Development of the Andana Mobile Application: An Interactive Japanese Language Learning Platform Using Flutter and Firebase

Authors

  • Sovantri Putra Paskah Halawa Universitas Negeri Medan, Sumatera Utara, Indonesia
  • Rizal Muslim Sinaga Universitas Negeri Medan, Sumatera Utara, Indonesia https://orcid.org/0009-0008-4277-8175
  • Agata Putri Handayani Simbolon Universitas Negeri Medan, Sumatera Utara, Indonesia
  • Selfi Audy Priscilia Universitas Negeri Medan, Sumatera Utara, Indonesia
  • Adidtya Perdana Universitas Negeri Medan, Sumatera Utara, Indonesia https://orcid.org/0000-0003-0326-9892

DOI:

https://doi.org/10.51903/0m972c26

Keywords:

Flutter, Firebase, Japanese Language Learning, Mobile Application, Mobile Learning

Abstract

The growing interest among Indonesian learners in the Japanese language has led to an increased demand for more interactive and flexible learning media. However, most existing applications remain limited to static material delivery without adaptive progress tracking. This study aims to develop Andana, a mobile learning platform for Japanese language education built with Flutter and Firebase. The research adopts a Research and Development (R&D) method with a software engineering approach, encompassing requirement analysis, system design, implementation, and functional testing. Data were collected from functional trials and user-based performance evaluations. Andana supports two leading roles—user and admin—and integrates multimedia-based learning features (PDF, video, audio), flashcards, listening and speaking exercises, and personalized note-taking. The results indicate that all system functionalities operate effectively, with real-time data synchronization and a responsive user interface. The integration of Flutter and Firebase enables dynamic data management and seamless content updates. This study contributes to the development of adaptive, cloud-based learning applications that are easy to maintain and expand. The findings imply that Andana can serve as an efficient and engaging medium for Japanese language learning, with strong potential for adaptation to other foreign languages in the future.

References

[1] K. Mohiuddin, H. Fatima, M. A. Khan, M. A. Khaleel, O. A. Nasr, and S. Shahwar, “Mobile Learning Evolution and Emerging Computing Paradigms: An Edge-Based cloud Architecture for Reduced Latencies and Quick Response Time,” Array, vol. 16, p. 100259, Dec. 2022, doi: 10.1016/j.array.2022.100259.

[2] A. ( Shahab, H. Jia, R. Nacheva, M. Bakaev, R. I. Maxim, and J. Arnedo-Moreno, “EDTF: A User-Centered Approach to Digital Educational Games Design and Development,” Information, vol. 16, no. 9, p. 794, Sep. 2025, doi: 10.3390/info16090794.

[3] C. Song, S. Y. Shin, and K. S. Shin, “Implementing the Dynamic Feedback-Driven Learning Optimization Framework: A Machine Learning Approach to Personalize Educational Pathways,” Appl. Sci., vol. 14, no. 2, p. 916, Jan. 2024, doi: 10.3390/app14020916.

[4] V. N. Santhosh, D. Coutinho, A. V. Ankola, Y. K. Parimala, S. Shankkari, and K. Ragu, “Effectiveness of Spaced Repetition Learning Using a Mobile Flashcard Application Among Dental Students: A Randomized Controlled Trial,” J. Dent. Educ., vol. 88, no. 9, pp. 1267–1276, Sep. 2024, doi: 10.1002/jdd.13561.

[5] A. Alam and A. Mohanty, “Educational Technology: Exploring the convergence of Technology and Pedagogy Through Mobility, Interactivity, AI, and Learning Tools,” Cogent Eng., vol. 10, no. 2, p. 2283282, Dec. 2023, doi: 10.1080/23311916.2023.2283282.

[6] T. Shaik et al., “A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis,” IEEE Access, vol. 10, pp. 56720–56739, 2022, doi: 10.1109/access.2022.3177752.

[7] G. Jošt and V. Taneski, “State-of-the-Art Cross-Platform Mobile Application Development Frameworks: A Comparative Study of Market and Developer Trends,” Informatics, vol. 12, no. 2, p. 45, Apr. 2025, doi: 10.3390/informatics12020045.

[8] M. Asadullah, M. Yeasmin, A. F. Alam, A. Alsolami, N. Ahmad, and I. Atoum, “Towards a Sustainable Future: A Systematic Review of Mobile Learning and Studies in Higher Education,” Sustainability, vol. 15, no. 17, p. 12847, Aug. 2023, doi: 10.3390/su151712847.

[9] S. Figueiredo, “The Effect of Mobile-Assisted Learning in Real Language Attainment: A Systematic Review,” J. Comput. Assist. Learn., vol. 39, no. 4, pp. 1083–1102, Aug. 2023, doi: 10.1111/jcal.12811.

[10] T. Inoue, Y. Chen, and T. Ohyanagi, “Assessing Handwriting Skills in a Web Browser: Development and Validation of an Automated Online Test in Japanese Kanji,” Behav. Res. Methods, vol. 57, no. 1, pp. 1–17, Dec. 2024, doi: 10.3758/s13428-024-02562-6.

[11] C. M. Chen, L. Ming-Chaun, and C. P. Kuo, “A Game-Based Learning System Based on Octalysis Gamification Framework to Promote Employees’ Japanese Learning,” Comput. Educ., vol. 205, p. 104899, Nov. 2023, doi: 10.1016/j.compedu.2023.104899.

