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All Journal MEDISAINS Jurnal Sisfokom (Sistem Informasi dan Komputer) Infoman's: Jurnal Ilmu-Ilmu Manajeman dan Informatika INTERNAL (Information System Journal) Jurnal Teknik Informatika (JUTIF) Jurnal Interkom : Jurnal Publikasi Ilmiah Bidang Teknologi Informasi dan Komunikasi Journal of Artificial Intelligence and Digital Business Jurnal Manajemen Teknologi dan Sistem Informasi JPNM : Jurnal Pustaka Nusantara Multidisiplin Nuansa Informatika INTERNAL (Information System Journal) Jurnal Intelek Dan Cendikiawan Nusantara
Irfan Fadil
Universitas Sebelas April
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Development and evaluation of a TinyML-based sensor fusion system for medical waste classification on low-cost embedded devices Afriani, Dini; Fadil, Irfan
MEDISAINS: Jurnal Ilmiah Ilmu-Ilmu Kesehatan Vol. 24 No. 1 (2026)
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/medisains.v24i1.29422

Abstract

Background: Medical waste management in resource-limited healthcare facilities remains dominated by manual segregation, which is error-prone and difficult to standardize. Existing automated solutions often rely on cloud-based deep learning or high-cost hardware, limiting real-time deployment at the point of waste generation. Objective: This study aimed to develop and evaluate a medical waste classification system integrating Tiny Machine Learning (TinyML) and multi-sensor fusion on a low-cost embedded device to achieve accurate, real-time, and resource-efficient on-device inference. Method: An experimental system design approach was employed, including dataset construction, model development, and embedded deployment. A TinyML-optimized MobileNetV2 model was integrated with heterogeneous sensor fusion and evaluated under embedded constraints to assess classification performance, latency, and memory usage. Result: The vision-only model achieved an accuracy of 84.5%, with frequent misclassification of sharps waste. After integrating sensor fusion, overall accuracy increased to 96.5%, and recall for sharps reached 98%. The system demonstrated efficient on-device inference with an average latency of 280 ms and low memory consumption (<1 MB). Conclusion: The proposed TinyML-based sensor fusion system provides a robust, accurate, and cost-effective solution for automated medical waste classification. This approach enhances healthcare worker safety and supports scalable deployment in resource-limited healthcare environments.
Co-Authors Agun Guntara Agun Guntara Asep Saeppani Atep Ruhiat Dian Budiana Egi Rifaldi Fasya Zahira Badeni Fauzy Abdilah Fidi Supriadi Firmansyah, Esa Guntara, Agun Hania, Revita Lestari, Hilda M.Arif Firmansyah Mahardika, Fathoni Muhammad Agreindra Helmiawan Mulyana, Aa Agus Prabowo, Dimas Aditya Rizki Nurul Nazmi Siti Nurhayati Surya Manggala, Ramdani