Abstract: The main problem using SVR is to find optimal parameter (σ) by using kernel function such as radial basis, polynomial, Gaussian and so on. Moreover, we also have to find optimal hyperplane parameter (C and ε). In the heart of statistical methods and data mining, the motivation of researcher doing this is to minimize time, money and energy in the analysis at the same time the results will be more accurate. The development of such a massive technology and the availability of data is very much making progress and improvement of methods based on data mining and machine learning. In this study, we proposed four different optimizations such as LIBSVM, MOSEK, QUADPROG, SMO applied to Localized Multi-Kernel Learning (LMKL) to assign local weights to kernel functions, so that, the best hyperplane parameters will be obtained. For the simulation, we use the electrical data and we have labeled based on the characteristics of different days (Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, National Holiday, Ramadhan). As well as we can capture the pattern of electricity consumption.