Teacher Quality Affects On Graduation Of Study Programming Data Approach There With CRISP-DM Method

Authors

  • Mawaddah Harahap Universitas Prima Indonesia
  • Namira Hidayati Universitas Prima Indonesia
  • Sumiati Panjaitan Universitas Prima Indonesia
  • Enjelyna Tambunan Universitas Prima Indonesia
  • Juniati Sihombing Universitas Prima Indonesia

DOI:

10.33395/sinkron.v8i4.12762

Keywords:

Teacher Quality Affects, Fuzzy Logic algorithms, CRISP-DM, Data Science

Abstract

Each student's graduation is influential to the teacher in every subject that can be predicted based on the pattern of habits of the teacher who presents the subject. Web Proggramming is the subject of study that must be completed by every student. If this course is not completed, it is not allowed for the student to take other courses related to it. The custom patterns of teachers in this study were taken from 300 student respondents. An analysis is done to compare the results of questionnaire scores with the assessment of college admissions teachers. From the results of the comparison, it is possible to predict the graduation rate of students to the web programming course. The results of the experiment were that 72% of the students received highly influential predictions, 12% Influential, 7% Sufficient, 5% Influential and 4% Highly Influential.

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References

Arora, S. et al. (2022) ‘Reasoning over Public and Private Data in Retrieval-Based Systems’, pp. 1–27. Available at: http://arxiv.org/abs/2203.11027.

Bayu Baskoro, B., Susanto, I. and Khomsah, S. (2021) ‘Analisis Sentimen Pelanggan Hotel di Purwokerto Menggunakan Metode Random Forest dan TF-IDF (Studi Kasus: Ulasan Pelanggan Pada Situs TRIPADVISOR)’, Journal of Informatics, Information System, Software Engineering and Applications (INISTA), 3(2), pp. 21–029. doi: 10.20895/INISTA.V3.

Clark, K. et al. (2020) ‘ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators’, pp. 1–18. Available at: http://arxiv.org/abs/2003.10555.

Divya, K. et al. (2020) ‘An Interpretation of Lemmatization and Stemming in Natural Language Processing’, Journal of University of Shanghai for Science and Technology, 22(10), pp. 350–357. Available at: https://www.researchgate.net/publication/348306833.

HaCohen-Kerner, Y., Miller, D. and Yigal, Y. (2020) ‘The influence of preprocessing on text classification using a bag-of-words representation’, PLoS ONE, 15(5), pp. 1–22. doi: 10.1371/journal.pone.0232525.

Jayanto, R., Kusumaningrum, R. and Wibowo, A. (2022) ‘Aspect-based sentiment analysis for hotel reviews using an improved model of long short-term memory’, International Journal of Advances in Intelligent Informatics, 8(3), pp. 391–403. doi: 10.26555/ijain.v8i3.691.

Kusumaningrum, R. et al. (2021) ‘Sentiment analysis of Indonesian hotel reviews: from classical machine learning to deep learning’, International Journal of Advances in Intelligent Informatics, 7(3), pp. 292–303. doi: 10.26555/ijain.v7i3.737.

Moch Sambas et al. (2022) ‘Analysis of Lodging and Competition on the Island of Bali during Covid-19 with Big Data’, International Journal of Travel, Hospitality and Events, 1(3), pp. 214–228. doi: 10.56743/ijothe.v1i3.172.

Morama, H. C., Ratnawati, D. E. and Arwani, I. (2022) ‘Analisis Sentimen berbasis Aspek terhadap Ulasan Hotel Tentrem Yogyakarta menggunakan Algoritma Random Forest Classifier’, Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 6(4), pp. 1702–1708.

Priyantina, R. A. and Sarno, R. (2019) ‘Sentiment analysis of hotel reviews using Latent Dirichlet Allocation, semantic similarity and LSTM’, International Journal of Intelligent Engineering and Systems, 12(4), pp. 142–155. doi: 10.22266/ijies2019.0831.14.

Utami, T. A. (2021) ‘Sentiment Analysis of Hotel User Review using RNN Algorithm’, International Journal of Informatics and Computation, 3(1), p. 30. doi: 10.35842/ijicom.v3i1.34.

Yao, L. (2022) ‘Sentiment analysis based on CNN - LSTM hotel reviews’, Journal of Physics: Conference Series, 2330(1). doi: 10.1088/1742-6596/2330/1/012018.

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How to Cite

Harahap, M., Hidayati, N., Panjaitan, S., Tambunan, E., & Sihombing, J. (2023). Teacher Quality Affects On Graduation Of Study Programming Data Approach There With CRISP-DM Method. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(4), 2275-2282. https://doi.org/10.33395/sinkron.v8i4.12762

Issue

Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023

Section

Articles

License

Copyright (c) 2023 Mawaddah Harahap, Namira Hidayati, Enjelyna Tambunan, Sumiati Panjaitan, Juniati Sihombing

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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