Systematic Literature Review of Waste Classification Using Machine Learning - Astika Wulansari; Arief Setyanto; Emha Taufiq Luthfi (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 2022)
The development of the global economy has caused people's living standards to increase and the production of domestic waste has also increased from year to year. The population of big cities that have limited environmental carrying capacity, causing the waste problem requires serious handling. Manual waste sorting is hazardous to health and wastes time, money and effort. If waste is not handled properly, environmental problems will increase in the long run. Machine learning works by combining features such as textures and colors to complement junk image recognition. Today's machine learning technology continues to develop, not only methods, types of waste, and features but also identify and analyze datasets used in waste management by gathering all scientific evidence. Collecting existing research and then identifying, assessing, and interpreting requires a systematic literature review. Until the end of 2021, the research topic of waste classification using machine learning was found with various types of waste, algorithms, datasets, and others. However, the dataset used by the algorithm in image recognition is relatively single, the types of garbage classified and the relative accuracy results can still be improved.