Hemp-Alumina Composite Radar Absorption Reflection Loss Classification - Muhlasah Novitasari Mara; Budi Basuki Subagio; Efrilia M Khusna; Bagus Satrio Utomo (Society of Visual Informatics, 2023)
The Radar Absorption Material (RAM) method is a coating for reducing the energy of electromagnetic waves received by converting the electromagnetic waves emitted by radar into heat energy. Hemp has been studied to have the strongest and most stable tensile characteristics of 5.5 g/den and has higher heat resistance compared to other natural fibers. Combining the characteristics of hemp with alumina powder (Al2O3) and epoxy resin could provide a stealth technology system that is able to absorb radar waves more optimally, considering that alumina has light, anti-rust and conductive properties. The electromagnetic properties of absorbent coatings can be predicted using machine learning. This study classifies the reflection loss of Hemp-Alumina Composite using Random Forest, ANN, KNN, Logistic Regression, and Decision Tree. These machine learning classifiers are able to generate predictions immediately and can learn critical spectral properties across a wide energy range without the influence of data human bias. The frequency range of 2-12 GHz was used for the measurements. Hemp-Alumina composite has result that the most effective structure thickness is 5mm, used as a RAM with optimum absorption in S-Band frequencies of -15,158 dB, C-Band of -16,398 dB and X-Band of -23,135 dB. The highest and optimum reflection loss value is found in the X-Band frequency with a thickness of 5mm which is equal to -23.135 dB with an absorption bandwidth of 1000 MHz and efficiencyof 93.1%. From this result, it is proven that Hemp-Alumina Composite is very effective to be used as a RAM on X-Band frequency. Based on the results of the experiments, the Random Forest Classifier has the highest values of accuracy (0.97) and F1 score (0.98). The F1 score and accuracy of Random Forest are 0.96 and 0.97, respectively, and do not significantly differ from KNN.