Zandy Yudha Perwira

Bio: Zandy Yudha Perwira is an academic researcher. The author has contributed to research in topics: Weather station. The author has an hindex of 1, co-authored 2 publications receiving 3 citations.

Peningkatan Skalabilitas Mini Weather Station Portable berbasis Internet of Things

Abstract: Indonesia is a country that has unique weather that provides not only abundant natural resources but also can causes disasters at any time. To reduce the threat of losses, observing weather elements using a weather station is a solution that can be used. The development of systems related to environmental monitoring and weather stations is not new. However, most research focuses on various innovations in utilization, low cost and power savings. These studies have not touched on the aspect of ease of system development, especially in the concept of adding nodes. Indonesia, as a country with diverse regional topography, needs an integrated weather monitoring system with the concept of centralized data collection to get a complete picture. In this study, a portable mini weather station system was built named Amicagama. This system is built with the concept of high scalability which means the system is designed to be used publicly, with each user able to manage the nodes which are their respective weather stations. Management by each user here means that each user can manage weather data to be submitted, add nodes at a new location, and can delete nodes at a certain location if something unexpected happens.

Pengaruh Latar Belakang Warna pada Objek Gambar terhadap Hasil Ekstraksi Sinyal EEG

Abstract: In this study, observation on the differences in features quality of EEG records as a result of training on subjects has been made. The features of EEG records were extracted using two different methods, the root mean square which is acquired from the range between 0.5 and 5 seconds and the average of power spectrum estimation from the frequency range between 20 and 40Hz. All of the data consists of a 4-channel recording and produce good quality classification on artificial neural network, with each of which generates training data accuracy over 90%. However, different results are occured when the trained system is tested on other test data. The test results show that the two systems which are trained using training data with object with color background produce higher accuracy than the other two systems which are trained using training data with object without background color, 63.98% and 60.22% compared to 59.68% and 56.45% accuracy respectively. From the use of the features on the artificial neural network classification system, it can be concluded that the training system using EEG data records derived from the visualization of object with color background produces better features than the visualization of object without color background.

Power Management In IoT Weather Station

Abstract: This paper presents a power management solution for an Internet of Things (IoT) based, battery operated weather station prototype. A complete simulation software was developed to validate this system with real weather data. Through the help of the developed software and power consumption measurements made on the prototype an optimized system runtime was calculated and implemented. In order to reduce maintenance the calculations were fine tuned to allow a switch from batteries to supercapacitors.

Three-Class Classification of EEG Signals Using Support Vector Machine Methods

Abstract: Many research on how the human brain works has been done in the last century. The use of electroencephalogram signal generated from quantifying the brain wave have 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 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 resemble mu rhythm of brain wave (8 - 12Hz) which affected from motor imagery activity. The classification of three classes, which are imagination of right body movement, left movement, and random word using multiclass support vector machine (SVM) shows a promising result with sensitivity of 96.88%, 86.12% and 52.78% from three different subjects.

Thunderstorm hazards early warning system

Abstract: Systematic monitoring of the status and trends of the thunderstorm activity of a given region is caused by necessity to minimize damage from lightning. Particular attention is paid to the reliability of the thunderstorm activity forecast and the time of its formation. So a small time span between the formation of a reliable forecast of the storm hazards and the onset of adverse events does not allow you to take timely measures for minimization the damage caused. Particular attention is paid to the reliability of the forecast generated. Thus, an inseparable combination and implementation of these requirements allows you to solve the problem of optimal synthesis of an early warning system of thunderstorm hazards (THEW).