Implementation of AI-Based Natural Language Processing (NLP) for Automatic Meeting Minutes and Summarization Using Voice-to-Text on Mobile Applications
DOI:
https://doi.org/10.35870/ijsecs.v5i3.5459Keywords:
Natural Language Processing, Voice-to-Text, Automatic Summarization, Automatic Meeting Minutes, Mobile Application, Gemini APIAbstract
Manual recording of the meeting minutes is often ineffective and has a high error rate. It makes access to needed information not fast. This research tries to solve these problems by designing and developing an end-to-end mobile application called Notulen.AI, which combines voice-to-text and Natural Language Processing (NLP) to automatically generate meeting minutes and summaries. The application was developed using the Flutter framework, with the Google Gemini API mainly used as the Automatic Speech Recognition (ASR) service and for NLP analysis. The research methodology consists of system design, module implementation, and testing to see performance and effectiveness. Testing on ASR modules gets good results with an average Word Error Rate (WER) of 6.75%. The NLP module also works well in extracting important information with ROUGE-1 scoring at 0.78 and F1-Score at 0.85. Effectiveness testing involving five respondents showed that this application could reduce minute-taking time by up to 70% and got a System Usability Scale (SUS) score of 85.5, which indicates high user acceptance. This research therefore proves that the integration of ASR and NLP on a mobile platform can be an efficient solution to enhance documentation accuracy in meetings.
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Iskandar Mulyana, D., Yel, M. B., Rahmanto, M. D., & Tinggi Ilmu Komputer Cipta Karya Informatika, S. (2023). Optimasi penerapan metode text recognition dalam fitur catatan otomatis berbasis mobile. Jurnal Riset Sistem Informasi Dan Teknik Informatika (JURASIK), 8(2), 489–498. https://tunasbangsa.ac.id/ejurnal/index.php/jurasik
Saiyyad, M. M., & Patil, N. N. (2023). Text summarization using deep learning techniques: A review. Engineering Proceedings, 59(1). https://doi.org/10.3390/engproc2023059194
Kumar, N. S., Devasena, G., & Kumar, S. V. (2024). Voice to text summarization using NLP. International Journal on Computational Modelling Applications, 1(2), 1-9. https://doi.org/10.63503/j.ijcma.2024.25
Syaifullah, M. I. (2024). Penerapan teknologi automatic speech recognition menggunakan model Wav2Vec2.0 sebagai alat bantu untuk mendeteksi kesalahan dalam membaca Al-Qur’an berbasis mobile. JSI: Jurnal Sistem Informasi, 16(2).
Jaman, A. B., & Fergina, A. (2021). Implementasi speech recognition berbasis Android dalam optimalisasi komunikasi bagi penyandang tunarungu.
Uppalapati, P. J., Dabbiru, M., & Rao, K. V. (2023). A comprehensive survey on summarization techniques. SN Computer Science, 4(5). https://doi.org/10.1007/s42979-023-02007-5
Sudrajat, M. R., & Zakariyah, M. (2024). Penerapan Natural Language Processing dan Machine Learning untuk prediksi stres siswa SMA berdasarkan analisis teks. Building of Informatics, Technology and Science (BITS), 6(3). https://doi.org/10.47065/bits.v6i3.6180
Russell, S. O. C., Gessinger, I., Krason, A., Vigliocco, G., & Harte, N. (2024). What automatic speech recognition can and cannot do for conversational speech transcription. Research Methods in Applied Linguistics, 3(3). https://doi.org/10.1016/j.rmal.2024.100163
Citarella, A. A., Barbella, M., Ciobanu, M. G., De Marco, F., Di Biasi, L., & Tortora, G. (2025). Assessing the effectiveness of ROUGE as an unbiased metric in extractive vs. abstractive summarization techniques. Journal of Computational Science, 87, 102571. https://doi.org/10.1016/j.jocs.2025.102571
Arriaga, C., Pozo, A., Conde, J., & Alonso, A. (2024). Evaluation of real-time transcriptions using end-to-end ASR models. https://arxiv.org/abs/2409.05674
Liu, H., Liu, H., Wang, X., Shao, W., Wang, X., Du, J., & Salim, F. D. (2020). Smartmeeting: A novel mobile voice meeting minutes generation and analysis system. Mobile Networks and Applications, 25(2), 521-536. https://doi.org/10.1007/s11036-019-01310-x
Patel, I., Julka, V., Kudtarkar, P., Sharma, I., & Sonawane, B. (2023, August). Online meeting summarization based on text and image processing. In International Conference on ICT for Sustainable Development (pp. 193-205). Springer Nature Singapore. https://doi.org/10.1007/978-981-99-5652-4_19
Chen, X., Sheng, F., He, R., Chen, S., Ma, H., Wu, Y., & Xu, J. (2021, August). Yanji: An automated mobile meeting minutes system. In 2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC) (pp. 425-428). IEEE. https://doi.org/10.1109/ISCEIC53685.2021.00095
Khan, M. A. A., AlAyat, M., AlGhamdi, J., AlOtaibi, S. M., AlZahrani, M., AlQahtani, M., ... & Jan, F. (2022, July). WeScribe: An intelligent meeting transcriber and analyzer application. In Proceedings of Third International Conference on Computing, Communications, and Cyber-Security: IC4S 2021 (pp. 755-766). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-1142-2_59
Muppidi, S., Kandi, J., Kondaka, B. S., Kethireddy, C., & Kandregula, S. E. (2023, October). Automatic meeting minutes generation using Natural Language processing. In 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT) (pp. 1-7). IEEE. https://doi.org/10.1109/EASCT59475.2023.10393102
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Copyright (c) 2025 Muhammad Umar Hamidi Yusuf, Dadang Iskandar Mulyana
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