Implementation of Sentiment Analysis in Internship Evaluation Information System
Article Sidebar
Main Article Content
Abstract
Internship is an academic activity that connects students with real-world work environments, making comments and scores from companies critical data sources for evaluating student achievement and curriculum alignment. In the Information System Study Program at Darma Persada University, company feedback from the 2016-2025 period had not been integrated or analyzed comprehensively, particularly regarding textual comments from corporate HR. This study aims to design, build, and evaluate a internship evaluation information system that processes numerical scores, letter grades, and company textual comments using sentiment analysis. The system was developed using the Rapid Application Development (RAD) methodology through requirements planning, design workshops, rapid construction, and evaluative implementation. Sentiment analysis was implemented via the Hugging Face API service to classify comments into positive, neutral, and negative categories along with their confidence scores. System evaluation was conducted using Black-box testing for functional validity, sentiment classification performance metrics (Accuracy, Precision, Recall, F1-Score), and user assessment using the System Usability Scale (SUS) questionnaire. The test results showed a 100% success rate in Black-box functional testing. The sentiment analysis model achieved an accuracy of 85.0%, precision of 84.6%, recall of 85.0%, and an F1-Score of 84.8% on the sample evaluation set. Furthermore, the system usability assessment via the SUS questionnaire yielded an average score of 82.5, placing the system in the 'Excellent' usability category (Grade A). This system effectively assists the study program in interpreting internship quality patterns objectively, systematically, and in a data-driven manner.
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
[1] T. Sudibyo and A. Setiawan, “Perancangan Sistem Informasi Berbasis Web Pendataan Stok Helm di Toko Helm Bogor,” Journal TIFDA (Technology Information and Data Analytic), vol. 1, no. 1, pp. 1–3, 2024, doi: 10.70491/tifda.v1i1.26.
[2] D. B. Darmawan, R. Septiadi, R. W. Saputra, and W. Haryono, “Design of Digital Attendance System and Monitoring of Attendance and Overtime at PT. Trikarsa Bahtera Abadi,” Journal Technology Information and Data Analytic, vol. 2, no. 1, pp. 56–61, 2025, doi: 10.70491/tifda.v2i1.87.
[3] S. Elysia and Herianto, “Chatbot Berbasis Retrieval Augmented Generation (RAG) untuk Peningkatan Layanan Informasi Sekolah,” Journal Technology Information and Data Analytic, vol. 1, no. 2, pp. 52–58, 2024, doi: 10.70491/tifda.v1i2.52.
[4] B. T. Mahardika and R. Maulana, “Implementation of Natural Language Processing in Digital File Management using Chatbot at The Center of PPKUKM Service,” Journal Technology Information and Data Analytic, vol. 2, no. 2, pp. 109–113, 2025, doi: 10.70491/tifda.v2i2.109.
[5] S. Suhartanta, N. Soewito, A. Kholifah, and A. Sofyan, “Evaluation of student internship programs to support the sustainability of industry-university cooperation,” Jurnal Pendidikan Vokasi, vol. 14, no. 1, pp. 63–73, 2024.
[6] M. Nugraha, R. A. S., H. Fathi, and M. R. Baginda, “Development a Web-Based Student Internship Application using Laravel Framework and Waterfall Model,” Journal of Information Technology and Its Utilization, vol. 6, no. 1, pp. 31–38, 2023.
[7] M. Wankhade, A. C. S. Rao, and C. Kulkarni, “A survey on sentiment analysis methods, applications, and challenges,” Artificial Intelligence Review, vol. 55, no. 7, pp. 5731–5780, 2022, doi: 10.1007/s10462-022-10144-1.
[8] M. Birjali, M. Kasri, and A. Beni-Hssane, “A comprehensive survey on sentiment analysis: Approaches, challenges and trends,” Knowledge-Based Systems, vol. 226, p. 107134, 2021, doi: 10.1016/j.knosys.2021.107134.
[9] S. Minaee, N. Kalchbrenner, E. Cambria, N. Nikzad, M. Chenaghlu, and J. Gao, “Deep learning-based text classification: A comprehensive review,” ACM Computing Surveys, vol. 54, no. 3, pp. 1–40, 2021, doi: 10.1145/3439726.
