bluuebunny/crossref_metadata_2025_split huggingface dataset:
Feature selection with Lasso for classification of ischemic strokes based on EEG signals
Authors: Angga Yuwono Hendra , Kusuma Wijaya Sastra , Prajitno Prawito 
Published: June 2020

Abstract
Electroencephalography (EEG) is an electrical signal data that can describe brain activity in which the signal contains important information that can be used to detect several diseases. One of the diseases that can be detected by EEG signals is stroke. The most common type of stroke is the acute ischemic stroke (AIS) due to blockage of blood supply to the brain which can generate the tissue damage in the brain EEG signal recording uses several electrodes where the more electrodes used in the recording, the greater the number of EEG features produced (high dimensional data). This can make it difficult for models of machine learning to have optimal performance on high-dimensional data. In this study, for optimizing the performance of the machine learning model by selecting features with the Least Absolute Shrinkage and Selection Operator (Lasso) method, where this method can select the relevant features by shrinking some coefficients to zero. The type of classification used in this study is random forest with features used for classification are Brain Symmetry Index (BSI), Delta-Alpha Ratio (DAR), Delta-Theta-Alpha-Beta Ratio (DTABR). The results showed that the Lasso method can optimize the performance of learning machines with an accuracy value of 75% with 24 features out of 45 features.
Predictive maintenance magnetic sensor using random forest method
Authors: Aji A S , Sashiomarda J A , Handoko D 
Published: June 2020

Abstract
BMKG has observations of earth’s magnets which are scattered in Indonesia. The BMKG earth magnetic sensor produces real-time data output. This research focuses on predictive maintenance models on earth magnetic sensors based on data output. The resulting data output is in the form of a delimited format in the form of spaces so that it is easy to process. The magnetic component used is the total component data (F) from the earth’s magnetic sensor. Data processing using python scripts with the algorithm used is the random forest method by comparing the resulting value difference to find out whether the data generated is still in tolerance or not. The results of the prediction model with the number of estimators 10 produce RF Score = 0.98 and MAE = 0.83.
Real time monitoring state-of-charge battery using internal resistance measurements for remote applications
Authors: Alif Kurniawan Taufiq , Natajaya Aldyan , Sidi Priambodo Purnomo , Wibisono Gunawan 
Published: June 2020

Abstract
Currently, the use of batteries as energy storage applications in various fields initiates many researchers to maximize the battery performance. One of the most important things to prevent a battery from over-charging or discharging issue is an accurate State-of-Charge (SoC) estimation of the battery. In this paper, real time monitoring SoC battery for the remote applications is proposed by calculating the battery’s internal resistance when charging or discharging. When charging process, the battery’s internal resistance (ric
) is low and will increase according with increasing the level of SoC. While at the discharging process, the battery’s internal resistance (rids
) is low and will increase in proportional to the decreasing of the SoC level. The calculation of rids
and rich
are conducted by measuring open circuit voltage, load voltage, load current, and charging current using a voltage divider mechanism on the Arduino Uno and current sensors, respectively. LORA RA-02 SX 1278 433 MHz is used to transmit the battery parameters from the remote area (transmitter) to the server (receiver). Then the SoC will be estimated based on internal resistance calculation using these parameters on the server. With a simple structure, the proposed systems can be applied to the future remote real time monitoring battery systems.
Design analysis of consolidation of cellular operators in Indonesia
Authors: Wibisono Gunawan , Fistarini Ria 
Published: June 2020

