Poverty is one of the fundamental problems that is the center of attention of the government in a country. One of the important aspects to support the Poverty Reduction Strategy is the availability of accurate and targeted poverty data. Naïve Bayes is one method that can be used to classify data. The results of the classification carried out will later help aid managers to make decisions regarding the classification of determining the recipients of basic food assistance. There are two classes of predictions for the recipients of the basic food assistance, namely eligible and not eligible. The data used for prediction is sample data from XYZ village. In this research, the nave Bayes algorithm is implemented and analyzed using a web-based application. From the results of the evaluation using the confusion matrix, the resulting accuracy for 135 training data with 40 testing data and seven attributes used resulted in an accuracy of 86%, recall of 85%, and precision of 88%.

The development of video surveillance has given rise to various types of surveillance cameras, one of which is the Internet Protocol Camera (IP Camera). The number of IP Camera brands in the market, makes people who want to buy IP Cameras have to find their own information about the specifications and capabilities of the IP Camera to be purchased. It takes time and effort to choose an IP Camera, because you have to learn one by one which IP Camera to buy. This study aims to build a decision support system for choosing an IP Camera with a website-based Exponential Comparison Method (MPE) to make it easier to determine the right IP Camera. MPE can sort the priority of decision alternatives on existing criteria and is able to distinguish the value of each alternative in contrast. Based on the case study, the best alternative is Xiaomi Mi 360 with a value of 386, followed by Yi Home Camera 3 getting a value of 369, Ezviz C6N getting a value of 350, Imilab EC4 getting a value of 343 and Cleverdog Egg Cam getting a value of 110. The results of the MPE calculation generated by the system shows the same value as the manual calculation, then the MPE calculation on the system is declared valid. In addition, the test results with black-box testing show that the system can run well.