The a-C/a-C:B homojunction of palmyra sugar has been successfully fabricated using the nanospray method. Palmyra sugar was chosen as the main source of carbon because it is cheap, renewable, abundant and available around the clock. nanospray is used as a deposition method on glass ITO substrates because of several advantages, namely cheap, easy, portable, low power consumption, the deposited layer is more evenly distributed and thinner. Junction samples when in bright conditions [emitted light] showed an increase in current and voltage values ​​compared to dark conditions. Testing the current and voltage of the junction sample shows the characteristics of a rectifier diode. This confirms the results of the test using PES as a doping process with amorphous carbon with boron capable of changing the conduction type from a-C from an intrinsic semiconductor to a p-type semiconductor. Testing the junction sample when irradiated with visible light using a lamp shows symptoms of the photovoltaic effect. Tests directly on the sun when conditions AM 1.5 samples showed symptoms of the photovoltaic effect. This indicates that the a-C/a-C:B amorphous carbon homojunction junction sample functions as a solar cell.

Classification of skin cancer is an important task to detect skin cancer and help with the treatment of skin cancer according to its type. There are many techniques in imaging used to classify skin cancer, one of the superior deep learning (DL) algorithms for classification is the Convolutional Neural Network (CNN). One type of skin cancer is dangerous is melanoma. In this study, CNN is proposed to help classify this type of skin cancer. The dataset consists of 15103 images of skin cancer pigments with 7 different types of skin cancer. These three tests proved malignant skin lesions can be classified with higher accuracy than non-melanocytic skin lesions which is 90% and performance evaluation shows melanocytic and non-melanocytic skin lesions detected with the highest accuracy. The tests conducted in this study grouped several types of skin diseases namely the first tests conducted using a group of melanocytic and non-melanocytic skin disease, second testing using groups of melanoma and melanocytic nevus diseases, and the final testing using malignant and benign. The proposed CNN model achieved significant performance with a best accuracy of 94% on the classification of melanoma and melanocytic nevus.