Efektivitas Teknologi Pemilahan Limbah Medis: Meta-Analisis Terhadap Sistem Sensor, IoT, Robotika, Dan Klasifikasi  Citra Digital

Sumaryanto Sumaryanto; Alfan Afandi

doi:10.31004/koloni.v5i3.1301

Authors

Sumaryanto Sumaryanto Universitas Ngudi Waluyo
Alfan Afandi Universitas Ngudi Waluyo

DOI:

https://doi.org/10.31004/koloni.v5i3.1301

Keywords:

Artificial Intelligence, Edge Computing, Medical Waste, Waste Sorting, Smart Waste Bin.

Abstract

The mixing of medical and non-medical waste in healthcare facilities increases the risk of workplace accidents, such as the transmission of nosocomial infections, and raises hospitals’ operating costs. The use of smart digital technology in smart waste bins offers a solution for automatic waste sorting without direct contact. This study employed a meta-analysis approach in accordance with the PRISMA guidelines. A literature search was conducted in the PubMed, ScienceDirect, and Google Scholar databases for publications from 2021 to 2026. Of the 164 studies identified, 10 articles that met the inclusion criteria were extracted and critically analyzed using a random-effects model and narrative synthesis. The results indicate that segregation technologies utilizing AI-based image processing and physical sensors achieved waste classification accuracy rates of 90% to 100%. The Inception-V3 architecture delivered the highest medical image accuracy at 93%, while the Inductive Proximity sensor achieved 100% accuracy with an extremely fast response time. The implementation of edge computing based on local microcontrollers has proven capable of eliminating dependence on the internet, saving power consumption, and reducing direct physical contact by users by up to 85%. Additionally, the integration of volume monitoring and IoT telemetry achieves waste volume measurement accuracy of up to 98%, with network node efficiency ranging from 93.75% to 96.66%. The implementation of Smart Waste Bin technology based on the integration of AI, edge computing, and electronic sensors has proven to be effective, hygienic, and economical in improving compliance with medical waste sorting.

References

Andi Muhammad Akbar, Muhammad Basri, & Wahyuddin Wahyuddin. (2025). Implementasi Machine Learning Menggunakan Algoritma Klasifikasi untuk Mendeteksi Jenis Sampah. Jurnal Publikasi Manajemen Informatika, 3(3), 152–161. https://doi.org/10.55606/jupumi.v3i3.3751

Batista, T. C., Nappi, I., Xavier, M. V. M., Fiuza, I. O., Vasconcelos, C. B. M. de, & Enohi, R. T. (2025). Burnout in nursing: causal factors and impacts on health and professional performance – a systematic review. Revista Brasileira de Medicina Do Trabalho, 23(03), 01–09. https://doi.org/10.47626/1679-4435-2025-1432

Belsare, K., Singh, M., Gandam, A., Samudrala, V., Singh, R., Soliman, N. F., … Algarni, A. D. (2024). RETRACTED: Wireless sensor network-based machine learning framework for smart cities in intelligent waste management. Heliyon, 10(16), e36271. https://doi.org/10.1016/j.heliyon.2024.e36271

Febriyanti, T., Bijaksana, M. A., & Farady, I. (2023). Klasifikasi Citra Limbah Medis Menggunakan Pre-Trained Model Yang Berdasarkan CNN. Jurnal Tugas Akhir Fakultas Informatika.

Firdaus, R. I., Sugiharto, W. H., & Ghozali, M. I. (2025). Implementasi Convolutional Neural Network Dalam Sistem Otomatis Pemilahan Sampah Infeksius Berbasis Citra Digital. SisInfo, 7(1), 11–22. https://doi.org/10.37278/sisinfo.v7i1.1052

G, U., Sankaranarayanan, B., & R K A, B. (2024). Smart Waste Revolution: AI-Powered Biomedical Segregation and Recycling with IoT Integration. International Journal of Mechanical Engineering, 11(12), 38–46. https://doi.org/10.14445/23488360/IJME-V11I12P104

Kementerian Lingkungan Hidup dan Kehutanan RI. Peraturan Menteri Lingkungan Hidup dan Kehutanan Nomor 6 Tahun 2021 tentang Tata Cara dan Persyaratan Pengelolaan Limbah Bahan Berbahaya dan Beracun. , (2021).

Muchtar, A. A., Riogilang, H., & Riogilang, H. (2022). Perencanaan Pengelolaan Limbah B3 Medis Padat Di Rumah Sakit Perguruan Tinggi Negeri. TEKNO, 20.

Pratiwi, D., & Wardani, R. S. (2020). Analisis manajemen pengelolaan limbah medis padat B3 di rumah sakit. Jurnal Kesehatan Masyarakat, 15(2), 112–121.

Purwanti, A. A. (2018). Pengelolaan Limbah Padat Bahan Berbahaya Dan Beracun (B3) Rumah Sakit Di Rsud Dr.Soetomo Surabaya. Jurnal Kesehatan Lingkungan, 10(3), 291–298.

Rahmadani, B., Harsoyo, I. T., & Olla, P. K. (2025). Rancang Bangun Alat Smart Bin AI Ruang Tunggu Pasien di Rumah Sakit. JUSTEK : Jurnal Sains Dan Teknologi, 8(2), 192–201.

Ramdani, F., & Nuroji. (2026). Sistem Monitoring Volume Sampah Medis Berdasarkan Ketinggian Menggunakan Sensor Ultrasonik Berbasis IoT dengan Notifikasi Telegram. Journal of Information System Research, 7(2), 460–469. https://doi.org/10.47065/josh.v7i2.9236

Renhoran, C., Ismail, M. M., Priyaniti, A. A., Saputra, E. A., & Kumarajati, D. Y. H. (2024). Rancang Bangun Sistem Pemilah Limbah Medis Berbasis Arduino Dan Inductive Proximity. Jurnal Informatika Dan Teknik Elektro Terapan, 12(3). https://doi.org/10.23960/jitet.v12i3.4941

Safira Fegi Nisrina, Lawrence Adi Supriyono, & Basuki Rahmat. (2025). SmartBin Medis Berbasis AI dan Edge Computing untuk Manajemen Limbah Klinis yang Higienis. Jurnal Informatika Dan Tekonologi Komputer (JITEK), 5(2), 82–90. https://doi.org/10.55606/jitek.v5i2.6385

Sandia, K. M., Yudhaa, A. P., Aryantoa, N. D., & Farabi, M. A. (2022). Klasifikasi sampah menggunakan Convolutional Neural Network. Indonesian Journal of Data and Science (IJODAS, 3(2), 72–81.

Wardahani, Y. A., Akbar, A. A., & Tejoyuwono, A. A. T. (2026). Hubungan Pengetahuan dan Sikap Perawat Instalasi Gawat Darurat Dengan Tindakan Dalam Upaya Pengelolaan Limbah Medis Padat di Rumah Sakit Umum Daerah Dr. Soedarso Kota Pontianak. COMSERVA : Jurnal Penelitian Dan Pengabdian Masyarakat, 5(10), 1911–1927. https://doi.org/10.59141/comserva.v5i10.3522