Agriculture is an activity to manage biological natural resources with the help of technology and labor. The presence of diseases in plants that suddenly inhibit plant growth is alarming to farmers. So, farmers cannot determine what conditions these plants suffer. This study will discuss the implementation of Artificial Intelligence-based plant disease detection software. At this stage, deep learning models are created using cameras matched with objects. The application development is to detect diseases in plants. The fourth step is testing. This application includes the implementation of Convolutional Neural Network and Recurrent Neural Network, which provides Artificial Intelligence architecture to diagnose plant diseases, and offer solutions to those plants from the results of research with tomato plant sample tests obtained four categories of disease Early Blight disease with a prediction of 100%, Bacterial Spots 90%, Healthy 100%, Late Blight 100% a system that can recommend health care related to crops based on images so that it can help farmers identify types of plant diseases. This application can help farmers to reduce crop failure for farmers caused by plant diseases to improve the quality of agricultural and plantation products

This research was conducted based on a report from the United States Federal Trade Commission regarding fraud through electronic text messages via SMS that fraudsters use to manipulate potential victims. Usually, scammers spread SMS spam as an intermediary for the crime. The development of a supervised learning algorithm is applied to predict SMS spam into three categories, such as SMS spam, SMS fraud, and promotional SMS. The prediction system is dividing into several stages in the development process, including data labelling, data preprocessing, modelling, and model validation. The known accuracy based on modelling using Logistic Regression using a test size of 15% is 99%, using a test size of 20% is 99%, and using a test size of 25% is 98%. The Multinomial Naïve Bayes algorithm's accuracy with a test size of 15%, 20%, 25% is 97%. So, the SMS spam prediction approach uses the logistic regression method, which has the highest accuracy.

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The logistics model can be modified by adding migration factors as a function to the population. This function considers the existence of limited human migration and interaction by the ability of environmental carrying capacity. This model can be completed qualitatively by using the method of point balance analysis and quantitatively by using the exact undesired metodediferensial. Both of these methods give the same result. If the intrinsic growth rate is greater than for migration then for a long period of time, the model will depend on intrinsic growth factor, environmental carrying capacity and migration rate. Furthermore, if the intrinsic growth is small rather than migration then for a prolonged period of time, the model will depend on minus intrinsic growth, environmental carrying capacity and immigration. Then, if migration growth is the same as migration then the model becomes Malthus model.