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.

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AbstrakDetak jantung manusia dapat memberikan informasi yang berguna tentang aktivitas yang terjadi di dalam tubuh. Salah satu informasi yang dapat diperoleh dari rekaman detak jantung atau elektrokardiogram adalah tingkat keterlelapan tidur seseorang (sleep stages). Dari sinyal elektrokardiogram seseorang, tingkat keterlelapan tidurnya dapat dikenali dengan terlebih dahulu mengekstrak fitur yang merepresentasikan sinyal elektrokardiogram tersebut secara keseluruhan. Ekstraksi dilakukan agar dimensi data dapat tereduksi sehingga proses klasifikasi dapat lebih mudah dilakukan. Penelitian ini melakukan ekstraksi fitur fraktal dan morfologi dari sinyal elektrokardiogram yang diperoleh dari PhysioNet. Sebelum melakukan ekstraksi fitur morfologi dari sinyal elektrokardiogram, terlebih dahulu dilakukan "Wavelet Denoising" untuk menghilangkan noise yang terdapat pada sinyal. Kata Kunci: deep sleep, elektrokardiogram, fraktal, pemrosesan sinyal,sleep stages AbstractHuman heart rate can provide useful information about the activities that occur in the body. One of information which may be obtained from recording the heart rate or electrocardiogram is commonly called a person's level of deep sleep (sleep stages). From a person's electrocardiogram signal, the level of deep sleep recognizable by extracting features that represent the electrocardiogram signal as a whole. Extraction is done so that the dimension of the data can be reduced so that the classification process can be more easily done. This study aims to extract fractal features and morphology of the electrocardiogram signal obtained from PhysioNet. Prior to the extraction of morphological features of the electrocardiogram signal, first performed "Wavelet Denoising" to remove the noise contained in the signal.