January 20, 2026 | 05:09 pm
TEMPO.CO, Jakarta — A skin disease detection device developed by students from Indonesia’s Bandung Institute of Technology (ITB) won first place in the Innovation Track at the national round of the Huawei ICT Competition.
The team is now preparing a prototype to compete at the Asia-Pacific regional level, scheduled to take place in Thailand in April 2026.
Competing under the name “Hawkeye,” the team consists of telecommunications engineering students from ITB’s School of Electrical Engineering and Informatics (STEI), class of 2022: Sulthan Miftahul Ulum, Rossi Putri Rusliadi, and Kimi Rafif Asyadda. They are supervised by lecturer Abdul Latif Sungkar.
“We want to develop a detection device, particularly for skin diseases that are often overlooked in rural communities,” Rossi told Tempo on Monday, January 19, 2026.
The device is designed to integrate a camera, a display screen to show detection results, artificial intelligence (AI) modules, and an Internet of Things (IoT) system.
Rossi explained that the core system is built using Huawei equipment, which is also used to train the AI model. Images of patients’ skin conditions captured by the camera are analyzed and matched against the AI’s database.
The innovation is intended to provide an initial screening rather than a definitive diagnosis, with final confirmation remaining the responsibility of medical professionals. However, the team aims to expand the device’s capabilities to identify early symptoms of skin conditions visible on the body.
“We are targeting a detection time of under one minute,” Rossi said.
To achieve this, the team must train the AI model for approximately one month to ensure a high level of accuracy.
The device is designed to recognize a range of common skin conditions, including fungal infections, leprosy, and scabies—diseases that are relatively prevalent in the Asia-Pacific region due to factors such as high humidity, dense populations, and malnutrition.
The AI will be trained using a large dataset of skin disease images collected from various reference sources.
“We need to label the data first and then input the algorithm so the device can perform detection,” Rossi said, adding that hundreds of thousands of images are required for the AI to accurately distinguish between different types of skin diseases.
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