Studi Implementasi Aplikasi Keuangan Android dengan Analisis Data

Authors

  • Rizki Dwi Sanjaya Universitas Esa Unggul
  • Binastya Anggara Sekti Universitas Esa Unggul

Keywords:

Personal Financial Management, Mobile Application, Android, Data Analysis, Transaction Recording

Abstract

In this modern era, efficient personal financial management tools are increasingly needed. Applications for Android-based mobile phones offer an efficient and easy-to-use method of managing personal finances. The aim of this research is to create and implement a personal financial management application that has data analysis features that help people manage income, expenses and savings. The app has features such as transaction recording, financial reporting, and data analysis, which provides information on spending patterns and recommendations for reducing costs. This study uses user interface design, application development using Android Studio, and application testing with end users. Studies show that these apps can help people make better financial decisions and better understand their financial condition. This research helps develop mobile applications by including data analysis features that help users manage finances.

References

S. Rudregowda, S. Patilkulkarni, V. Ravi, G. H.L., and M. Krichen, “Audiovisual speech recognition based on a deep convolutional neural network,” Data Science and Management, vol. 7, no. 1, pp. 25–34, Mar. 2024, doi: 10.1016/j.dsm.2023.10.002.

K. Robindro, S. S. Devi, U. B. Clinton, L. Takhellambam, Y. R. Singh, and N. Hoque, “Hybrid distributed feature selection using particle swarm optimization-mutual information,” Data Science and Management, vol. 7, no. 1, pp. 64–73, Mar. 2024, doi: 10.1016/j.dsm.2023.10.003.

X. Gao, A. Insuwan, Z. Li, and S. Tian, “The dynamics of price discovery between the U.S. and Chinese soybean market: A wavelet approach to understanding the effects of Sino-US trade conflict and COVID-19 pandemic,” Data Science and Management, vol. 7, no. 1, pp. 35–46, Mar. 2024, doi: 10.1016/j.dsm.2023.10.004.

M. Ma and E. Pinsky, “Using machine learning to identify primary features in choosing electric vehicles based on income levels,” Data Science and Management, vol. 7, no. 1, pp. 1–6, Mar. 2024, doi: 10.1016/j.dsm.2023.10.001.

M. Soori, B. Arezoo, and R. Dastres, “Virtual manufacturing in Industry 4.0: A review,” Mar. 01, 2024, KeAi Communications Co. doi: 10.1016/j.dsm.2023.10.006.

F. Zhang, Y. Zhang, Y. Xu, and Y. Chen, “Dynamic relationship between volume and volatility in the Chinese stock market: evidence from the MS-VAR model,” Data Science and Management, vol. 7, no. 1, pp. 17–24, Mar. 2024, doi: 10.1016/j.dsm.2023.09.003.

M. A. Uddin et al., “Data-driven strategies for digital native market segmentation using clustering,” International Journal of Cognitive Computing in Engineering, vol. 5, pp. 178–191, Jan. 2024, doi: 10.1016/j.ijcce.2024.04.002.

P. P. Singh, F. I. Anik, R. Senapati, A. Sinha, N. Sakib, and E. Hossain, “Investigating customer churn in banking: A machine learning approach and visualization app for data science and management,” Data Science and Management, vol. 7, no. 1, pp. 7–16, Mar. 2024, doi: 10.1016/j.dsm.2023.09.002.

V. Vajrobol, B. B. Gupta, A. Gaurav, and H. M. Chuang, “Adversarial learning for Mirai botnet detection based on long short-term memory and XGBoost,” International Journal of Cognitive Computing in Engineering, vol. 5, pp. 153–160, Jan. 2024, doi: 10.1016/j.ijcce.2024.02.004.

B. Abdellaoui et al., “Analyzing emotions in online classes: Unveiling insights through topic modeling, statistical analysis, and random walk techniques,” International Journal of Cognitive Computing in Engineering, vol. 5, pp. 221–236, Jan. 2024, doi: 10.1016/j.ijcce.2024.05.003.

P. Krishnamoorthy, M. Sathiyanarayanan, and H. P. Proença, “A novel and secured email classification and emotion detection using hybrid deep neural network,” International Journal of Cognitive Computing in Engineering, vol. 5, pp. 44–57, Jan. 2024, doi: 10.1016/j.ijcce.2024.01.002.

M. A. Uddin et al., “Deep learning-based human activity recognition using CNN, ConvLSTM, and LRCN,” International Journal of Cognitive Computing in Engineering, vol. 5, pp. 259–268, Jan. 2024, doi: 10.1016/j.ijcce.2024.06.004.

R. Mohawesh, H. Bany Salameh, Y. Jararweh, M. Alkhalaileh, and S. Maqsood, “Fake review detection using transformer-based enhanced LSTM and RoBERTa,” International Journal of Cognitive Computing in Engineering, vol. 5, pp. 250–258, Jan. 2024, doi: 10.1016/j.ijcce.2024.06.001.

V. Mahalakshmi, P. Shenbagavalli, S. Raguvaran, V. Rajakumareswaran, and E. Sivaraman, “Twitter sentiment analysis using conditional generative adversarial network,” International Journal of Cognitive Computing in Engineering, vol. 5, pp. 161–169, Jan. 2024, doi: 10.1016/j.ijcce.2024.03.002.

V. KP, R. AB, G. HL, V. Ravi, and M. Krichen, “A tweet sentiment classification approach using an ensemble classifier,” International Journal of Cognitive Computing in Engineering, vol. 5, pp. 170–177, Jan. 2024, doi: 10.1016/j.ijcce.2024.04.001.

B. S, J. A. D, P. N. Renjith, and K. Ramesh, “DDSS: Driver decision support system based on the driver behaviour prediction to avoid accidents in intelligent transport system,” International Journal of Cognitive Computing in Engineering, vol. 5, pp. 1–13, Jan. 2024, doi: 10.1016/j.ijcce.2023.12.001.

M. K. H. Kanchon, M. Sadman, K. F. Nabila, R. Tarannum, and R. Khan, “Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies,” International Journal of Cognitive Computing in Engineering, vol. 5, pp. 269–278, Jan. 2024, doi: 10.1016/j.ijcce.2024.06.002.

C. Zhang, J. Zhang, W. Li, O. Castillo, and J. Zhang, “Exploring static rebalancing strategies for dockless bicycle sharing systems based on multi-granularity behavioral decision-making,” International Journal of Cognitive Computing in Engineering, vol. 5, pp. 27–43, Jan. 2024, doi: 10.1016/j.ijcce.2024.01.001.

S. Neethirajan, “From predictive analytics to emotional recognition–The evolving landscape of cognitive computing in animal welfare,” Jan. 01, 2024, KeAi Communications Co. doi: 10.1016/j.ijcce.2024.02.003.

N. M. Lopes, M. Aparicio, and F. T. Neves, “Knowledge mapping analysis of situational awareness and aviation: A bibliometric study,” Jan. 01, 2024, KeAi Communications Co. doi: 10.1016/j.ijcce.2024.06.003.

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Published

2024-10-27

How to Cite

Rizki Dwi Sanjaya, & Binastya Anggara Sekti. (2024). Studi Implementasi Aplikasi Keuangan Android dengan Analisis Data. Prosiding SISFOTEK, 8(1), 433 -437. Retrieved from http://seminar.iaii.or.id/index.php/SISFOTEK/article/view/527

Issue

Vol. 8 No. 1 (2024): SISFOTEK VIII 2024

Section

Sistem Informasi dan Teknologi

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Copyright (c) 2024 Seminar Nasional Sistem Informasi dan Teknologi (SISFOTEK)

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