The Evaluation of Computer Science Curriculum for High School Education Based on Similarity Analysis


  • Syaifudin Ramadhani SMA Negeri 4 Malang, Indonesia
  • Mokhammad Amin Hariyadi Universitas Islam Negeri Maulana Malik Ibrahim, Malang, Indonesia
  • Cahyo Crysdian Universitas Islam Negeri Maulana Malik Ibrahim, Malang, Indonesia



Text Mining, Similarity Document , Computer Science Curriculum, Curriculum Document, Cosine Similarity, Word2Vec Similarity


The government is currently developing regulations to regulate education curriculum For High School Students. In this regulation, curriculum standards have been created that can be developed by educators in schools. Computer science teachers at the school level develop a curriculum that has been set as a standard curriculum. However, measurable evaluation to optimize the development of the new curriculum has not been available yet. This research proposes a form of evaluation that can be used as a benchmark by analyzing the similarity of curriculum content developed by teachers using a text mining approach. This is conducted by comparing computer science documents with applicable documents, namely knowledge field documents. It is expected that the results of optimizing competency development in the computer science curriculum can be achieved better. The average similarity checking performances using Cosine Similarity and Word2Vec are 40.9850 and 97.3558 respectively. Meanwhile, in the process of fulfilling the knowledge sector, with Cosine Similarity an average percentage of 40.98% was obtained, and with Word2Vec an average percentage of 97.36% was obtained. The results of this trial will be used as a basis for measurable evaluation of teacher contributions to be able to develop the curriculum better according to the applicable curriculum. The results of this evaluation are also used by the government to make future curriculum evaluations more measurable and the standards used are clear and help facilitate curriculum development in schools.


Download data is not yet available.

Plum Analytics





Astawa, Ida Bagus Made dan Adnyana. 2018. Belajar dan Pembelajaran. Depok: Rajawali Grafindo Persada.

"SNPT. (2020 The National Standard on Higher Education (Standar Nasional Pendidikan Tinggi) 3 (Indonesia Ministry of Education and Culture (Kementerian Pendidikan dan Kebudayaan)) Accessed 20 September 2023, permendikbud-no-3-tahun-2020 BERITA NEGARA REPUBLIK INDONESIA No.47, 2020, 1-75."

"Mubai, Akrimullah & Jalinus, Nizwardi & Ambiyar, Ambiyar & Wakhinuddin, Wakhinuddin & Abdullah, Rijal & Rizal, Fahmi & Waskito, Waskito. (2021). Implementasi Model Cipp Dalam Evaluasi Kurikulum Pendidikan Teknik Informatika. EDUKATIF?: JURNAL ILMU PENDIDIKAN. 3. 1383-1394. 10.31004/edukatif.v3i4.549.".

Cc2020 Task Force, Computing Curricula 2020: Paradigms for Global Computing Education. New York, NY, USA: ACM, 2020. doi: 10.1145/3467967.

C. Crysdian, "The evaluation of higher education policy to drive university entrepreneurial activities in information technology learning," Cogent Education, vol. 9, no. 1, p. 2104012, Dec. 2022, doi: 10.1080/2331186X.2022.2104012.

S. Anggara, "Exploring the Effectiveness of Merdeka Belajar Kampus Merdeka Policy in Indonesian Higher Education Institutions: An In-depth Case Study Analysis," AIJP, vol. 15, no. 2, pp. 1563-1570, May 2023, doi: 10.35445/alishlah.v15i2.3885.

H. Mahliatussikah and S. Kuswardono, "Merdeka Belajar Kampus Merdeka (MBKM) Curriculum Design in Arabic Language Education Study Program," in Proceedings of the Unima International Conference on Social Sciences and Humanities (UNICSSH 2022), R. Harold Elby Sendouw, T. Pangalila, S. Pasandaran, and V. P. Rantung, Eds., in Advances in Social Science, Education and Humanities Research, vol. 698. Paris: Atlantis Press SARL, 2023, pp. 587-595. doi: 10.2991/978-2-494069-35-0_72.