[12] Q. Zhou, J. Zhang, and Z. Zhu, “Evaluating Urban Visual Attractiveness Perception Using Multimodal Large Language Model and Street View Images,” Buildings, vol. 15, no. 16, p. 2970, Aug. 2025, doi: 10.3390/buildings15162970.

[13] H. Ren, W. Wei, and W. Zhang, “The Influencing Factors on the Application of Information Technology Among Japanese Language Teachers in Chinese Universities,” Asia-Pacific Educ. Res., vol. 34, no. 2, pp. 825–841, Aug. 2024, doi: 10.1007/s40299-024-00899-2.

[14] M. C. Demirdag, S. Kucuk, and A. Tasgin, “An Investigation of the Effectiveness of Augmented Reality Technology Supported English Language Learning Activities on Preschool Children,” Int. J. Hum. Comput. Interact., vol. 41, no. 4, pp. 2410–2423, 2025, doi: 10.1080/10447318.2024.2323278.

[15] W. Liu et al., “Location- and Physical-Activity-Based Application for Japanese Vocabulary Acquisition for Non-Japanese Speakers,” Multimodal Technol. Interact., vol. 7, no. 3, p. 29, Mar. 2023, doi: 10.3390/mti7030029.

[16] -K ; Lo, C.-K. Flipped, L.-K. Ng, and C.-K. Lo, “Flipped Classroom and Gamification Approach: Its Impact on Performance and Academic Commitment on Sustainable Learning in Education,” Sustainability, vol. 14, no. 9, p. 5428, Apr. 2022, doi: 10.3390/su14095428.

[17] C. Halkiopoulos and E. Gkintoni, “Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis,” Electronics, vol. 13, no. 18, p. 3762, Sep. 2024, doi: 10.3390/electronics13183762.

[18] N. El-Haggar, L. Amouri, A. Alsumayt, F. H. Alghamedy, and S. S. Aljameel, “The Effectiveness and Privacy Preservation of IoT on Ubiquitous Learning: Modern Learning Paradigm to Enhance Higher Education,” Appl. Sci., vol. 13, no. 15, p. 9003, Aug. 2023, doi: 10.3390/app13159003.

[19] M. Abdollahi, S. F. Yeganli, M. Baharloo, and A. Baniasadi, “Hardware Design and Verification with Large Language Models: A Scoping Review, Challenges, and Open Issues,” Electronics, vol. 14, no. 1, p. 120, Jan. 2025, doi: 10.3390/electronics14010120.

[20] K. I. Manik, R. Wahyudi, F. Amanah, M. Y. Noor, and A. Perdana, “Implementasi Flutterflow dan Firebase Firestore dalam Pengembangan Aplikasi Manajemen Keuangan Berbasis No-Code,” JATI (Jurnal Mhs. Tek. Inform., vol. 9, no. 4, pp. 5929–5935, May 2025, doi: 10.36040/jati.v9i4.13961.

[21] N. Nikhlis, M. Jamal, and U. Ghofur, “Deteksi Dini Pencurian Data pada Perangkat Seluler Menggunakan Machine Learning,” J. Ilm. Sist. Inf., vol. 4, no. 2, pp. 110–129, May 2025, doi: 10.51903/fz0xzt26.

[22] M. Ur Rehman, S. Kuhn, E. Dritsas, and M. Trigka, “A Survey on the Applications of Cloud Computing in the Industrial Internet of Things,” Big Data Cogn. Comput., vol. 9, no. 2, p. 44, Feb. 2025, doi: 10.3390/bdcc9020044.

[23] S. Cazco et al., “Optimization of Emergency Notification Processes in University Campuses Through Multiplatform Mobile Applications: A Case Study,” Computers, vol. 14, no. 11, p. 453, Oct. 2025, doi: 10.3390/computers14110453.

[24] A. S. Hakim, P. J. Mustaqim, and A. Naufal, “Peran Pendidikan Digital dalam Melindungi Privasi Pengguna dan Mencegah Dampak Sosial,” J. Ilm. Sist. Inf., vol. 4, no. 2, pp. 162–174, May 2025, doi: 10.51903/k9cvkc90.

[25] S. U. Anggono and S. Supriyanto, “Peluang dan Tantangan Sistem Pelatihan Berbasis AI dalam Meningkatkan Keterampilan Tenaga Kerja di Era Digital,” J. Ilm. Sist. Inf., vol. 4, no. 2, pp. 224–231, May 2025, doi: 10.51903/6mbmzk12.

Downloads

Published

2025-11-12

Issue

Vol. 4 No. 3 (2025): November: Jurnal Ilmiah Sistem Informasi

Section

Articles

License

Copyright (c) 2025 Sovantri Putra Paskah Halawa, Rizal Muslim Sinaga, Agata Putri Handayani Simbolon, Selfi Audy Priscilia, Adidtya Perdana

Creative Commons License

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

All articles published in this journal are open access and licensed under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).

Under this license:

  • Authors retain copyright of their work.

  • Others are free to:

    • Share — copy and redistribute the material in any medium or format;

    • Adapt — remix, transform, and build upon the material for any purpose, even commercially;

  • As long as they:

    • Give appropriate credit to the original author(s) and the source;

    • Provide a link to the license;

    • Indicate if changes were made;

    • Distribute their contributions under the same license as the original.

This ensures that derivative works will also remain free and open under the same terms.