Receipt Scanning with EasyOCR and ChatGPT-4o in a Mobile Finance App: an Agile Kanban Approach
DOI:
https://doi.org/10.70609/g-tech.v9i4.7822Keywords:
Agile Kanban, Finance Tracking, Generation Z, OCR, Receipt ScanningAbstract
Technological advancements have provided convenience for Generation Z in managing finances; however, many are still not accustomed to recording their financial activities regularly. Shopping receipts, which should serve as proof of transactions, are often ignored or poorly managed, despite their important role in tracking expenses. Therefore, this research aims to develop an Android-based financial recording application capable of handling both manual input and automated recording through receipt scanning using Optical Character Recognition (OCR) technology. The findings indicate that ChatGPT-4o significantly outperforms EasyOCR by providing more consistent accuracy and faster, stable processing, making it a more reliable solution for receipt-based financial recording. Developed using the Agile Kanban method, the application was validated through alpha testing and proven to function properly across all features. Beyond practical benefits for users, this research also contributes to the financial technology literature by demonstrating the integration of large language models (LLM) to enhance OCR performance in mobile finance applications.
References
Alaidaros, H., Omar, M., Romli, R. B., & Romli, R. (2020). Developing an Improved Model of the Agile Kanban Method (i-KAM): A Research Design and Preliminary Results. International Journal of Multidisciplinary Sciences and Advanced Technology, 1(10), 32–38. https://www.ijmsat.com/archives/ijmsat-volume-1-issue-10
Anthony, Herman, & Yulianto, A. (2024). Pengembangan Sistem Pengenalan Plat Nomor Indonesia Menggunakan YOLOv8 dan EasyOCR. Jurnal Ilmiah Komputasi, 23(4), 571–578. https://doi.org/10.32409/jikstik.23.4.3659
Asrun, N. A., & Gunawan, A. (2024). Pengaruh Gaya Hidup dan Media Sosial terhadap Perilaku Konsumtif Generasi Z di Kota Medan dengan Literasi Keuangan sebagai Media Intervening. Jurnal Manajemen Bisnis Dan Keuangan, 5(1), 173–186. https://doi.org/10.51805/jmbk.v5i1.205
Astuti, Y., & Wicaksana, K. K. (2018). Rancang Bangun Sistem Pemindaian Struk Belanja untuk Mendapatkan Rincian Belanja. Seminar Nasional Teknologi Informasi Dan Multimedia, 6(1), 37–42. https://ojs.amikom.ac.id/index.php/semnasteknomedia/article/view/2111
Bardvall, M., & Hassle, I. (2024). Automating Invoice Recognition : A Comparative Study of Large Language Models and OCR/ML Technologies [KTH Royal Institute of Technology]. https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-351177
Echchakoui, S., & Heppell, H. (2023). Financial Literacy: A Bibliometric Literature Review. Advances in Management and Applied Economics, 13(3), 129–161. https://doi.org/10.47260/amae/1335
Felia Putri, D., & Nurlaila, N. (2022). Analisis Sistem Pencatatan Manual Laporan Keuangan Terhadap Kinerja Akuntan Di Perusahaan Umum Daerah Pasar Kota Medan. SIBATIK JOURNAL: Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan, 1(6), 763–770. https://doi.org/10.54443/sibatik.v1i6.90
Garcia, M. B., & Claour, J. P. (2021). Mobile Bookkeeper: Personal Financial Management Application with Receipt Scanner Using Optical Character Recognition. 2021 1st Conference on Online Teaching for Mobile Education (OT4ME), 15–20. https://doi.org/10.1109/OT4ME53559.2021.9638794
Hakim, A., & Samsoni. (2024). Penerapan library Tesseract Optical Character Recognition dalam fitur reimbursement menggunakan metode Kanban (Studi Kasus: PT. Kayana Nusa Teknologi). 3(3), 600–607. https://www.journal.mediapublikasi.id/index.php/oktal/article/view/2434
Hartono, R. (2022). Penerapan Kanban Model Sebagai Metode Perancangan Sistem Informasi (Studi Kasus: Pemetaan Sekolah SMA/K/MA Kota Tasikmalaya). JURNAL PETIK, 8(1), 27–34. https://doi.org/10.31980/jpetik.v8i1.1252
JaidedAI. (2020). EasyOCR: Ready-to-use OCR with 80+ supported languages. GitHub. https://github.com/JaidedAI/EasyOCR
Kim, S., Baudru, J., Ryckbosch, W., Bersini, H., & Ginis, V. (2025). Early evidence of how LLMs outperform traditional systems on OCR/HTR tasks for historical records. http://arxiv.org/abs/2501.11623
Kosadi, F., Ginting, W., & Merliana, V. (2021). Digital Receipts of Online Transactions in the Reconciliation Process and the Preparation of Financial Reports. Journal of Indonesian Economy and Business, 36(1), 31–50. https://doi.org/10.22146/jieb.59884
Lohita, V. A. K., Suprapto, Wi., & Sahetapi, W. L. (2022). Generasi Z dalam Memanjakan Diri di Restoran All You Can Eat. Jurnal Ilmiah Manajemen Dan Bisnis (JIMBis), 1(2). https://doi.org/10.24034/jimbis.v1i2.5376
Matsui, A., Kobayashi, T., Moriwaki, D., & Ferrara, E. (2023). Detecting multi-timescale consumption patterns from receipt data: a non-negative tensor factorization approach. Journal of Computational Social Science, 6(2), 1179–1192. https://doi.org/10.1007/s42001-020-00078-5
S. Bhuvanapriya, K. K. Sreedeve, B. Vaidianathan, & S. Saranya. (2024). The Essential Role of Expense Tracking in Personal and Business Financial Management. American Journal of Economics and Business Management, 7(12), 1637–1655. https://doi.org/10.31150/ajebm.v7i12.3171
Sari, R., Musa Adi, I., & Hidayati, A. (2023). Personal Track Your Cash: Prototipe Aplikasi Pembacaan Setruk 0020 Belanja Menggunakan OCR dan Google Vision. Prosiding Seminar Inovasi Vokasi, 2(1), 565–573. https://prosiding.pnj.ac.id/sniv/article/view/436
Sowjanya, S., & Vijaya Chamundeeswari, V. (2024). Information Extraction Using RPA and Generative AI from Unstructured Documents: A Case of Invoices (pp. 250–264). https://doi.org/10.1007/978-3-031-69986-3_19
Ulfah. (2024). Pengaruh Literasi Keuangan terhadap Pengelolaan Keuangan Pribadi. Jurnal Manajemen, Akuntansi Dan Pendidikan (JAMAPEDIK), 1(2), 233–240. https://doi.org/10.59971/jamapedik.v1i2.53
Widiantara, I. G., & Romli, M. A. (2024). Bidirectional Long Short-Term Memory untuk Ekstraksi Informasi pada Struk Belanja. Voteteknika (Vocational Teknik Elektronika Dan Informatika), 12(4), 411. https://doi.org/10.24036/voteteknika.v12i4.130990
Zen, M., Irwan, Hafni, & Ananda, M. D. P. (2024). Implementasi dan Pengujian Menggunakan Metode BlackBox Testing Pada Sistem Informasi Tracer Study. Bulletin of Computer Science Research, 4(4), 327–340. https://doi.org/10.47065/bulletincsr.v4i4.359
Downloads
Published
Issue
Section
License
Copyright (c) 2025 M. Fiqry Septiawan, Siska Anraeni, Ramdaniah
This work is licensed under a Creative Commons Attribution 4.0 International License.
