. The results can be seen from the ontology's visualization using Protégé that the relationship between entities with the same value can be seen. The query results revealed that 36.15% of the theses is about Information System, and 24.41% involve making applications.


web semantic k-nearest neighbour confusion matrix theses semantic web RDF SPARQL Protégé

Article Details

Author Biography

Aditya Pradana, Universitas Padjadjaran Bandung

Departemen Ilmu Komputer Program Studi Teknik Informatika
How to Cite
Pradana, A., & Ridwansyah, R. . (2021). Klasifikasi Topik Skripsi Berdasarkan Makna dengan Pendekatan Semantik Web. Jurnal Komputer Terapan , 7(1), 33–41.


  1. J. Jensen, “A systematic literature review of the use of Semantic Web technologies in formal education,” Br. J. Educ. Technol., vol. 50, no. 2, pp. 505–517, 2019, doi:
  2. S. Ouf, M. A. Ellatif, S. E. Salama, and Y. K. Helmy, “A proposed paradigm for smart learning environment based on semantic web,” Comput. Hum. Behav., vol. 72, p. 79, 2017.
  3. D. Purnamasari, I. W. S. Wicaksana, A. R. Wijaya, H. Riesvicky, and W. Pratama, “PEMANFAATAN WEB SEMANTIK DALAM KLASIFIKASI METADATA SEBAGAI PENCEGAHAN PEMBAJAKAN INFORMASI,” in Prosiding Konferensi Nasional Sistem Informasi, 2015, pp. 1126–1131.
  4. S. Kanza and J. G. Frey, “A new wave of innovation in Semantic web tools for drug discovery,” Expert Opin. Drug Discov., vol. 14, no. 5, pp. 433–444, 2019, doi: 10.1080/17460441.2019.1586880.
  5. W3C, “The Semantic Web Made Easy.”
  6. W3C, “Resource Description Framework.”
  7. W3C, “OWL 2 Web Ontology Language Document Overview (Second Edition).”
  8. S. University, “Protege.”
  9. A. L. Asysyafii and S. Saidah, “Ontologi Web Obat Esensial Nasional Menggunakan Protégé 5.0,” J. Ilm. Teknol. dan …, vol. 21, no. 3, pp. 196–205, 2017, [Online]. Available:
  10. J. Han, M. Kamber, and J. Pei, Data Mining Concepts and Techniques. Waltham: Elsevier, 2012.
  11. S. Visa, B. Ramsay, A. Ralescu, and E. van der Knaap, “Confusion Matrix-based Feature Selection,” in Proceedings of the Twenty­second Midwest Artificial Intelligence and Cognitive Science Conference, 2011, vol. 710, no. January, p. 8.
  12. N. F. Noy and D. L. McGuinness, “Ontology Development 101: A Guide to Creating Your First Ontology.”