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All Journal Tsaqofah: Jurnal Penelitian Guru Indonesia Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika
Resmidarni Resmidarni
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Computer Science & IT Education

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Rancang Bangun Aplikasi Absensi Kepegawaian Menggunakan Metode Geolocation : (Studi Kasus: Puskesmas - Tarusan, Pesisir Selatan) Engla Pabuser; Zulhendra Zulhendra; Syafrijon Syafrijon; Resmidarni Resmidarni
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 3 No. 2 (2025): Maret: Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v3i2.736

Abstract

The attendance system is an important aspect of personnel management that affects the effectiveness and efficiency of an institution's operations. Puskesmas Tarusan in Pesisir Selatan Regency still uses a manual attendance system that is vulnerable to data inaccuracies, real-time tracking difficulties, and potential fraud such as leaving attendance. To overcome this problem, this research aims to design a geolocation-based attendance application that can improve the accuracy of employee attendance recording and reduce the potential for abuse of the attendance system. This application uses a GPS-based geolocation method that allows attendance recording to only be done at a predetermined work location. By utilising this technology, attendance recording becomes more accurate, transparent, and can be monitored in real-time. This application was developed using the Unified Modelling Language (UML) approach for system design and using mobile-based technology to be easily accessed by employees. The test results show that this system is able to improve the accuracy of recording employee attendance and reduce the potential for fraud in attendance. In addition, this application provides convenience for management in monitoring employee attendance efficiently. With the implementation of this system, it is expected that Puskesmas Tarusan can improve transparency, accountability, and effectiveness in personnel management.
Smart Focus Detection: Early Warning System Memantau Tingkat Konsentrasi Siswa dengan Behavior Recognition Fadjar Dwitama Ginting; Resmidarni Resmidarni; Agariadne Dwinggo Samala; Rizkayeni Marta
TSAQOFAH Vol 5 No 6 (2025): TSAQOFAH: Jurnal Penelitian Guru Indonesia
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/tsaqofah.v5i6.7514

Abstract

This study originates from the need for a real-time student concentration monitoring system in response to challenges in maintaining learner engagement during the instructional process. The aim of the research is to develop Smart Focus Detection, an early warning system based on deep learning and computer vision designed to automatically detect students’ focus levels. The system was developed using a modern client–server architecture integrating the YOLOv8 behavior detection model, a FastAPI-based backend service, and a Vue.js-based user interface dashboard. Development followed the Agile methodology through the stages of planning, design, implementation, and testing. The YOLOv8 model was trained using public datasets from Kaggle and Roboflow, with data preprocessing and augmentation techniques applied, and evaluated using metrics including Precision, Recall, F1 Score, and Mean Average Precision (mAP). Results show that the model achieved an mAP@0.5 score of 85%, indicating high accuracy in detecting both focused and unfocused student behaviors. Limited trials in classroom settings demonstrated that the system’s interactive dashboard effectively displays data through live monitoring features, statistical visualizations, and automated notifications. The study concludes that Smart Focus Detection holds strong potential as a proactive pedagogical assistant, enabling teachers to conduct timely interventions to enhance student engagement and learning effectiveness. The findings imply promising opportunities for leveraging artificial intelligence in the development of data-driven adaptive learning systems.
Co-Authors Agariadne Dwinggo Samala Engla Pabuser Fadjar Dwitama Ginting Rizkayeni Marta Syafrijon Syafrijon Zulhendra Zulhendra