Desain Website Penjualan Grosir dengan Optimasi Random Captcha dan Google Oauth
Keywords:
Wholesale Sales Information System, Random captcha, Google OAuth, Security of Online Transaction SystemsAbstract
[Design and Development of a Wholesale Sales Website with Random CAPTCHA Optimization and Google OAuth Integration] The development of information technology, especially the internet, has significantly impacted business activities, including the trade sector. Berkah Dua Saudara Wholesale Store still uses conventional sales and promotion methods, resulting in limited marketing reach and inefficient transaction processes, particularly for customers located far from the store. In addition, the lack of adequate security systems poses risks of unauthorized access.
This study aims to design and develop a web-based wholesale sales information system capable of managing online transactions and promotions. The system implements Random CAPTCHA to prevent bot attacks and Google OAuth as a secure and efficient authentication method.
The research method includes literature study, system design, implementation, and testing using Black Box Testing. The results show that the system improves transaction efficiency, expands marketing reach, and enhances system security.
Downloads
References
[1]H. Zhao, “Instance weighting versus threshold adjusting for cost-sensitive classification,” Knowl Inf Syst, vol. 15, no. 3, pp. 321–334, 2008, doi: 10.1007/s10115-007-0079-1.
[2]V. Komisarenko and M. Kull, “Cost-sensitive classification with cost uncertainty: do we need surrogate losses?,” Mach Learn, vol. 114, no. 6, p. 132, 2025, doi: 10.1007/s10994-024-06634-8.
[3]Z. Sadeghi et al., “A review of Explainable Artificial Intelligence in healthcare,” Computers and Electrical Engineering, vol. 118, p. 109370, Aug. 2024, doi: 10.1016/j.compeleceng.2024.109370.
[4]A. A. Soladoye, N. Aderinto, M. R. Popoola, I. A. Adeyanju, A. Osonuga, and D. B. Olawade, “Machine learning techniques for stroke prediction: A systematic review of algorithms, datasets, and regional gaps,” Int J Med Inform, vol. 203, p. 106041, Nov. 2025, doi: 10.1016/j.ijmedinf.2025.106041.
[5]O. Olabanjo, A. Wusu, O. Afisi, and B. Akinnuwesi, “Stroke Risk Factor Prediction Using Machine Learning Techniques: A Systematic Review,” Journal of Applied Sciences, vol. 24, no. 1, pp. 1–15, Jan. 2024, doi: 10.3923/jas.2024.1.15.
[6]K. Moulaei, L. Afshari, R. Moulaei, B. Sabet, S. M. Mousavi, and M. R. Afrash, “Explainable artificial intelligence for stroke prediction through comparison of deep learning and machine learning models,” Sci Rep, vol. 14, no. 1, p. 31392, 2024, doi: 10.1038/s41598-024-82931-5.
[7]M. Hassanzad and K. Hajian-Tilaki, “Methods of determining optimal cut-point of diagnostic biomarkers with application of clinical data in ROC analysis: an update review,” BMC Med Res Methodol, vol. 24, no. 1, p. 84, Apr. 2024, doi: 10.1186/s12874-024-02198-2.
[8]R. Pan, Z. Zhang, Y. Fan, J. Cao, K. Lu, and T. Yang, “Multi-objective optimization method for learning thresholds in a decision-theoretic rough set model,” International Journal of Approximate Reasoning, vol. 71, pp. 34–49, 2016, doi: https://doi.org/10.1016/j.ijar.2016.01.002.
[9]E. W. Steyerberg et al., “Assessing the Performance of Prediction Models,” Epidemiology, vol. 21, no. 1, pp. 128–138, Jan. 2010, doi: 10.1097/EDE.0b013e3181c30fb2.
[10]R. D. Riley et al., “Evaluation of clinical prediction models (part 2): how to undertake an external validation study,” BMJ, vol. 384, p. e074820, Jan. 2024, doi: 10.1136/bmj-2023-074820.
[11]F. Asadi, M. Rahimi, A. H. Daeechini, and A. Paghe, “The most efficient machine learning algorithms in stroke prediction: A systematic review,” Health Sci Rep, vol. 7, no. 10, Oct. 2024, doi: 10.1002/hsr2.70062.
[12]I. Muslem and D. Armiady, “A PSO-Based CVRPPD Model with Weather and Traffic Constraints for Two-Wheeled Urban Delivery in Indonesia,” Instal: Jurnal Komputer, vol. 17, pp. 281–290, 2025, doi: 10.54209/jurnalinstall.v17i05.388.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Jurnal Ilmu Komputer Aceh
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This journal is an open access journal that provides direct, barrier-free national access to the full text of all published articles without any cost to readers or their institutions. Readers are entitled to read, download, copy, distribute, print, search, or link to the full text of all articles in the ILKA Journal. This journal provides open access to its content on the principle that making research freely available to the public supports greater global knowledge exchange.
