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All Journal Jurnal Ilmiah Multidisiplin Indonesia International Journal of Education, Vocational and Social Science
Dina Angela
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Engineering Languange, Linguistic, Communication & Media Physics Social Sciences Other

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Project-Based Learning Models In 21st Century Health Education: Enhancing Collaboration and Problem-Solving Skills: A Systematic Review Askrening, Askrening; Reni Yunus; Alvin Sicat; Sutrisman, Heny; Pajar Machmud; Chotimah, Chotimah; Adrianus Prihartanto; Rosmerry Simanjuntak; Dina Angela
International Journal of Education, Vocational and Social Science Vol. 4 No. 01 (2025): Pebruary,International Journal of Education, Vocational and Social Science( IJ
Publisher : Cita konsultindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63922/ijevss.v4i01.1572

Abstract

This research aims to explore project-based learning models in 21st century health education, with a focus on improving collaboration and problem-solving skills. Through a systematic literature review covering 15 research journals published between January 2020 and January 2024, it was found that project-based learning significantly improved students' global health competencies. Results showed improvements in communication and problem-solving skills, reflecting progress in students' capacity to think critically and collaborate in the context of global health issues. Although the results are positive, challenges such as inadequate teacher training and limited resources must be overcome to maximize the benefits of this learning model. This research provides important insights for educators and policy makers in implementing project-based learning in health education.
Development of Low-Power IOT Devices with Edge Machine Learning on ESP32-S3-Cam for Early Detection of Rice Diseases: Supporting Agricultural Efficiency Maclaurin Hutagalung; Yoyok Gamaliel; Nella Puspita Manullang; T.A Nugroho; Dina Angela
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 5 No. 03 (2026): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), March 2026
Publisher : Sean Institute

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Abstract

  This study aims to develop an early detection system for rice plant diseases using a machine learning (ML) approach based on edge computing with ESP32-S3 Cam devices and the Edge Impulse platform. This system is expected to provide an efficient and cost-effective solution for detecting rice diseases in agricultural areas with limited internet and electricity access. In this study, CNN and MobileNetV2 models were used to classify rice leaf diseases, including brown spot, tungro, and blight, achieving 92.73% accuracy on the test dataset. This system is designed with an offline-first principle, allowing the device to operate locally by optimising power and memory usage. The model, which is optimised through quantisation and transfer learning, is small in size, only about 587 KB, and can be operated on devices with limited resources. In addition, this system can send notifications via Telegram and Google Sheets when connectivity is available. Field test results show that the system performs well across various environmental conditions, including low light and high humidity, with a detection accuracy of 90-95%. With innovations in lightweight ML models and edge computing, this study contributes to improving agricultural efficiency in Indonesia, especially in addressing the challenges posed by climate change that affect rice production. This research also provides insights for the further development of smart farming systems integrated with IoT technology for real-time disease detection.
Co-Authors Adrianus Prihartanto Alvin Sicat Askrening Chotimah Chotimah MacLaurin Hutagalung Nella Puspita Manullang Pajar Machmud Reni Yunus Rosmerry Simanjuntak Sutrisman, Heny T.A Nugroho Yoyok Gamaliel