Klasifikasi kebutuhan fungsional dan non fungsional dalam pengembangan perangkat lunak e-learning
Abstract
In education, technology has become an integral part of human life. Utilizing technology in the form of e-learning platforms allows access to learning flexibly and online. Therefore, this research aims to develop a better e-learning platform for the needs of users in the field of education. The focus of this research is how to facilitate developers in the software design process by using User Requirements and providing analysis as the basis for e-learning software development. The method used is Naive Bayes by using the Rapidminer data analysis tool. Classification of user requirements involves identifying, grouping, and evaluating functional and non-functional requirements. The evaluation results show that the built model performs well, supported by 88.89% accuracy and high precision. The model can predict with good accuracy and has high precision in predicting non-functional classes. In this study, 50 data sets consist of functional and nonfunctional data. The data is divided into two subsets: 28% for training data and 72% for testing data. This division is important to ensure fair representation in model analysis and testing. This helps to get a more accurate picture of the model's ability and reliability in classifying functional and nonfunctional data.
Downloads
References
Budake, R. D., Bhoite, S. D., & Kharade, K. G. (2024). Machine learning-based identification as well as classification of functional and non-functional requirements. https://doi.org/10.1007/978-981-99-6690-5_38
Caroline, J. E. F., Parmar, P., Tiwari, S., Dixit, A., & Gupta, A. (2019). Accuracy prediction using analysis methods and F-measures. Journal of Physics: Conference Series, 1362(1), 012040. https://doi.org/10.1088/1742-6596/1362/1/012040
Diantoro, A., & Santoso, I. B. (2017). Eggs fertilities detection system on the image of kampung chicken egg using naive bayes classifier algorithm. MATICS, 9(2), 53. https://doi.org/10.18860/mat.v9i2.4198
Kong, W. E., Haw, S.-C., Palanichamy, N., & Rahman, S. H. A. (2024). An e-learning recommendation system framework. International Journal on Advanced Science, Engineering and Information Technology, 14(1), 10–19. https://doi.org/10.18517/ijaseit.14.1.19043
Kumar, R., Krishna Goswami, B., Motiram Mhatre, S., & Agrawal, S. (2024). Naive bayes in focus: A thorough examination of its algorithmic foundations and use cases. International Journal of Innovative Science and Research Technology (IJISRT), 2078–2081. https://doi.org/10.38124/ijisrt/IJISRT24MAY1438
Li, H. (2024). The naïve bayes method. In Machine Learning Methods (pp. 67–75). Springer Nature Singapore. https://doi.org/10.1007/978-981-99-3917-6_4
Markoulidakis, I., & Markoulidakis, G. (2024). Probabilistic confusion matrix: A novel method for machine learning algorithm generalized performance analysis. Technologies, 12(7), 113. https://doi.org/10.3390/technologies12070113
Rahayu, S., & Yamasari, Y. (2024). Klasifikasi penyakit stroke dengan metode Support Vector Machine (SVM). Journal of Informatics and Computer Science, 05.
Riehl, K., Neu nteufel, M., & Hemberg, M. (2023). Hierarchical confusion matrix for classification performance evaluation. Journal of the Royal Statistical Society Series C: Applied Statistics, 72(5), 1394–1412. https://doi.org/10.1093/jrsssc/qlad057
Sani, J., Kamal, M. M., Biswas, T. K., Chowdhury, S., Roy, S., & Sarwar, S. (2024). Technologies used in education. International Journal For Multidisciplinary Research, 6(3). https://doi.org/10.36948/ijfmr.2024.v06i03.22020
Santoso, I. B. (2015). Deteksi non-RTH(Ruang terbuka hijau) Kota Malang berbasis citra google earth dengan menggunakan naïve bayes classifier. MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi, 7(2), 59–64.
Senan, S., & Tasci, S. K. (2024). A comprehensive analysis of data mining tools for biomedical data classification. https://doi.org/10.4018/979-8-3693-1062-5.ch012
Serwornoo, M. Y. W., Danso, S., Azanu, B., Semarco, S. K. M., & Aidoo, E. A. K. (2024). Use of digital platforms among university students: A systematic literature review. Qeios. https://doi.org/10.32388/SQKGT5.2
Veziroğlu, M., Veziroğlu, E., & Bucak, İ. Ö. (2024). Performance comparison between naive bayes and machine learning algorithms for news classification. In Bayesian Inference - Recent Trends. IntechOpen. https://doi.org/10.5772/intechopen.1002778
Wienand, M., Wulfert, T., & Hoang, H. (2024). Design principles for e-learning platforms featuring higher-education students’ enterprise systems end-user training. Discover Education, 3(1), 82. https://doi.org/10.1007/s44217-024-00165-z
Zahri, M. H., Sunge, A. S., & Zy, A. T. (2024). Analysis of product sales in the application of data mining with naive bayes classification. Journal of Computer Networks, Architecture and High Performance Computing, 6(3), 1213–1223. https://doi.org/10.47709/cnahpc.v6i3.4255
Zulpukarova, D. I., Abdukaimova, A. Z., & Alieva, K. U. (2024). The use of online educational platforms in teaching. Bulletin of Issyk-Kul University. https://doi.org/10.69722/1694-8211-2024-56-66-73
Copyright (c) 2024 Nenden Nuraeni
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work’s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
