Automatic Tourism Waste Selection Using Image Digital and Artificial Intelligence (AI) - Bakir, Muh Janwar; Fathurrahman, Haris Imam Karim (Association for Scientific Computing Electrical and Engineering (ASCEE), 2023)
Garbage is leftovers or discarded items that are no longer used and are no longer used by their owners. Waste is generally divided into two, namely organic and inorganic waste. Both of these wastes have benefits for us, but they also have an impact on the environment. Organic waste is waste that comes from the remains of living creatures (nature) such as animals, humans, plants that are experiencing decay or weathering. This waste is classified as environmentally friendly waste because it can be broken down by bacteria naturally and quickly. The research object studied in this research is camera detection on a waste detection tool using a camera which aims to detect types of tourism waste, where in this research I will conduct research on the detection of organic and non-organic tourism waste. The waste problem in Indonesia is caused by an increase in waste produced by the community, a lack of rubbish disposal sites (TPS), the spread of insects and rats due to rubbish, as well as environmental pollution through land, water and air pollution. So it is hoped that this tool will be able to reduce the waste problem in Indonesia, especially in the tourism environment. In this study, an average value of 0.83% was obtained, where the results were in accordance with the initial target when starting training and carrying out detection. This makes it possible to move the servo more accurately because the detection results have a high value. From the test results above, an accuracy of 90% was obtained, and the results of the servo movement were in accordance with the detection results, where if the results were organic waste detection, the servo would rotate 90 degrees and if the detection results were non-organic, the servo would not move or remain in the 0 degree position. There was no error in servo accuracy, but the error in detection was 10% from 20 samples which resulted in the servo moving in the direction of the servo movement in the error detection direction.