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Joko Aryanto
Universitas Teknologi Yogyakarta, Sleman
Computer Science & IT

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Pengembangan Aplikasi Sistem Presensi Siswa dengan Integrasi Fitur Kehadiran Per Mata Pelajaran dan Izin Digital Arrum Junia Budiarti; Joko Aryanto
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.826

Abstract

Student attendance is a crucial aspect of school administrative management; however, the conventional paper-based system implemented at SMP Negeri 1 Giritontro has several limitations, such as the risk of data loss and delays in attendance recapitulation. This study aims to develop a student attendance system application integrated with subject-based attendance features and a digital leave permission module. The software development methodology employed in this study is the Waterfall model, which includes the stages of requirements analysis, system design, implementation, and testing. The application was developed using Flutter as the frontend technology, Next.js as the backend framework, and MongoDB as the database. The results of functional testing using the Black Box Testing method indicate a 100% success rate across all normal-condition scenarios, including login functionality, subject data management, attendance recording, and digital leave submission. The system is capable of improving real-time attendance recording efficiency and providing faster access to information for students, teachers, and school administrators. Through this digitalization, student attendance management becomes more accurate, transparent, and significantly reduces administrative workload.
Platform Pemesanan dan Pelacakan Pengiriman Beton Siap Pakai Berbasis Web dan Mobile dengan Pendekatan SDLC dan TAM M Rifqi Al Amin Sp; Joko Aryanto
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.838

Abstract

This study aims to develop a web and mobile-based platform for ordering and delivering ready-mix concrete as a solution to delays and data errors frequently found in manual processes. The system is designed to simplify the ordering process for customers while enabling suppliers to manage deliveries and production schedules in real-time. The research applies the System Development Life Cycle (SDLC) framework and refers to the Technology Acceptance Model (TAM) to ensure that the system is user-friendly and meets user needs. Data were collected through business process observation and system requirements analysis, and the platform was implemented using modern web and mobile technologies. The results show that the developed system increases ordering efficiency by up to 40% and reduces delivery errors by 25% compared to manual methods. Additionally, the platform offers an interactive dashboard that enables both customers and suppliers to monitor order status accurately. Based on these findings, it can be concluded that the developed platform effectively supports the digital transformation of business processes in the ready-mix concrete industry, enhancing the service quality of construction companies.
Penggunaan Model Inceptionv3 Berbasis Transfer Learning untuk Mendeteksi Masker Wajah Secara Real-Time Muhammad Chaska Putra Sofyan; Joko Aryanto
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.865

Abstract

The use of face masks has become an essential health protocol to prevent the spread of infectious diseases. However, public compliance remains low due to the absence of effective automated monitoring systems. This study aims to develop a real-time face mask detection system using transfer learning with the InceptionV3 architecture. The model was trained on facial image datasets classified into two categories: mask and no mask. By leveraging the ability of InceptionV3 to extract complex visual features, the training process becomes more efficient without training the model from scratch. The system is integrated with a webcam to perform real-time detection in real environments. The testing results indicate that the model achieved an accuracy of 98.7%, with stable detection performance and real-time responsiveness. These findings highlight the strong potential of deep learning approaches to support automated and effective monitoring of public health protocol compliance.
Co-Authors Arrum Junia Budiarti M Rifqi Al Amin Sp Muhammad Chaska Putra Sofyan