Abstract
To increase the network capacity demands cellular operators have to add sites, use technology with a high efficiency spectrum, and increase spectrum allocation. The limited frequency spectrum becomes an obstacle for operators in increasing the availability of system capacity, which in turn can reduce the quality of mobile broadband services. Indonesian cellular operators are expected to have a spectrum deficit of around 400 - 450 MHz in 2020. One way to overcome spectrum deficits is consolidation between cellular operators. It is therefore very interesting to conduct a consolidation research of Indonesian operators and evaluate and analyze their impact on reducing spectrum deficits and competition. In this research the consolidation design was carried out with the following provisions: a maximum of two operators, migration of spectrum two of the consolidated operators that were not contiguous was carried out to a minimum, the consolidated spectrum of results is a contiguous frequency, and no spectrum divestment. The consolidation scenario of Indonesian cellular operators is simulated on operators namely Tsel, XL, Isat, and H3I operating at frequencies of 900, 1800 and 2100 MHz in six regional mobile broadband services namely Sumatra, Java, Bali Nusa Tenggara, Kalimantan, Sulawesi and Maluku Papua. There are three consolidation scenarios. Scenario I consolidation consisted of H3I+Isat consolidation, XL+Tsel, scenario II consolidation consisted of H3I+XL consolidation and Isat+Tsel consolidation, and scenario III consolidation consisted of H3I+Tsel and Isat+XL consolidation. First evaluated consolidation which gives the least spectrum deficit. Then the consolidation scenario is evaluated using company operational and financial data to decide which operator consolidation provides efficiency and fair competition. It can be shown from the results that the best technically consolidated operators may not necessarily provide good financial results.
Prediction of PM2.5 and PM10 parameters using artificial neural network: a case study in Kemayoran, Jakarta
Authors: Putra A M M B , Martarizal  , Putra R M 
Published: June 2020

Abstract
It was recorded that in August 2019 the case of acute respiratory infection in Indonesia had doubled compared to the previous months. This is in line with the increasing levels of PM10 and PM2.5 in several regions in Indonesia. In the end the public is increasingly aware of the importance of air quality information. Prediction of air quality will greatly help the public to anticipate the dangers of declining air quality. The use of Artificial Neural Network can be a solution in making daily air quality forecasts whose parameters are not linear. This research shows that utilization of historical data parameters of temperature, humidity, air pressure, rainfall, sun exposure and wind speed as well as BMKG’s PM10 and PM2.5 data is able to produce forecasting modeling for PM2.5 and PM10 concentrations in the Kemayoran area, Jakarta by utilizing Artificial Neural Network Modeling. The result is success to make prediction of PM2.5 and PM10 and it will be better if more historical data applied.
Identification system for beeswax on Malang apple using VNIR imaging
Authors: Praditya Naufal , Harmoko Saputro Adhi 
Published: June 2020

Abstract
Wax coating identification on fruits is very difficult without a non-destructive method. In general, destructive methods were used to identify wax or coatings by soaking the fruit in hot water or using a mixture of vinegar and water. There are also destructive systems that was used such as gas chromatography linked with mass spectrometry, but this method takes much time and difficult to operate. Visible Near Infrared Imaging (VNIR) becomes the alternate solution to identify wax on the surface of the fruit without spoiling the quality of the fruit. In this study, identification system for beeswax application on apples has been made successfully. The process starts through image acquisition, image correction, object detection, window averaging, classification model, and the coating status. The VNIR image was acquired on a wavelength range from 400 to 1000 nm. The data was divided for training and testing the classification model using cross-validation method, then the model was evaluated using confusion matrix. Several classification models were used to compare the result and to conclude which model gives the best accuracy for identification and classification problems. The accuracy of the three models were 72.92% for PCA-SVM model, 81.25% for DT model, and 91.67% for RF model.
The influence of particle size to diffusivity of nanogold particles based on Brownian motion
Authors: Zulfahmi  , Handoko Djati , Prajitno Prawito , Isnaeni  
Published: June 2020

