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Internet of Things is an intelligence devices that can connect to any other devices. Based on that ability to connect and its intelligence, it makes people to create an intelligence device and capable to learn like human brain. To accomplish that goal, machine learning is the solution. Combination between machine learning and IoT generate a revolution in human life and application industry. The combination also generate new trend in market. Therefore, in this paper will be discuss about machine learning including machine learning technique and algorithm and also discuss about IoT including architecture and elements. Then in this paper will discuss about some researches that have already done that combine IoT with machine learning approach, including issue and challenge in those researches.

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This research is related to language article in Indonesia that discuss about causality relationship research used as public health surveillance information monitoring system. Utilization of this research is suitability of feature selection, phrase annotation, paragraph annotation, medical element annotation and graph-based semantic annotation. Evaluation of system performance is done by intrinsic approach using the Naive Bayes Multinomial method. The results obtained sequentially for recall, precision and f-measure are 0.924, 0.905, and 0.910.

Bus Rapid Transit (BRT) is one of the main choices of public transportation that supports mobility of Jakarta community. As one of the main choices of public transportation, BRT should provide good service and always improve its performance. Needs for moving or mobility will cause a problem if the moving itself is heading at the same area and at the same time. That will cause some problems which are often faced in urban areas such as traffic and delay. To overcome those problems there needs to be a strategy to build good public transportation planning, besides need to know individual travel patterns to overcome problems and improve BRT service. In case to realize those plans needs to be built origin-destination (O-D) matrix. O-D matrix is a matrix that each cell is an amount of trip from the source(row) to the destination (column). O-D matrix is beneficial for analysis, design and public transportation management. O-D matrix also provides useful information like amount of trip between 2 different locations, that can be utilized as fundamental information for decision making for three levels of strategic management (long term planning), tactic (service adjustment and network development), and operational (scheduling, passenger statistic, and performance indicator). To build O-D matrix is required a predictive model that can be measured to predict passenger destination. The predictive model will be build using classification algorithms such as Decision Tree and K-Nearest Neighbor (KNN).