<![CDATA[The growing problem of plastic waste requires innovative and sustainable solutions in terms of processing and recycling. One of the key technologies in the recycling process is the shredder machine, which serves to shred plastic waste into smaller sizes before further processing. However, in practice, industry players, especially at the microscale, often face a dilemma in choosing the optimal shredder machine between performance and cost aspects. This research aims to fill the gap by conducting a comparative analysis of three shredder designs based on price and technical performance criteria. The method used in this research involves quantitative measurements of vary in several key aspects, namely: physical dimensions (body area), frame length, transmission system (gearbox and/or clutch), storage capacity, chopper type, motor specifications (power), speed controller, additional features such as presser, foldability, and total price. Furthermore, the data was analyzed using a simple machine learning approach based on heatmap scoring with the help of Python libraries such as Pandas, Seaborn, and Matplotlib. The analysis results show that the 3rd design provides the best performance. This research contributes to data-driven decision making in shredder machine selection, with an approach that combines technical and economic aspects in an integrated manner. The findings are expected to serve as a reference for the development of efficient and sustainable waste plastic processing technology in the microscale industry sector.]]>
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