KNN Algorithm to Determine Optimum Agricultural Commodities in Smart Farming

One of the problems in agriculture is the difficulty of determining the correct type of plant on land with certain conditions. In this study, we tried to apply an algorithm to determine the commodity according to the conditions of the planting area. The idea is to find a match between the variables owned by the environment and the plant profile. The algorithm used in this research is KNN, which was chosen because the variable has a numeric data type to perform the matching. However, sometimes, the matching does not only use variable data types. In some cases, the available variables are string data types. In this study, the researchers tried to improve the matching method on KNN to adjust the string data type variables. At the end of this study, data visualization showed the types of plants that match the case examples from a field. It proves that the proposed method can classify string data types.