Abstract
Diffusivity calculation has been conducted by many researchers in various methods. One ingenious and sample-efficient way to determine the diffusivity is the examination under the microscope by using microbead random movements, called Brownian motion, through the footage of its movements. This study was conducted to observe the diffusivity of nanogold particles towards the use of various microbead sizes. The one-micron-sized microbead was mixed into nanogold particles solution and prepared onto slide glass, so was the three micron-sized and five-micron-sized microbead. The footage of microbead movements for each size is then tracked and processed using MATLAB image processing to obtain the mean square displacement (MSD) of the microbead and then fitting it to time to find the sample diffusivity. As a result, the nanogold diffusivity observed using one-micron, three-micron, and five-micron-sized microbead is respectively around 5.66 x 10−17 cm2s−1
, 3.45 x 10−17 cm2s−1
, and 1.71 x 10−17 cm2s−1
. Thus, the nanogold diffusivity observed using one-micron-sized microbead is greater than three-micron and five-micron-sized microbead, which can be concluded that the bigger of microbead size used, the smaller diffusivity obtained.
The advantage of blown fiber technology for out site plan network deployment than conventional cabled optical fiber system in Depok apartment project
Authors: Priyo Hutomo Sri , Wibisono Gunawan 
Published: June 2020

Abstract
The current deployment of optical fiber networks in several major cities in Indonesia is intensive. Development of fiber as the backbone or last mile for special services and almost all of us have encountered. Maybe one of them is a network deployment studio in one of the Apartment Projects in Depok City. The population of Depok reaches 4% per year higher than the national growth, the number of students and students who reach more than 80 thousand who live in Depok from only 2 campuses namely the University of Indonesia and Gunadarma University make the residential business growth both in the form of residential horizontal (boarding rooms) ) or vertical residential (Apartment). Developers are competing to build Apartments which, of course, are potential new revenue and new markets for Internet & Pay-TV Providers. The problem of the length of time the deployment of network infrastructure, traffic is not taken into account and still prioritizes the quality and cost that remains efficient become an issue of concern to the Provider. Blown Fiber is a new technology with four main components namely products, blowing devices, optical fiber bundles, and terminating/connecting hardware. This technology results in more cost-efficient, more flexible designs, better work costs in installation, termination and cheaper testing compared to conventional optical fiber deployment technology. The implementation of Blown Fiber in a deploying studio in one of the Apartments in Depok specifically on the Out Site Plan (OSP) turned out to provide convenience and acceleration in terms of network deployment.
Automatic take-off landing quadcopter control on dynamics platform
Authors: Dutasatria Akmal , Darma Surya 
Published: June 2020

Abstract
SAR (Search and Rescue) become an interesting topic discussed worldwide because of its urgency to save humans life. Recent natural disaster in Palu, Central Sulawesi Indonesia demonstrates the lack of mobility which can cause a problem for the SAR team reaching the victim within the isolated area. Uneasy job for the SAR team become more challenging in time response and in finding and saving disaster victims. Consider the fact, there is a need to develop more efficient and effective way to search and rescue the survivors. The using of Quadcopter can answer the problems. Unfortunately the Quadcopter still conventionally controlled by a remote and further it has to be on static ground whenever its take off or landing. This paper reports a Quadcopter fitted with dynamical autonomous take off and landing systems. Thus it can carry out the advantage from a tactical SAR vehicles or other moving platforms. Using object detection approach, The navigation system in an image-based control quadcopter or called visual servoing is the use of machine vision as a control of the closed loop position for movement. Images can be detected using YOLOv3 Real Time Object Detector, and tracks it by estimating the motion of objects in successive video frames. By using 3D reconstruction method the distance and position of the designated target and the quadcopter could be determined. Then the estimation of the relative position of the object is used as input for the control system on the quadcopter using PID controller. The controller commands the quadcopter to approach the target, while the image processing checks the relative position between the two. If the position satisfies the minimum landing parameter, then the controller commands a landing. While for take off we implement the controller to maintain its equilibrium and sufficient power to throttle.
Parasitic resistance calculation of PV module at various irradiance based on three condition Lambert-W
Authors: Umar Lazuardi , Wati Agustina , Wiyadi Eri , Hamzah Yanuar 
Published: June 2020