"CIPG and Nesta. (2019). Understanding Indonesia's inno_vation system. Downloaded from https://media. 20 September 2023."

M. Pabbajah, I. Abdullah, R. N. Widyanti, H. Jubba, and N. Alim, "Student demoralization in education:The industrialization of university curriculum in 4.0.Era Indonesia," Cogent Education, vol. 7, no. 1, p. 1779506, Jan. 2020, doi: 10.1080/2331186X.2020.1779506.

P. P. Tardan, A. Erwin, K. I. Eng, and W. Muliady, "Automatic text summarization based on semantic analysis approach for documents in Indonesian language," in 2013 International Conference on Information Technology and Electrical Engineering (ICITEE), Yogyakarta, Indonesia: IEEE, Oct. 2013, pp. 47-52. doi: 10.1109/ICITEED.2013.6676209.

A. Fuddoly, J. Jaafar, and N. Zamin, "Keywords Similarity Based Topic Identification for Indonesian News Documents," in 2013 European Modelling Symposium, Manchester, United Kingdom: IEEE, Nov. 2013, pp. 14-20. doi: 10.1109/EMS.2013.3.

S. Sohangir and D. Wang, "Improved sqrt-cosine similarity measurement," J Big Data, vol. 4, no. 1, p. 25, Dec. 2017, doi: 10.1186/s40537-017-0083-6.

A. R. Lahitani, A. E. Permanasari, and N. A. Setiawan, "Cosine similarity to determine similarity measure: Study case in online essay assessment," in 2016 4th International Conference on Cyber and IT Service Management, Bandung, Indonesia: IEEE, Apr. 2016, pp. 1-6. doi: 10.1109/CITSM.2016.7577578.

C. Slamet, A. R. Atmadja, D. S. Maylawati, R. S. Lestari, W. Darmalaksana, and M. A. Ramdhani, "Automated Text Summarization for Indonesian Article Using Vector Space Model," IOP Conf. Ser.: Mater. Sci. Eng., vol. 288, p. 012037, Jan. 2018, doi: 10.1088/1757-899X/288/1/012037.

A. Amalia, D. Gunawan, Y. Fithri, and I. Aulia, "Automated Bahasa Indonesia essay evaluation with latent semantic analysis," J. Phys.: Conf. Ser., vol. 1235, no. 1, p. 012100, Jun. 2019, doi: 10.1088/1742-6596/1235/1/012100.

P. P. Gokul, B. K. Akhil, and K. K. M. Shiva, "Sentence similarity detection in Malayalam language using cosine similarity," in 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore: IEEE, May 2017, pp. 221-225. doi: 10.1109/RTEICT.2017.8256590.

N. R. Ramadhanti and S. Mariyah, "Document Similarity Detection Using Indonesian Language Word2vec Model," in 2019 3rd International Conference on Informatics and Computational Sciences (ICICoS), Semarang, Indonesia: IEEE, Oct. 2019, pp. 1-6. doi: 10.1109/ICICoS48119.2019.8982432.

Z. Jingling, Z. Huiyun, and C. Baojiang, "Sentence Similarity Based on Semantic Vector Model," in 2014 Ninth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, Guangdong, China: IEEE, Nov. 2014, pp. 499-503. doi: 10.1109/3PGCIC.2014.101.

D. A. Diartono, I. Nugroho, and J. A. Razaq, "PENINGKATAN HASIL SISTEM TEMU KEMBALI INFORMASI BERBASIS PADA KATA MAJEMUK MENGGUNAKAN JACCARD SIMILARITY," JDI, vol. 14, no. 1, pp. 1-10, Mar. 2022, doi: 10.35315/informatika.v14i1.9160.




How to Cite

Ramadhani, S., Hariyadi, M. A., & Crysdian, C. (2023). The Evaluation of Computer Science Curriculum for High School Education Based on Similarity Analysis. International Journal of Advances in Data and Information Systems, 4(2), 201-213.
Abstract views : 457 times