DESIGNING AN INTERACTIVE DIGITAL ATTENDANCE SYSTEM WITH SCANNING BARCODES AND WHATSAPP NOTIFICATIONS FOR STUDENTS AT PRIVATE VOCATIONAL SCHOOL DWIWARNA MEDAN USING THE RAD METHOD
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Abstract
The rapid development of information technology has encouraged its application in various fields, including education. One of the most important administrative activities in schools is the student attendance process. However, attendance systems that are still conducted manually have several weaknesses, such as the risk of data manipulation, slow recap processes, and low efficiency in monitoring student attendance. Therefore, a more modern, effective, and accurate attendance system is needed. This study aims to design an interactive digital attendance system using barcode scanning technology and WhatsApp notifications at Dwiwarna Private Vocational High School, Medan. The system allows students to record their attendance quickly by scanning a unique barcode and provides real-time attendance information to parents or guardians through WhatsApp notifications. The system development method used in this research is Rapid Application Development (RAD), which emphasizes fast development, prototype-based processes, and active user involvement at every stage. The result of this study is a web-based attendance system that is able to improve the efficiency of attendance recording, minimize errors and fraud, and strengthen communication between the school and students’ parents. With the implementation of this system, it is expected that student attendance data management at Dwiwarna Private Vocational High School, Medan can be carried out more optimally, transparently, and accurately.
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[2] B. Hidayat, N. Rahmawati, & S. Putri. (2022). Analysis of e-learning effectiveness using technology acceptance model during post-pandemic learning. International Journal of Emerging Technologies in Learning, 17(11), 201–214. https://doi.org/10.3991/ijet.v17i11.30521
[3] C. Saputra, A. Nugroho, & Y. Firmansyah. (2022). Web-based inventory information system development using waterfall method. Journal of Computer Science and Information Technology, 10(3), 221–230. https://doi.org/10.31294/jcsi.v10i3.11221
[4] D. Wijaya & M. Sari. (2022). QR code implementation for digital attendance systems in educational institutions. Journal of Engineering and Applied Technology, 6(1), 55–63. https://doi.org/10.31764/jeat.v6i1.7845
[5] E. Ramadhan, T. Prakoso, & L. Handayani. (2022). Comparative analysis of Naïve Bayes and decision tree algorithms for sentiment analysis. Journal of Data Science and Artificial Intelligence, 4(2), 98–107. https://doi.org/10.33096/jdsa.v4i2.512
[6] F. Alamsyah & R. Putri. (2023). Design of smart campus systems using Internet of Things technology. International Journal of Advanced Computer Science and Applications, 14(4), 311–320. https://doi.org/10.14569/IJACSA.2023.0140438
[7] G. Santoso, N. Aulia, & P. Hidayat. (2023). Cyber security awareness among university students in Indonesia. Journal of Information Security Research, 5(1), 44–53. https://doi.org/10.1234/jisr.v5i1.2023
[8] H. Kurniawan, D. Lestari, & M. Yusuf. (2023). Development of mobile learning applications using agile methodology. Journal of Software Engineering and Technology, 11(2), 120–129. https://doi.org/10.15294/jset.v11i2.54210
[9] I. Fadillah & R. Hakim. (2023). Artificial intelligence applications in educational data mining: A systematic review. Education and Information Technologies, 28(5), 5521–5538. https://doi.org/10.1007/s10639-022-11345-2
[10] J. Maulana, S. Arifin, & K. Dewi. (2023). Cloud computing adoption in small and medium enterprises. Procedia Computer Science, 216, 332–340. https://doi.org/10.1016/j.procs.2022.12.145
[11] K. Nugraha & A. Wibowo. (2024). Analysis of blockchain technology implementation in digital transaction systems. Journal of Digital Innovation, 9(1), 77–88. https://doi.org/10.21070/jdi.v9i1.2024
[12] L. Purnamasari, T. Aditya, & F. Rizki. (2024). Machine learning-based prediction system for network intrusion detection. International Journal of Network Security, 26(2), 180–190. https://doi.org/10.6633/IJNS.202403_26(2).05
[13] M. Akbar & S. Hidayati. (2024). Evaluation of information system usability using system usability scale method. Journal of Information Technology Education, 23, 67–79. https://doi.org/10.28945/5210
[14] N. Cahyani, A. Saputro, & R. Wahyudi. (2024). Big data analytics for business intelligence in retail industries. Data Technologies and Applications, 58(3), 412–425. https://doi.org/10.1108/DTA-11-2023-0451
[15] O. Permata & D. Gunawan. (2024). Implementation of deep learning algorithms for image classification systems. Journal of Artificial Intelligence Research, 15(4), 210–220. https://doi.org/10.1613/jair.1.14521
[16] P. Siregar, M. Yani, & H. Fauzi. (2025). Sentiment analysis of social media using support vector machine algorithm. Journal of Computer Engineering and Informatics, 13(1), 91–101. https://doi.org/10.30865/jcei.v13i1.2025
[17] Q. Rahmatullah & E. Fitriani. (2025). Development of AI chatbot services for academic administration systems. International Journal of Interactive Mobile Technologies, 19(2), 88–99. https://doi.org/10.3991/ijim.v19i02.51220
[18] R. Wijaksana, T. Firmansyah, & Y. Prabowo. (2025). Internet of Things-based smart monitoring system for server management. Journal of Network and Systems Management, 33(1), 1–14. https://doi.org/10.1007/s10922-025-09721-4
[19] S. Amelia & D. Kusuma. (2025). Digital transformation strategies in higher education institutions. Education Sciences, 15(3), 145–158. https://doi.org/10.3390/educsci15030145
[20] T. H. Saputra, A. Maulana, & L. K. Dewi. (2025). Optimization of cybersecurity systems using penetration testing methods. Journal of Cyber Security Technology, 9(2), 134–146. https://doi.org/10.1080/23742917.2025.1021456
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