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  3. Vol. 11, No. 3, August 2026 (Article in Progress)
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Vol. 11, No. 3, August 2026 (Article in Progress)

Issue Published : Jun 4, 2026
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

AIoT-Enabled Automatic Waste Sorting System with Real-Time WhatsApp Notifications

https://doi.org/10.22219/kinetik.v11i3.2593
Muchamad Rusdan
Universitas Teknologi Bandung
Sri Kuswayati
Universitas Teknologi Bandung

Corresponding Author(s) : Muchamad Rusdan

muchamad.rusdan@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 11, No. 3, August 2026 (Article in Progress)
Article Published : Jun 7, 2026

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Abstract

The waste management crisis, particularly in educational institutions, requires innovative solutions that combine artificial intelligence and automation. This research develops and evaluates an automated waste sorting system based on Artificial Intelligence of Things (AIoT) integrated with WhatsApp notifications. The system utilizes the EfficientNet-B0 deep learning model optimized with transfer learning and runs on a Raspberry Pi 4 edge device to classify waste into five categories: plastic, paper, metal, glass, and organic in real time. Classification results are translated into physical actions by a servo actuator mechanism, while ultrasonic sensors monitor trash bin capacity. The real-time notification system via WhatsApp API sends alerts to administrators. A 30-day evaluation on campus showed that the system achieved 92.3% classification accuracy with an inference latency of 1.8 seconds. The mechanical system successfully sorted waste with a 94.5% success rate, and WhatsApp notifications had a 99.1% delivery rate, with an average administrator response time of 8.2 minutes during operational hours. A comparative analysis demonstrated that this system increased sorting efficiency by 87% and reduced operational costs by 45% compared to manual waste sorting methods. These findings conclude that the proposed integration of edge AI, mechanics, and WhatsApp notifications creates a smart waste management solution that is not only effective and real-time but also practical, economical, and sustainable for wider implementation.

Keywords

Artificial Intelligence of Things Deep Learning Edge Computing Smart Waste Management WhatsApp Notifications Waste Classification
Rusdan, M., & Kuswayati, S. (2026). AIoT-Enabled Automatic Waste Sorting System with Real-Time WhatsApp Notifications. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 11(3). https://doi.org/10.22219/kinetik.v11i3.2593
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References
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  37. H. Lichter, M. Schneider-Hufschmidt, and H. Zullighoven, “Prototyping in industrial software projects-bridging the gap between theory and practice,” IEEE Trans. Softw. Eng., vol. 20, no. 11, pp. 825–832, 1994, doi: 10.1109/32.368126.
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Read More

References


B. P. Relations, “11,3 million Tons of Garbage in Indonesia are Mismanaged,” BRIN. Accessed: Nov. 15, 2025. [Online]. Available: https://brin.go.id/en/news/119838/113-million-tons-of-garbage-in-indonesia-are-mismanaged

S. Elias, J. Krogstie, A. Kaboli, and A. Alahi, “Environmental Science and Ecotechnology Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability : A comprehensive systematic review,” Environ. Sci. Ecotechnology, vol. 19, p. 100330, 2024, doi: 10.1016/j.ese.2023.100330.

D. B. Olawade et al., “Smart waste management : A paradigm shift enabled by artificial intelligence,” Waste Manag. Bull., vol. 2, no. 2, pp. 244–263, 2024, doi: 10.1016/j.wmb.2024.05.001.

W. Rahman, R. Islam, A. Hasan, N. I. Bithi, and M. Hasan, “Intelligent waste management system using deep learning with IoT,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 5, pp. 2072–2087, 2022, doi: 10.1016/j.jksuci.2020.08.016.

B. Fang, J. Yu, Z. Chen, A. I. Osman, M. Farghali, and I. Ihara, Artificial intelligence for waste management in smart cities : a review, vol. 21, no. 4. Springer International Publishing, 2023. doi: 10.1007/s10311-023-01604-3.

G. Caiza, M. Saeteros, W. Oñate, and M. V Garcia, “Fog computing at industrial level, architecture, latency, energy, and security: A review,” Heliyon, vol. 6, no. April 2019, p. e03706, 2020, doi: 10.1016/j.heliyon.2020.e03706.