Abstract
Serial and parallel parasitic resistances of the photovoltaic (PV) module are needed to determine the characteristics and performance of the module. Its values are constant for a long period operation. Characterization of PV modules using direct irradiation of sunlight is rather difficult to obtain constant radiation under a clear sky condition and the measurement should be taken in a short time period less than 5 minutes. In this work, Rs and Rsh values were determined by characterizing PV module at various levels of sunlight intensity and modeled after Lambert-W function. A Solarland SLP100S-12 monocrystalline 100Wp photovoltaic module was illuminated at different intensity 100%, 75%, 50%, and 25 % using filters covered the PV module surface. Based on the measured I-U curves, three important parameters are generated for the Lambert-W calculation: short-circuit current (Isc), open-circuit voltage (Uoc), and also the current-voltage at the Maximum Power Point (Imp, Ump). The calculated values were 0.63Q + 0.027 for Rs and 93.2Q + 0.588 for Rsh and relative constant for filter variation which are not influenced by the intensity of the sun.
Prediction system for pH measurement on Brassica oleraceae (Red Cabbage) using machine learning regression
Authors: Wulan Hastuti Dian , Harahap Marlina , Adila Ferdiansyah Faizal , Harmoko Saputro Adhi , Imawan Cuk 
Published: June 2020

Abstract
pH is an important unit to represent the chemical condition of a liquid, solid substance, food nutrition, and microbial activity as well. pH value is also commonly used to detect the behaviour of chemical substances. Measurement of pH value can be shown by color changes of the substance based on the acidity condition from the measured environment. In this research, the colorimetric based on machine learning and pH value detected from the color information for point-of-care applications. For this investigation, we used the pH buffer solution and natural dyes derived from Brassica oleraceae (Red Cabbage) that shows colorimetric response gradually shifted from red to green along with the increasing of pH from 2.00 (acid) to 11.00 (alkaline). In this paper, we propose a method for predicting pH value based on Artificial Neural Network Regression (ANNR) and K-Nearest Neighbour Regression (KNNR) with RGB, HSV and LAB color space. As a result, the performance (99.83% ± 0.11) of this method could estimate the pH reasonably well for point-of-care application.
Design and analysis of air quality monitoring system PM10 and PM2.5 integrated with weather parameters (a case study on Margonda Raya street Depok)
Authors: Simanullang R , Soekirno S , Larassari H A 
Published: June 2020

Abstract
An observation of air quality is very important related to the increasing pollution in Indonesia resulted from the use of motor vehicles, industrial activities, wildfires, and others. It triggers the increase of PM10 and PM2.5 particulate pollutants in the atmosphere. Meteorological factors influence the fluctuation of PM10 and PM2.5 concentration. The solution to overcome the problem is the design of IoT-based PM10 and PM2.5 digital monitoring system that is integrated with parameters of temperature, humidity, wind speed and direction. The design system consists of sensor hardware ZH03A laser dust sensor, SHT11 temperature and humidity sensor, and JL-FS2 wind speed and direction. Data were processed on Arduino ATMega2560-based microcontroller and then sent to the server using internet so that they could be displayed on website. The information system on the website was developed using PHP and HTML. The device had been tested with calibration and comparison methods. As the results of the research, the device could be operated properly and the measuring results were displayed on web in real time to provide air quality information. The correlation of PM source concentrations with meteorological parameters is that temperature (positive correlation), wind speed (positive correlation), relative humidity (negative correlation) and based on the direction of wind, the area is dominated by west winds.
Developing a system to determine impact force in tennis
Authors: Bakhtiar Syahrial , Harris Sujae Ian , Kudo Shige , Rizal Wan Zakaria Wan , Ong Alex , Hamill Joseph 
Published: June 2020

