<![CDATA[The increasing volume of plastic waste, particularly plastic bottles, is a significant environmental problem in many countries, including Indonesia. Lack of public awareness in recycling and inefficient waste management exacerbate this condition. Based on data from the Ministry of Environment in 2021-2023, the national waste composition is dominated by food waste with an average of 39% and plastic waste of 18% and the rest of the waste from metal, glass and tree branches. The existing waste management system is often ineffective in encouraging people to actively participate in recycling. One way to increase public participation in recycling is by incentivizing points that can be exchanged for prizes or discounts. However, the implementation of this system often faces technical and logistical obstacles, such as accurate and efficient identification and classification of bottles, so it is necessary to apply the integration of image processing and machine learning in the process of identifying and classifying bottle waste. From the modeling results, the machine learning model successfully identifies the type of bottle waste with an average accuracy 57,5 %.]]>
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