[10] B. Wilie et al., “IndoNLU: Benchmark and resources for evaluating Indonesian natural language understanding,” in Proc. 1st Conf. Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conf. Natural Language Processing, 2020, pp. 843–857.
[11] F. Koto, A. Rahimi, J. H. Lau, and T. Baldwin, “IndoLEM and IndoBERT: A benchmark dataset and pre-trained language model for Indonesian NLP,” in Proc. 28th International Conference on Computational Linguistics, 2020, pp. 757–770.
[12] T. Wolf et al., “Transformers: State-of-the-Art Natural Language Processing,” in Proc. 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 2020, pp. 38–45, doi: 10.18653/v1/2020.emnlp-demos.6.
[13] Hugging Face, “Text Classification,” Hugging Face Inference Providers Documentation, 2026. [Online]. Available: https://huggingface.co/docs/inference-providers/en/tasks/text-classification. [Accessed: Jun. 20, 2026].
[14] Hugging Face, “Model Cards,” Hugging Face Hub Documentation, 2026. [Online]. Available: https://huggingface.co/docs/hub/en/model-cards. [Accessed: Jun. 20, 2026].
[15] I. Daqiqil, H. Saputra, Syamsudhuha, R. Kurniawan, and Y. Andriyani, “Sentiment analysis of student evaluation feedback using transformer-based language models,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 36, no. 2, pp. 1127–1139, 2024, doi: 10.11591/ijeecs.v36.i2.pp1127-1139.
[16] D. G. Mandhasiya, H. Murfi, and A. Bustamam, “The hybrid of BERT and deep learning models for Indonesian sentiment analysis,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 33, no. 1, pp. 591–602, 2024, doi: 10.11591/ijeecs.v33.i1.pp591-602.
[17] H. Jayadianti, W. Kaswidjanti, A. T. Utomo, S. Saifullah, F. A. Dwiyanto, and R. Drezewski, “Sentiment analysis of Indonesian reviews using fine-tuning IndoBERT and R-CNN,” ILKOM Jurnal Ilmiah, vol. 14, no. 3, pp. 348–354, 2022, doi: 10.33096/ilkom.v14i3.1505.348-354.
[18] M. Pratiwi, L. Mayola, K. H. Laoli, V. U. Ilhami Arsyah, and N. Pratiwi, “Medical Record Information System with Rapid Application Development (RAD) Method,” Journal of Information Systems and Technology Research, vol. 1, no. 2, pp. 124–130, 2022, doi: 10.55537/jistr.v1i2.170.
[19] M. A. Fauzi, H. Tribiakto, A. Moniva, F. Amir, I. K. Ilyas, and E. Utami, “Systematic Literature Reviews on Rapid Application Development Information System,” Bulletin of Computer Science and Electrical Engineering, vol. 4, no. 1, pp. 57–64, 2023, doi: 10.25008/bcsee.v4i1.1181.
[20] H. Pangestu, A. Azis, and A. Prasetyo, “Website Based School Information System at SMP Negeri 4 Kemangkon using Rapid Application Development Method,” JINAV: Journal of Information and Visualization, 2024, doi: 10.35877/454RI.jinav2340.
[21] S. Supriyono, “Software Testing with the approach of Blackbox Testing on the Academic Information System,” International Journal of Information System and Technology, 2020.
[22] S. Ismiati, F. P. Aditiawan, and A. L. Nurlaili, “Black Box Testing with the Equivalence Partitioning and Cause Effect Graph Method in Archive Information System,” Jurnal Teknik Informatika (JUTIF), vol. 5, no. 4, pp. 981–990, 2024, doi: 10.52436/1.jutif.2024.5.4.1944.
[23] T. Gebru et al., “Datasheets for datasets,” Communications of the ACM, vol. 64, no. 12, pp. 86–92, 2021, doi: 10.1145/3458723.
[24] Hugging Face, “Dataset Cards,” Hugging Face Hub Documentation, 2026. [Online]. Available: https://huggingface.co/docs/hub/datasets-cards. [Accessed: Jan. 20, 2026].