by Agfianto Eko PUTRA, Wiwit SURYANTO, Agung Nugraha SULISTYANA
Seismic data analysis of the 2006 Merapi volcano eruption has been carried out using the Continuous Wavelet Transform (CWT) and the Wavelet-based Decomposition and Correlation (WAVEDECOR) combined with the Fast Fourier Transform (FFT). The CWT is used to show the frequency pattern of the event while the WAVEDECOR is used to denote the frequency band of the signal. The CWT and the WAVEDECOR are supported by the FFT to ensure the dominant frequency of the observed signals. The results show that visual patterns and dominant frequency distribution of certain events, including the VT-A, the Low Frequency (LF), the VT-B, tremor, multiphase and lava avalanche. The result from this analysis was then compared with related eruption signal of Merapi in 1996 to determine the pattern similarity. The comparison results show almost identical results for dominant frequencies in VT-A events as well as MP events. The findings in the VT-B event showed that the dominant frequency pattern was slightly different from the 1996 data which showed at medium to high-frequency while for the 2006 data showed only at a medium frequency.
Tags: merapi, Volcano Eruption, wavelet
Willy Permana Putra (Politeknik Negeri Indramayu), Rizal Iman M (Politeknik Negeri Indramayu), A Sumarudin (Politeknik Negeri Indramayu dan Agfianto Eko Putra (UGM)
Luas perairan Indonesia sebesar 64.97 % dari luas wilayah Indonesia dengan jumlah nelayan sebanyak 2.17 juta nelayan di seluruh Indonesia. Kebanyakan dari nelayan Indonesia tersebut masih menggunakan cara tradisional dalam melaut. Dalam penelitian ini dilakukan untuk dapat membuat server sistem tracking perahu nelayan secara realtime. Dengan sistem ini, nelayan kecil dengan kegiatan nya hanya 1 – 2 hari melaut (one day fishing) dalam melakukan pelayarannya tingkat keselamatan nelayan dapat ditingkatkan dikarenakan dapat terpantau posisinya. Sistem yang dikembangkan menggunakan server berbasis mqtt protocol di sisi gateway. Server menggunakan mysql server menggunakan get JSON Parsing. Sebagai front end sistem tracking untuk menampilkan kordinat perahu berbasis web. Dari hasil implementasi didapat bahwa server dapat merespon dengan baik dari end device dengan melakukan pengiriman data secara periodik.
Achmad Solichin, Agus Harjoko, and Agfianto Eko Putra
This study proposed a model for determining the movement direction of the object based on the optical flow features. To increase the speed of computational time, optical flow features derived into a Histograms of Oriented Optical Flow (HOOF). We extracted them locally on the grid with a certain size. Moreover, to determine the movement direction we also analyzed multiple frames at once. Based on the experiment results, showing that the value of accuracy, precision, and recall of the movement detection is good, amounting to 93% for accuracy, 73.07% for precision and 84.25% for recall. Furthermore, the results of testing using the best parameter shows the value of accuracy of 98.1%, 35.6% precision, 41.2% recall, and direction detection error rate (DDER) 25,28%. The results of this study are expected to provide benefits in video analysis studies such as riots detection and abnormal movement in public places.
Tags: Histograms of Oriented Optical Flow (HOOF), movement direction, multiple frames, optical flow, Video analysis
Agfianto Eko Putra, Catur Atmaji, and Fajrul Ghaleb
In the area of affective computing technology, the classification of emotions can be used for a variety of things such as health, entertainment, education, etc. This study determined the classification of emotions based on EEG (Electroencephalography) signals, which is emotions are classified according to the 2-dimensional graphics modeling of arousal and valence. This research uses a wavelet decomposition method to get features from the EEG signal. Features taken from the signal is a power signal decomposition of sub-band theta, alpha, beta, and gamma. These features are derived from the 5 levels decomposition using Coiflet2 and Daubechies2 mother wavelet. Classification is done using k-Nearest Neighbor (kNN) with the closest neighbor calculated based on correlation distance. Data validation is done using 5-folds cross-validation for validation of test data and training data. The highest accuracy obtained by using the mother wavelet Coiflet 2 with kNN parameter k=21. Valence classification accuracy is 57.5%, and accuracy of arousal is 63.98%.
Tags: eeg, emotion classification, knn, wavelet
Catur Atmaji, Agfianto Eko Putra, and Irvan Albab Tontowi
Many research on how the human brain works have been done in the last century. The use of electroencephalogram signal generated from quantifying the brainwave has been developed in many areas including the development of brain-computer interface (BCI) concept. One type of BCI that interesting for the future use is motor imagery (MI) based-BCI which only requiring imagination of a person to control an object. This study proposed a feature extraction in eight different channels using discrete wavelet (DWT) coefficients. The wavelet coefficient is transformed to a frequency domain using discrete Fourier transform (DFT) and then average power spectrum is calculated. Level 5 of detail component of the DWT is chosen because, from 512Hz sampling frequency (8 - 16Hz), it resembles mu rhythm of brain wave (8 - 12Hz) which affected from motor imagery activity. The classification of three classes, which are the imagination of right body movement, left the movement, and random word using multiclass support vector machine (SVM) shows a promising result with a sensitivity of 96.88%, 86.12% and 52.78% from three different subjects.
