Waste sorting is an important step toward building a more measurable waste management system. However, manual sorting remains error-prone, as waste is often disposed of in categories that do not correspond to the designated bins.
This condition has encouraged Luluk Lusiantoro, S.E., M.Sc., Ph.D., a lecturer at the Department of Management, Faculty of Economics and Business, Universitas Gadjah Mada (FEB UGM), to develop the Smart Waste Bin, an artificial intelligence (AI)-powered waste-sorting device capable of identifying and sorting waste. The device is designed to generate data on waste types and weight, map waste locations, and calculate carbon emissions.
Luluk Lusiantoro, S.E., M.Sc., Ph.D., a lecturer at the Department of Management, FEB UGM, and the developer of WESTA, explained that Smart Waste Bin is one of the devices within the WESTA (AI-Powered Waste Circular Ecosystem). The device is designed to minimize errors that commonly occur during manual waste sorting.
“We often find that waste is still sorted incorrectly even when separate bins for organic, inorganic, and residual waste are provided. We want to minimize these errors with AI so that users can place their waste in the smart bin. The AI will then automatically sort it,” he explained.
Currently, the Smart Waste Bin, developed in collaboration with Qubit Instrument (CV Bumi Lestari), is designed to recognize three categories of waste: organic, inorganic, and residual. In the future, the classification will be expanded to recognize more types of waste, including hazardous and toxic materials (B3).
The Smart Waste Bin does not operate independently; it is integrated with the WESTA application. This integration allows waste identification data to be displayed on the device’s screen and through a mobile application, which is currently under development and being prepared for publication on the Google Play Store.
Through the system, users can obtain information on the identified waste type, estimated weight, estimated carbon emissions, location, and the product brand found in the waste. This information is an important component of WESTA, as waste data can be used to identify waste generation patterns and support data-driven waste management.
“Why is the brand important? Because the data can later be shared with companies that produce the waste found in the environment. Hopefully, this can support the principle of Extended Producer Responsibility, in which producers are also responsible for the waste generated by their products,” Luluk said.
The device is not only intended to help communities sort waste more accurately. The data generated can also contribute to a circular economy ecosystem by connecting waste-related information to product producers.
Smart Waste Bin is connected to the WESTA application through Bluetooth Low Energy (BLE). Users can connect the application to the device, insert waste, and allow the system to scan and send the detection results. The results include the waste category, estimated weight, and carbon footprint reduction measured in kilograms of CO₂e.
Luluk added that the device is also being developed to support an incentive system, particularly for waste with economic value, such as plastic bottles. Identified waste that enters the waste management chain can be converted into points, providing users with additional incentives to sort and manage waste.
“The point redemption feature for rewards is currently still under development,” he explained.
The development of the Smart Waste Bin is part of the 2026 Innovation Development Grant (HPI) program, which aims to scale up WESTA into a system ready for operational testing. The ecosystem integrates the Smart Waste Bin device, the Mobile Waste Scanner application, and an enterprise dashboard for managing and analyzing waste and carbon footprint data.
The development of the Smart Waste Bin has also entered the integration stage. Based on progress as of August 2026, the electronic installation had been completed, and by August 18, all device functions were operating in an integrated manner with the application and AI model.
As part of the development process, WESTA is planned to undergo testing within the FEB UGM environment. The next stages include optimizing the AI model, integrating four waste classes—including B3 waste—into the hardware, testing accuracy and stability, and conducting usability testing.
Through the integration of devices, applications, AI, and data analytics, WESTA is being developed to establish a waste management system that goes beyond disposal and sorting by generating data that can support decision-making and the development of a circular economy.
Reportage: Kurnia Ekaptiningrum
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