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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Sinergi Jurnal Teknologi Elektro InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Abdi Masyarakat (JAM) Indonesian Journal of Electrical Engineering and Computer Science Journal of Integrated and Advanced Engineering (JIAE) Internet of Things and Artificial Intelligence Journal
Rachmat Muwardi
Universitas Mercu Buana
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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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.
Co-Authors Adi, Puput Dani Prasetyo Ahmad Faizin, Ahmad Akhmad Wahyu Dani, Akhmad Wahyu Alvin Sepbrian Andika Arrizki Asif Khan Bugi Nur Rohman Chung, Gregorius Justin Danang Widya Pratama Diah Septiyana Firman Andika Freddy Artadima Silaban Galang P. N. Hakim Galang Persada Nurani Hakim Galatia Erica Yehezkiel Gao Hongmin Harun Usman Ghifarsyam Harun Usman Ghifarsyam Hendy Juliyanto Heri Santoso, Makmur Hongmin Gao Hongmin Gao Ihsan, Hafid Joe Mada Ranseda Permana Julpri Andika Kartika, Eka Anggara Yuda Ketty Siti Salamah Kusuma, Prima Wijaya Lenni Lenni Lenni Lenni Lenni Lenni, Lenni Lukman Medriavin Silalahi Mahabror, Dendi Mhd Adanan Purba Mirna Yunita Mirna Yunita Muhammad Hafizd Ibnu Hajar Mustain Rhozaly Ninda Sari Nugroho, Ivan Prasetyo Rahmatullah, Rizky Reyhan Rahmat Adisaputro Rhozaly, Mustain Septiyana, Diah Setiyo Budiyanto Suwoyo, Heru Triyanto Pangaribowo Wang, Yanxi Yehezkiel, Galatia Erica Yosy Rahmawati Yudhi Gunardi Yuliza Yuliza Yuliza Yuliza Yuliza Yuliza Zendi Iklima Zody Arifiansyah