M. Nkwo, B. Suruliraj, and R. Orji, “Persuasive Apps for Sustainable Waste Management : A Comparative Systematic Evaluation of Behavior Change Strategies and,” Front. Artif. Intell., vol. 4, no. December, pp. 1–18, 2021, doi: 10.3389/frai.2021.748454.

T. J. Sheng et al., “An Internet of Things Based Smart Waste Management System Using LoRa and Tensorflow Deep Learning Model,” IEEE Access, vol. 8, pp. 148793–148811, 2020, doi: 10.1109/ACCESS.2020.3016255.

K. Ahmed, M. Kumar, A. Kumar, and S. Dubey, “Measurement : Sensors Artificial intelligence and IoT driven system architecture for municipality waste management in smart cities : A review,” Meas. Sensors, vol. 36, no. December 2023, p. 101395, 2024, doi: 10.1016/j.measen.2024.101395.

S. Lionita, C. Manik, M. A. Berawi, and M. Sari, “Smart Waste Management System for Smart & Sustainable City of Indonesia ’ s New State Capital : A Literature Review,” in E3S Web of Conferences 517, ICETIA 2023, 2024, pp. 1–6. doi: 10.1051/e3sconf/202451705021.

C. Rosca and A. Stancu, “Innovative AIoT Solutions for PET Waste Collection in the Circular Economy Towards a Sustainable Future,” Appl. Sci., vol. 15, no. 13, pp. 1–36, 2025, doi: 10.3390/app15137353.

B. Fang, J. Yu, Z. Chen, A. I. Osman, M. Farghali, and I. Ihara, Artificial intelligence for waste management in smart cities : a review, no. 0123456789. Springer International Publishing, 2023. doi: 10.1007/s10311-023-01604-3.

R. Baraskar, S. Khan, A. Sabale, S. Bavdhane, and A. D. Rathod, “A Review of Smart AI Garbage Management,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 13, no. VI, 2025, doi: 10.22214/ijraset.2025.72003.

C. A. A. Era, M. Rahman, and S. T. Alvi, “Artificial Intelligence of Things (AIoT) Technologies, Benefits and Applications,” in 2024 4th International Conference on Emerging Smart Technologies and Applications (eSmarTA), 2024, pp. 1–6. doi: 10.1109/eSmarTA62850.2024.10638992.

K. P. Seng, L. M. Ang, and E. Ngharamike, “Artificial intelligence Internet of Things : A new paradigm of distributed sensor networks,” Int. J. Distribited Sens. Networks, vol. 18, no. 3, pp. 1–27, 2022, doi: 10.1177/15501477211062835.

H. Abdu and M. H. M. Noor, “A Survey on Waste Detection and Classification Using Deep Learning,” IEEE Access, vol. 10, pp. 128151–128165, 2022, doi: 10.1109/ACCESS.2022.3226682.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” IEEE Xplore, pp. 1–9, 2015.

M. Shafiq and Z. Gu, “Deep Residual Learning for Image Recognition : A Survey,” Appl. Sci., vol. 12, no. 18, pp. 1–43, 2022, doi: 10.3390/app12188972.

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 4510–4520. doi: 10.1109/CVPR.2018.00474.

M. Tan and Q. V Le, “EfficientNet : Rethinking Model Scaling for Convolutional Neural Networks,” in Proceedings of the 36th International Conference on Machine Learning, California, 2020.

C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J. Big Data, 2019, doi: 10.1186/s40537-019-0197-0.

S. Shahrabadi, V. Alves, E. Peres, R. M. Dos Santos, and T. Adão, “Unbalancing Datasets to Enhance CNN Models Learnability: A Class-Wise Metrics-Based Closed-Loop Strategy Proposal,” IEEE Access, vol. 13, pp. 57485–57503, 2025, doi: 10.1109/ACCESS.2025.3554395.

M. A. Morid, A. Borjali, and G. Del Fiol, “A scoping review of transfer learning research on medical image analysis using ImageNet,” Comput. Biol. Med., vol. 128, p. 104115, 2021, doi: https://doi.org/10.1016/j.compbiomed.2020.104115.

A. Mumuni and F. Mumuni, “Data augmentation : A comprehensive survey of modern approaches,” Array, vol. 16, no. August, p. 100258, 2022, doi: 10.1016/j.array.2022.100258.

