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Classification on corn plants is used to classify leaf of corn plants that are healthy and have diseases consisting of Northern Leaf Blight, Common Rust and Gray Leaf Spot. Convolutional Neural Network (CNN) is one of algorithms from the branch of deep learning that utilizes artificial neural networks to produce accurate results in classifying an image. In this study, ResNet-9 architecture implemented to build the best model CNN for classification corn plant diseases. After that we doing comparisons of epochs have been carried out to obtain the best model, including comparisons of epochs of 5, 25, 55, 75 and 100. After the epoch comparison, the highest accuracy value was obtained in the 100 epoch experiment so that in this study 100 epochs were used in model formation. The number of datasets used is 9145 data which is divided into two, there are training data (80%) and testing data (20%). In this study, three hyperparameter tuning experiments were carried out and the results of hyperparameter tuning experiments where num_workers is 4 and batch_size is 32. This classification obtained an accuracy rate of 99% and the model is implemented into a web interface.

The reliability of stand-alone and hybrid power plant systems was dependent on electrical loads that the system must supply. For example, on renewable energy sources (RES), Reviews of those systems needs to be calculated well before the development process. One of the most important processes in the initial calculation is the electrical load that must be supplied by the system. The electrical load has a major influence on the amount of power generating capacity. A power plant that has higher electricity production than the load to be fulfilled was considered capable of meeting the system electrical load requirements. However, in terms of the reliability, it is considered as a loss because it will affect the life of the components and the high cost of operating from the system. Therefore, this research discusses the effect of load growth on hybrid power plant system performance of Baron Techno Park. The result of the research shows that the total electricity production of Baron Techno Park hybrid power plant system is 319.695 kWh/year with Net Present Cost (NPC) is $560.077 and the cost of energy (COE) is $0.64/kWh. Total electricity consumption of the PLTH Baron Techno Park is 67.413 kWh/year with total excess electrical energy is 245,547 kWh/year. Load growth of 5%, 10%, 15%, and 20% of the total current load affect the consumption of electric energy, excess electrical energy, and COE. The higher the load growth will affect the total electricity consumption that is increasingly higher so that the total excess electrical energy is lower. This research found that the performance of the system is not influenced by load growth. The highest performance of the system is resulted by the wind turbine of 72.62%, followed by solar panels of 18.82%, and biodiesel of 8.56%.