Klasifikasi percepatan dari sinyal gempa bumi dan sinyal linier aktivitas manusia menggunakan akselerometer smartphone berbasis algoritme support vector machine

The threat of earthquake calamity spread throughout most of the Indonesian archipelago. Smartphone’s accelerometer usage as a seismic parameter detector in Indonesia, of which the noise has obstacles, mainly due to human activities. This study aims to classify linear acceleration signals caused by human activity and earthquake acceleration signals as an initial effort to reduce noise caused by human activity in the smartphone’s accelerometer signals. Both signals are classified by using the Support Vector Machine (SVM) algorithm of which consists of several steps, respectively, data collection, data preprocessing, data segmentation, feature extraction, and classification. These algorithms are tested to 2545 human activity signals in trouser pocket, 2430 human activity signals in shirt pocket and earthquake acceleration signals. Based on the test results by using the confusion matrix, linear acceleration signal data caused by human activity and earthquake acceleration signals can be classified properly using an SVM algorithm with Polynomial or Gaussian kernel with a small kernel scale value. The algorithms can achieve an accuracy of 87.74% to 97.94%.Ancaman bahaya gempabumi tersebar hampir di seluruh wilayah kepulauan Indonesia. Akselerometer pada smartphone, yang dapat dimanfaatkan sebagai instrumen pendeteksi parameter kegempaan di Indonesia, memiliki kendala terkait noise akibat aktivitas manusia. Penelitian ini bertujuan memilah sinyal percepatan linier manusia dan sinyal gempabumi sebagai upaya awal untuk mereduksi noise akibat aktivitas manusia dalam sinyal akselerometer smartphone. Sinyal percepatan linier aktivitas manusia dan sinyal percepatan gempabumi diklasifikasikan menggunakan algoritme Support Vector Machine (SVM) dengan beberapa tahapan yaitu penghimpunan data, prapengolahan data, segmentasi data, ekstraksi fitur, dan klasifikasi. Algoritme SVM diujikan terhadap 2545 sinyal aktivitas manusia pada kantong celana, 2430 sinyal aktivitas manusia pada kantong baju dan sinyal gempabumi. Berdasarkan hasil pengujian menggunakan matriks konfusi, data sinyal percepatan linier aktivitas manusia dan sinyal percepatan gempabumi dapat diklasifikasikan secara baik menggunakan algoritme SVM berkernel Polinomial atau Gaussian dengan nilai kernel skala yang kecil. Algoritme ini mampu mencapai akurasi 87,74% hingga 97,94%

Rancang Bangun Human Activity Recognizer Sebagai Classifier Sinyal Gempa Bumi Dan Aktivitas Manusia Pada Akselerometer Smartphone

Akselerometer pada smartphone dapat merekam sinyal gempa bumi guna mendukung mitigasi kebencanaan di Indonesia. Aktivitas manusia menghasilkan noise yang cukup signifikan terhadap data akselerometer pada smartphone. Human Activity Recognizer (HAR) diimplementasikan memilah sinyal aktivitas manusia dari sinyal gempa bumi yang direkam akselerometer smartphone. Penelitian ini bertujuan untuk mengklasifikasikan sinyal percepatan linier aktivitas manusia dengan sinyal percepatan  gempa bumi pada akselerometer smartphone Android melalui Human Activity Recognizer (HAR) berbasis algoritma K-Nearest Neighbor (K-NN). Metode penelitian terdiri dari penghimpunan data, pra pengolahan data, segmentasi data, ekstraksi ciri, dan klasifikasi. Berdasarkan hasil pengujian HAR sebagai classifier sinyal gempa bumi dan sinyal aktivitas manusia menggunakan algoritma K-NN secara umum mampu memilah sinyal akselerometer smartphone akibat aktivitas manusia dan sinyal gempa bumi dengan rentang akurasi 66,9% hingga 100%. Algoritma tipe Fine K-NN memiliki akurasi tertinggi sebesar 100%

