This work is an initial study of the research that aims to help people by giving an information about the earthquake while it happens eventhough the phone is not connected to the internet. In this research, we identify the pattern of the simulated earthquake signal from the mobile phone accelerometer via machine learning. Before the data is processed into the classifier, static windowing and denoising was done to boost up the accuracy. Another fractal features are extracted from the pre-denoised data, which are the box counting dimension feature and the Hurst coefficient. The purpose of doing static windowing is to obtain more features so that we can have many potential useful attribute candidates as possible.Denoising with symlet wavelet is done to remove the noises which can worsen the classification accuracy. The classification is done using support vector machine and multilayer perceptron classifier with the accuracy of 81 % and 82.15%, respectively.

This paper aims to build a new understanding of the nonstandard mathematical analysis. The main contribution of this paper is the construction of a new set of numbers, R Z< , which includes infinities and infinitesimals. The construction of this new set is done naïvely in the sense that it does not require any heavy mathematical machinery, and so it will be much less problematic in a long term. Despite its naïvety character, the set R Z< is still a robust and rewarding set to work in. We further develop some analysis and topological properties of it, where not only we recover most of the basic theories that we have classically, but we also introduce some new enthralling notions in them. The computability issue of this set is also explored. The works presented here can be seen as a contribution to bridge constructive analysis and nonstandard analysis, which has been extensively (and intensively) discussed in the past few years.