O. Salman, I. Elhajj, A. Kayssi, and A. Chehab, “Edge computing enabling the Internet of Things,” in 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), 2015, pp. 603–608. doi: 10.1109/WF-IoT.2015.7389122.

M. Sathesh, K. Ramakrishnan, M. Raja, K. Kalaiarasi, and M. Balamurugan, “Edge Computing Integration in IoT Networks for Real-Time Data Processing,” in 2024 International Conference on Cybernation and Computation (CYBERCOM), 2024, pp. 585–590. doi: 10.1109/CYBERCOM63683.2024.10803202.

D. Minott, S. Siddiqui, and R. J. Haddad, “Benchmarking Edge AI Platforms: Performance Analysis of NVIDIA Jetson and Raspberry Pi 5 with Coral TPU,” in SoutheastCon 2025, 2025, pp. 1384–1389. doi: 10.1109/SoutheastCon56624.2025.10971592.

C. Pulgar´ın-Ospina, L. Launet, A. Colomer, and V. Naranjo, “Optimizing Deep Learning Models for Edge Computing in Histopathology: Bridging the Gap to Clinical Practice,” in Procedia Computer Science, ScienceDirect, 2024, pp. 2549–2557. doi: 10.1016/j.procs.2024.09.443.

R. Dagli and S. Eken, “Deploying a smart queuing system on edge with Intel OpenVINO toolkit,” Soft Comput., vol. 25, no. 15, pp. 10103–10115, 2021, doi: 10.1007/s00500-021-05891-2.

V. Shankar, “Edge AI: A Comprehensive Survey of Technologies, Applications, and Challenges,” in 2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET), 2024, pp. 1–6. doi: 10.1109/ACET61898.2024.10730112.

A. R. Hakim, J. Rinaldi, and M. Y. B. Setiadji, “Design and Implementation of NIDS Notification System Using WhatsApp and Telegram,” in 2020 8th International Conference on Information and Communication Technology (ICoICT), 2020, pp. 1–4. doi: 10.1109/ICoICT49345.2020.9166228.

S. E. J. De Witt, H. N. Chua, M. B. Jasser, and R. T. K. Wong, “A Literature Review of Notification Systems: Challenges and Opportunities for Fake News Alerts,” in 2024 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), 2024, pp. 279–284. doi: 10.1109/I2CACIS61270.2024.10649632.

A. Rahmatulloh, I. Darmawan, and A. Putra, “WasteInNet : Deep Learning Model for Real-time Identification of Various Types of Waste,” Clean. Waste Syst., vol. 10, no. December 2024, p. 100198, 2025, doi: 10.1016/j.clwas.2024.100198.

M. H. Samsuri et al., “Comparative Performance Analysis of Edge-AI Devices in Deep Learning Applications,” in 2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA), 2024, pp. 1–6. doi: 10.1109/ICIEA61579.2024.10665079.

M. M. Hossen et al., “A Reliable and Robust Deep Learning Model for Effective Recyclable Waste Classification,” IEEE Access, vol. 12, pp. 13809–13821, 2024, doi: 10.1109/ACCESS.2024.3354774.

H. Pradiko, S. Wahyuni, and W. A. Ganiy, “Knowledge-attitude-practice method analysis as a guide for Kasomalang Kulon Village waste bank planning,” in The 5th International Seminar on Sustainable Urban Development, IOP Publishing, 2021, pp. 1–6. doi: 10.1088/1755-1315/737/1/012074.

H. Lichter, M. Schneider-Hufschmidt, and H. Zullighoven, “Prototyping in industrial software projects-bridging the gap between theory and practice,” IEEE Trans. Softw. Eng., vol. 20, no. 11, pp. 825–832, 1994, doi: 10.1109/32.368126.

R. A. Aral, Ş. R. Keskin, M. Kaya, and M. Hacıömeroğlu, “Classification of TrashNet Dataset Based on Deep Learning Models,” in 2018 IEEE International Conference on Big Data (Big Data), 2018, pp. 2058–2062. doi: 10.1109/BigData.2018.8622212.

S. Meng and W.-T. Chu, “A Study of Garbage Classification with Convolutional Neural Networks,” in 2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN), 2020, pp. 152–157. doi: 10.1109/Indo-TaiwanICAN48429.2020.9181311.

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