Suwanto Raharjo, Retantyo Wardoyo, Agfianto Eko Putra
Proper nouns (often abbreviated PN or NNP) are a class of words important in labeling and subsequent text processing, especially in natural language processing (NLP). Name entity recognition (NER) is one study that requires PN. The lack of labeled data for Indonesian text, especially the PN label, may be attributed for the lack of NER research in the Indonesian language. This study aims to detect PN in Indonesian-language translations of the Quran guided by deriving location information from the Quran as its source text. In the Indonesian language, PN is written using initial capital letters, which are used to determine and guide PN location. This article proposes that PN in Indonesian-language translations of the Quran can be determined based on PoS Information of Quranic text by developing a certain production rule. The results of this research showed that the proposed method has promising results for further research.
Tags: Indonesian, Name entity, Part of speech, Proper noun, Recognition, Tagging
By Catur Atmaji, Agfianto Eko Putra, Hanif Arrijal
Abstract
In the past few decades, biomedical signals have played important roles in assisting diagnosis for medical purposes. After the rose of brain-computer interfaces (BCI) and human-machine interaction (HMI) concept, biomedical signals such as an electroencephalograph (EEG) and electrooculogram (EOG) begun to be implemented in control and communication systems. EOG, the signal resulted from eye movement, has been used to design various applications from drowsiness detection to virtual keyboard control. The key of the system developed from EOG signal is the detection system for every eye movement. In this study, a sliding window technique is proposed to make eye movement patterns easier be formulated and using overlap window to avoid local extrema when computing the feature. Evaluation of this method shows that combination of 0.5 s-window length and 25% overlap give 17% and 1% false discovery rate (FDR) in the vertical and horizontal channel while the true positive rate (TPR) in both channels is 98%. The combination of automatic window and 25% overlap give a better accuracy with 99% and 100% TPR in the two directions while the FDRs are 22% and 1%.
Please contact Mr. Catur Atmaji for more information.
Tags: eog, eye movement, sliding window
by Andi Dharmawan, Ahmad Ashari, Agfianto Eko Putra
Abstract
Quadrotor as one type of UAV (Unmanned Aerial Vehicle) is a system that underactuated. It means that the system has a signal control amount is lower than the degrees of freedom or DOF (Degree Of Freedom). This condition causes the quadrotor have limited mobility. If quadrotor is made to have 6 DOF or more (over-actuated system), the motion control system to optimize the flight will be different from before. We need to develop over-actuated quadrotor control. Quadtiltrotor as the development of quadrotor has some control signal over its DOF. So we call it as an over-actuated system. Based on the type of maneuver to do, the transition process when the quad tiltrotor performs a translational motion using the tilting rotor need special treatment. The tilt angle change is intended that the quad tiltrotor can perform translational motion while still maintaining its orientation angle near 0°. This orientation angle can change during the undesirable rotational movement as the effect of the transition process. If additional rotational movements cannot be damped, the quad tiltrotor can experience multi overshoot, steady-state error, or even fall. Because of this matter, we need to develop flight control system to handle it. The flight control system of quad tiltrotor can be designed using a model of the system. Models can be created using quad tiltrotor dynamics by the Newton-Euler approach. Then the model is simulated along with the control system using the method of control. Several control methods can be utilized in a quad tiltrotor flight systems. However, with the implementation of LQG control method and Integrator, optimal translational control of the quad tiltrotor can be achieved.
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Tags: Newton-Euler, over-actuated, quadrotor, uav, under-actuated
by Hesti Khuzaimah Nurul Yusufiyah, Ilona Usuman, Agfianto Eko Putra, Triyogatama Wahyu Widodo
This research is motivated by the condition of children with autism who require continuous monitoring without a parent accompany, and certain therapies who is capable to reduce repetitive behaviors and increase the concentration. Music therapy is one of the treatment which able to perform it. The implementation of this research is located in a special schools with autism, the perform is proper at certain times and requires tools for music therapy. Therefore, the integrated device was made to monitor the activities of children with autism, as well as providing music therapy automatic face recognition based on Eigenface method.This device perform when the children with autism under certain conditions (e.g. crying, the teacher would not work orders, etc.), then by capturing the the facial image, the system will process to compare the similarity with the existing database. Then, the shortest distance of euclidean is chosen. If the captured of facial image is similar to the one existing database, then the music is performed as music therapy for children with autism. The results of this system, indicates that the child’s responses become more calm, easy concentration, and the repetitive attitude is reduced. While the accuracy of the system achieves by 80% (compare with the old and new database) and 20%. (without new database).
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Tags: autism, eigenface, face rcognition, music therapy
by Mardi Hardjianto, Jazi Eko Istiyanto, Subanar, Agfianto Eko Putra
Abstract—Many fall detection systems are developed using accelerometer and gyroscope on a smartphone. In existing fall detection systems, smartphone location placement is generally selected from the beginning and cannot be repositioned. The movement of smartphone decreases the accuracy of the system. This research provides fall detection model using accelerometer on smartphone and the placement of the smartphone could vary as of six predetermined positions. The method used for fall detection is threshold method applying only one parameter, the value of resultant acceleration.
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