Indonesia with a population of 250 million is a large market, Millennials tend to be more adaptive to the development of communication technology [1]. There are lot of opportunities that are used by various groups, one of which is the need to use smartphones that can make it easier for people to exchange information [2].  The shift in sales of smartphone brands in Indonesia is influenced by  massive advertising carried out by smartphone vendors (smartphone capitalists) to consumers [3]. The enthusiasm of the community in welcoming this platform is so great, lot of comment about smartphone brand stated by public is an interesting thing to be processed to be information. Utilization of that information requires analytical techniques so that the produced information can help many parties. The method used in this study is Naïve Bayes classification method which is a learning technique for data mining algorithms that uses probability and statistical methods [4]. This method is used to classify comments given by the community to smartphone brands. The comments given in this application will later be classified into positive, negative, and neutral comments. The purpose of this study was to find out how much positive, negative and neutral comments the community gave to smartphone brands, so that later it would facilitate the smartphone brand in providing policies or development in the future.

This article presents a decision system for choosing the most appropriate connectivity technology for an Internet of Things (IoT) system. The analytical method of multicriteria decision AHP (Analytical Hierarchy Process) was used to determine which technology most meets the user's specifications for a given IoT application from a set of both objective and subjective criteria. The method was applied in a case study built to evaluate the system in a scenario with real characteristics. The system was able to select the connectivity technology that came closest to the user's demand for both short and long range scenarios.