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Improving the Green Economy Utilizing ReadCharge Solar Literacy Technology at SMP Arrihlah Nusraningrum, Dewi; Hasibuan, Sawarni; Astuti, Dian Widi; Kusuma, Prima Wijaya; Mahendra, Dendi
Jurnal Abdi Masyarakat (JAM) Vol 11, No 1 (2025): JAM (Jurnal Abdi Masyarakat) - September 2025
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/jam.v11i1.31098

Abstract

The empowerment of this community partnership implements a program to improve the green economy by increasing reading interest among Arrihlah Islamic Junior High School students by utilizing solar-based ReadCharge technology. The initiative aims to encourage literacy in schools and introduce renewable energy to students as part of sustainable environmental solutions. With the use of environmentally friendly technology, it is hoped that awareness of the importance of green technology in supporting the education and economic development of the community will be created. The program started with designing and building a solar-powered stand-up ReadCharge, then making the design, making the standard operating for its use, providing reading books with a variety of interesting options, and providing training on the use of ReadCharge to students, teachers, parents, and community leaders around Arrihlah Junior High School in several trainings. The training is organized systematically, from the introduction of ReadCharge and its use to its maintenance. The training evaluation provided an overview that ReadCharge delivers an in-depth understanding of the use of solar power to preserve the environment and increase reading interest for students, teachers, and the community. The practical implications of empowering community partnerships funded by grants from the Ministry of Education and Culture are part of the Government's program that encourages people to develop and utilize renewable energy, namely energy sourced from solar heat.
Raspberry Pi 4 and Ultrasonic Sensor for Real-Time Waste Classification and Monitoring with Capacity Alert System Yuliza, Yuliza; Muwardi, Rachmat; Kusuma, Prima Wijaya; Lenni, Lenni; Rahmatullah, Rizky; Yunita, Mirna; Dani, Akhmad Wahyu
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.30036

Abstract

The problem of waste management creates daily rubbish buildup due to thorough sorting. garbage sometimes accumulates in public garbage receptacles due to officials' ignorance of bin capacity and collectors' schedules, causing unclean conditions and the development of deadly diseases. Internet of Things technology was used to create a smart waste classification system with a notification mechanism in this study. This system classifies waste into plastic, metal, B3, and organic using a Raspberry Pi 4, camera module, and deep learning model. The classification uses a Convolutional Neural Network to speed up waste processing and separation. This research can be linked with research on separating trash types in one container and then allocated to garbage bins by type. Ultrasonic sensors and Raspberry Pi 4 can continuously monitor waste levels by sending data to the Ubidots IoT platform over HTTP. Based on experimental device data, system analysis shows 90% classification accuracy for all four waste categories. A Wireshark network analysis showed 61,098 bytes/s of throughput, 16 ms of delay, and zero data loss, demonstrating the system's ability for real-time monitoring and alerting. This research provides a realistic, cost-effective, and minimal solution to improve garbage classification and reduce collection costs to promote sustainability.