Making and testing prototypes is very important in developing a product. This is very important to do to minimize errors that may occur when the product has been released to end-users. There is research that discusses problems and determines the design of solutions for ordering creative gifts using design thinking. In this research, the focus is on problem space design thinking which includes empathize, define, and ideate processes with the result of the solution design in the form of a wireframe. The wireframe is made in two versions which are then carried out A/B testing to get user views regarding the design that will be used. The design of the chosen solution can be developed to the prototype stage. The focus of this research lies in solution space design thinking, namely making prototypes based on research results in the form of wireframes and conducting testing. Prototypes are important to be made as a communication tool between developers and end-users. With a prototype, end-users can directly test it without having to wait for the final product to be released, so that they can find out existing deficiencies as early as possible. The prototype was created using the Figma application. After the prototype is successfully made, the prototype is tested using the HEART Framework which focuses on aspects of Happiness and Task Success. The target set for the two aspects of the assessment is to achieve a very high level of usability level. Tests are carried out on all users, namely owner, admin, and customer. From the test results, the reliability criterion value is 0.87, which means the level of usability reaches a very high level. In other words, what has been targeted, can be achieved in this test. For the next stage, the prototype can be developed into a finished product so that its benefits can be felt directly by end-users.
This study has developed a CNN model applied to classify the eight classes of land cover through satellite images. Early detection of deforestation has become one of the study’s objectives. Deforestation is the process of reducing natural forests for logging or converting forest land to non-forest land. The study considered two training models, a simple four hidden layer CNN compare with Alexnet architecture. The training variables such as input size, epoch, batch size, and learning rate were also investigated in this research. The Alexnet architecture produces validation accuracy over 100 epochs of 90.23% with a loss of 0.56. The best performance of the validation process with four hidden layers CNN got 95.2% accuracy and a loss of 0.17. This performance is achieved when the four hidden layer model is designed with an input size of 64 × 64, epoch 100, batch size 32, and learning rate of 0.001. It is expected that this land cover identification system can assist relevant authorities in the early detection of deforestation.
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