Abstract
The purpose of this project was to develop a system that could estimate impact force in tennis forehand stroke. Such estimation is critical feedback during training. Method: A tennis racket was used for this study. A strain gauge was embedded into the body of the racket. Changes in strain measurement (analogue signal) was converted to digital signal using the AD converter and was synced with the motion capture system. Calibration of the strain gauge was conducted by hanging fixed weights of equal increment on the racket and expressed as a relation of fixed weight mass and strain data (captured on motion capture) using linear regression equation. The equations summation of forces was used to determine the impact force. Results and Discussions: The peak impact resultant force estimates were similar to those reported in the literature. Despite the sampling frequency of 200Hz, data estimated from this study may give a representation of peak resultant impact force as the data were not filtered. Conclusion: The system developed in this study may be suitable to estimate peak resultant impact forces. Coaches could simply use the data gathered and in future, synchronise with an app on mobile devices to establish impact force directly and instantaneously with acceptable accuracy.
Simple gold nanoparticles production method by ablated laser: Diameter modification
Authors: Pambudi S , Isnaeni  , Handoko D 
Published: June 2020

Abstract
Recently, an improved interest in gold nanoparticles (AuNPs) with an interesting phenomenology such as plasmon resonance, has been aroused. There are many methods to synthesized AuNPs. We have successfully fabricated AuNPs base on laser ablation method. The pulsed Nd:YAG laser ablated at 1064 wavelength to a pure gold plate. The frequency was adjusted in low range to produce low energy density laser beam. Three different solution (aqueous, polyvinyl alcohol and polyethylene glycol) was applied to modify AuNPs diameter. The modified nanoparticles diameter was observed by mean UV-Vis spectroscopy exhibit different absorption peak wavelength. For comprehensive discussion, the spectrum was compared to simulation tool on MATLAB. Interestingly, AuNPs diameters have been modified by using different solution.
Influence of the liquid injection hole to ripple frequency of the QCM sensor
Authors: Pratiwi Retno A , Akbar Muchammad A , Sakti Setyawan P 
Published: June 2020

Abstract
The liquid injection is one of the crucial roles in the used of QCM sensors in a liquid application. Liquid sample injection should be controlled. As the liquid injected onto the QCM sensor surface using peristaltic pump, a short pressure pulse exists. The short pulse affect the stability of the resonance frequency of the QCM sensor. The pulsation flow of the peristaltic pump leads to a ripple resonance frequency of the QCM sensor. Resonance frequency measurement of the QCM sensor in the reaction cell injected using the peristaltic pump has been measured. Different injection holes size was made to observe the effect to the sensor stability. The result showed that frequency of the QCM sensor has a ripple frequency as the liquid injected using the peristaltic pump. It was observed that bigger injection hole at the exit flow resulted in a smaller ripple frequency. Further investigation is required to get the smallest ripple p frequency during the liquid injection using the peristaltic pump.
Synthesis of silver nanoparticles (AgNPs) using Sodium Chloride (NaCl) for Iron (III) ions detection based on colorimetric and optical changes
Authors: Retno Yuniarni Diah , Intan Pratiwi Nur , Umar Aminah , Imawan Cuk 
Published: June 2020

Abstract
Iron ions (Fe3+) is one of the most hazardous metals found in the water supply. The contamination of that metal originated from the corrosion process of pipes and cracks from waste disposal sites. High levels of iron would affect a bad impact to the environment. The current study aimed to synthesize silver nanoparticles (AgNPs) for heavy metals detection using a method which depends on colorimetric and optical changes. The approach to synthesize AgNPs adapted from polyol reduction method, i.e. by mixing polyvinyl alcohol (PVA), sodium chloride (NaCl) and silver nitrate (AgNO3). The colorimetric response of AgNPs to Fe3+ ions was observed visually and measured quantitatively by a spectroscopic method using a UV-Vis spectrophotometer. This work shows that the colloidal AgNPs were sensitive to identify Fe3+ ions.