Evaluasi Spasial Estimasi Curah Hujan pada Radar Cuaca Menggunakan Metode Z-R Marshall-Palmer di Wilayah Jawa Barat

Rainfall is one of the weather parameters that affect various sectors. High rainfall intensity can trigger hydrometeorological disasters, so rainfall observation data is vital to monitor rainfall conditions in an area. An automatic rain gauge is an instrument that measures rainfall at an observation point, but the instrument has reasonably low coverage and has yet to reach the entire region. Weather radar is a remote sensing instrument capable of spatially estimating rainfall. Weather radar data can be used to estimate rainfall using the Marshall-Palmer Z-R method. The application of the method can be an alternative for areas that do not have rainfall observation equipment. However, the estimation needs to be evaluated to improve the accuracy of the estimation value. Based on the evaluation, the highest coefficient of determination was 0.92, and the lowest was 0.67. The lowest RMSE value was 2.40, the highest was 6.76, the highest ME value was 16.59, and the lowest was 5.93; the highest bias was 12.90, and the lowest was 5.30. The study results show that the weather radar can operate according to the specifications of the maximum observation distance of up to 220 KM, but the farther the observation distance to a point, the higher the performance of rainfall estimation accuracy.Curah hujan merupakan salah satu parameter cuaca yang berpengaruh terhadap berbagai sektor. Intensitas curah hujan tinggi dapat memicu terjadinya bencana hidrometeorologi sehingga data pengamatan curah hujan sangat penting untuk memantau kondisi curah hujan di suatu wilayah. Peralatan curah hujan otomatis merupakan instrumen mengukur curah hujan di suatu titik pengamatan, namun peralatan tersebut memiliki cakupan yang cukup rendah dan belum menjangkau seluruh wilayah. Radar cuaca merupakan salah satu instrumen penginderaan jauh yang mampu mengestimasi curah hujan secara spasial. Output data pengamatan radar cuaca dapat dijadikan estimasi curah hujan menggunakan metode pendekatan Z-R Marshall-Palmer. Penerapan metode tersebut dapat menjadi alternatif untuk wilayah yang belum memiliki peralatan pengamatan curah hujan. Namun, estimasi tersebut perlu dievaluasi sehingga dapat meningkatkan perfoma akurasi nilai estimasi tersebut. Berdasarkan evaluasi yang telah dilakukan, koefisien determinan tertinggi sebesar 0,92 dan terendah sebesar 0,67. Nilai RMSE terendah sebesar 2.40 dan tertinggi sebesar 6.76, nilai ME tertinggi sebesar 16.59 dan terendah 5.93, bias tertinggi sebesar 12,90 dan terendah 5.30. Hasil studi menunjukkan radar cuaca dapat beroperasi sesuai spesifikasi jarak maksimal pengamatan hingga 220 KM, namun semakin jauh jarak pengamatan terhadap suatu titik, mempengaruhi performa akurasi estimasi curah hujan

Algorithm performance comparison for earthquake signal recognition on smartphone’s accelerometer

Micro-electro-mechanical-system accelerometer is able to detect acceleration signal caused by earthquake. Such type of accelerometer is also used by smartphones. There are few algorithms that can be used to recognize the type of acceleration signal from smartphone. This study aims to find signal recognition algorithm in order to consider the most proper algorithm for earthquake signal detection. The initial stage of designing the recognizer is data collection for each type of signal classification. The next step is to apply a highpass filter to separate the signals collected from the gravitational acceleration signal. The signal is divided into several segments. The system will extract features of each signal segment in the time and frequency domain. Each signal segment is then classified according to the type of signal using the classifier through a series of training data processes. The classifier which has the highest accuracy value is exported into the new input signal modeling. As the result, fine K-NN algorithm has the highest level of accuracy in the classification. The fine K-NN algorithm has an accuracy rate of 99.75% in the classification of human activity signals and earthquake signals with a memory capacity of 6,044 kilobytes and processing time of 15.93 seconds. This algorithm has the best classifier criteria compared to decision tree, support vector machine and linear discriminant analysis algorithms