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All Journal Inspiration: Jurnal Teknologi Informasi dan Komunikasi Journal of Embedded Systems, Security and Intelligent Systems
Maya Itasari
Politeknik Negeri Ujung Pandang
Computer Science & IT

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Automated Student Activity Monitoring Based on Spatiotemporal Modeling Using MediaPipe and Long Short-Term Memory Andi Syarwani; Hartinah; Maya Itasari; Nurul Amalia Amri; Annisa Nurfadhilah; Muhdalifah Muhtar
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9371

Abstract

Computer vision-based Human Activity Recognition (HAR) systems hold significant potential for applications in educational settings, particularly for monitoring student activities in laboratories or classrooms. Activities such as typing, smartphone usage, and resting are often visually indistinguishable due to their highly similar seated postures. This study proposes a spatiotemporal modeling approach to automatically and non-invasively recognize such activities. Body poses are extracted from video streams using MediaPipe Pose and represented as sequential feature vectors, which are then analyzed using a Long Short-Term Memory (LSTM) network to capture temporal dynamics. The model is trained on video data of students performing three primary activity classes. Evaluation on validation data demonstrates a classification accuracy of 98.48%, with average precision, recall, and F1-score values of approximately 98%. However, testing on unseen videos shows a decrease in accuracy to around 65%, primarily due to misclassification in segments with minimal movement. These findings suggest that the model is sensitive to subtle pose transitions, which are common in seated activity contexts. Overall, the proposed approach demonstrates promising potential for automated student activity monitoring and provides a foundation for developing pose-based behavioral analysis systems in contextual learning environments.
Adaptive Power Regulation of Street Lights Using Light Sensor Thresholding: A Proteus-Based Simulation Study Maya Itasari; Zainal Akbar
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 2 (2025): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

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Abstract

This paper presents the design and simulation of a street lighting power control system based on PWM modulation integrated with ambient light sensing. The simulation circuit consists of a DC-DC converter, an Arduino Nano microcontroller, an IRFZ44N MOSFET as a switching device, an INA219 sensor for current and voltage monitoring, an LDR (Light Dependent Resistor) for ambient light detection, and a street lamp module as the load. PWM duty cycle variation, ranging from 10% to 100%, was simulated to analyze its effect on the voltage, current, and power characteristics of the system. Additionally, an LDR sensor was employed to dynamically adjust the PWM output based on external light intensity, simulating vehicle headlights or environmental lighting changes. The simulation results showed that the maximum power consumption reached 5.490 W at 100% PWM. By implementing a scheduled PWM adjustment (50%-100%) combined with LDR-based control, the system achieved an estimated energy efficiency improvement of over 40% compared to conventional constant illumination at full brightness. The proposed design demonstrates the potential for significant energy savings and offers an effective solution for developing intelligent and energy-efficient street lighting systems in smart city applications
Co-Authors Andi Syarwani Annisa Nurfadhilah Hartinah Muhdalifah Muhtar Nurul Amalia Amri Zainal Akbar