Object retrieval analysis on plastic bottle waste recycling-based image control using convex hull algorithm and autoregressive integrated moving average prediction method

In Indonesia, plastic garbage bottles are the most common sort of waste. Given that waste is expected to grow annually, managing plastic waste is a major challenge. The results of the study were achieved by comparing the reference, which was a collection of manually created contour images, with 50 sets of vortex images with different forms and vortex areas as experimental objects. The results indicate that the suggested approach reports a mean error of 2.84%, a correlation coefficient of 0.9965, and a root mean square error of 0.2903 when compared to the manual extraction method. These findings imply that the extract area determined by the procedure outlined in this research is more accurate and nearer to the actual values. The proposed method can therefore be used in place of the traditional process for investigating cooling parameters through manual testing. With measurement values mean absolute percentage error (MAPE)=121,842, mean absolute deviation (MAD)=20,140, and mean squared deviation (MSD)=776,712, the trend analysis of plastic bottles for autoregressive integrated moving average (ARIMA) modeling leads to the conclusion that the waste from plastic bottles will continue to rise annually and that efforts must be made to address this trend with knowledge and waste recycling technology. Plastic that is advantageous to industry and society.