REDUCING GADGETS ADDICTION THROUGH TRADITIONAL GAMES

REDUCING GADGETS ADDICTION THROUGH TRADITIONAL GAMES

21
Jan
2022

The Covid-19 pandemic has exposed children to gadgets such as computers, laptops, and cellphones more intensely due to distance learning. Gadgets for learning are also followed by the increasing use of gadgets for social media and online games that trigger gadget addiction. To reduce this problem, UNY students consisting of Dwi Agnes Setianingrum, Dian Anggraini and Akhip Nugroho (science education), Furi Ningsih Sri Sukowati (physics education), and Aerafatma Ahyaun Nisa (elementary school teacher education) initiated the development of a traditional game.

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ANTI-THEFT WALLET INNOVATION MADE FROM BANANA MIDRIB WASTE

ANTI-THEFT WALLET INNOVATION MADE FROM BANANA MIDRIB WASTE

17
Jan
2022

UNY students made a wallet with a security system that will inform the owner when the wallet is more than 10 meters apart from its owner. Uniquely, the wallet is made of banana midrib fiber, given a Javanese letter pattern. They are Asni Muslimah (Clothing Engineering Education), Annisa Nurfatimah Febrianti (Accounting Education), Annisa Alimah Ufairoh (Physical Education), Latifah Nur Khasanah (Chemical Education), and Atiqotul Maula Al Farihah (Sociological Education). According to Asni Muslimah, they made the smart wallet because many people still use conventional wallets.

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Inobike UNY: an electric bicycle innovated by UNY students

Inobike UNY: an electric bicycle innovated by UNY students

3
Jan
2022

Universitas Negeri Yogyakarta(UNY) students have successfully made an electric bicycle named "Inobike UNY". This bicycle can travel up to 40 kilometers (km) with charging for approximately 2 hours. The INOBIKE UNY bicycle is an early example of research integrated with learning and community service.

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AGE DETECTION THROUGH FACE PHOTOS

AGE DETECTION THROUGH FACE PHOTOS

7
Dec
2021

Anthony Fioren H, Mohammad Damarjati P and Syukron Abdul A succeeded in making an age detection application through facial photos. Anthony Fioren stated that they used training data in image data of a human face with age information from the face to train a computer to recognize the age and gender of a human face. "To determine gender and age from the image, we use algorithms and digital image processing methods," he said. It works to extract parts of the human face in the image and then process it so that the output issued is the age and gender.

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