bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fuzzy kernel k-medoids application with fisher score feature selection for predicting bank financial failure
Authors: Rustam Z , Saragih G S 
Published: June 2020

Abstract
The bank financial failure has a huge impact on the real sector, households and can even cause knock-on effects for other banks, therefore is important to predict bank financial failure. Prediction bank financial failure is like an early warning system for bank, because a bankruptcy doesn’t happen suddenly but there are indications that can be detected that is financial statement. Financial statement will be extracted to 6 components of CAMELS. The 2019, we had predicted bank financial failures using random forest with the data that used in Boyacioglu, Kara and Baykan paper (2009), however in this research we will make novelty by using fuzzy kernel k-medoids. Based on our results, fuzzy kernel k-medoids using RBF kernel with σ = 0.1 and 60% composition of training data has 100% for accuracy, sensitivity, precision, specificity, and f-score with 0.9 sec running time. If we compare to our previous research by random forest, fuzzy kernel k-medoids gives the highest accuracy prediction, but if we compare to Boyacioglu, Kara and Baykan research (2009), it’s has the same accuracy but with fuzzy kernel k-medoids, we can use only 60% of training data to learn.
Comparison of multinomial logistic discriminant analysis (mlgda) and classification and regression tree (cart) performance in classifying the impact of working children
Authors: Amri M Z A , Sumertajaya I M , Syafitri U D 
Published: June 2020

Abstract
Working children have some impacts that befall them. They are hampered by access to education, exploited in working hours and even experience the two impacts at the same time. This research focus on applying and comparing the classification performance of the impact of working children by multinomial logistic discriminant analysis (MLgDA) and classification and regression tree (CART). MLgDA is useful for dealing with cases that do not meet the linearity assumption and suitable for data containing numerical and categorical predictors. CART is not affected by outliers, collinearity and heteroscedasticity among predictors. The data sourced from National Labor Force Survey 2018. All predictors are categorical and characteristics between regions are different. More than half of working children in Indonesia face some impacts, reaching 54.31%. The best classification performance is produced by MLgDA, with an accuracy of 78.73%. MLgDA is superior to CART in almost all measures.
Analysis of Factors Affecting Adolescent’s Hypertension in Banda Aceh, Indonesia
Authors: Rahayu L , Munawar  , Kesuma Z M , Aini D 
Published: June 2020

AbstractHypertension is a heart and blood vessels disorder characterized by an increase of blood pressure. Hypertension is also known as the silent disease or the silent killer because the patients do not know they had hypertension or not before checking their blood pressure. This research aims to identify any factors that affect hypertension in adolescents and analyze the probability of hypertension in adolescents. The data in this study were obtained from the adolescent nutrition status survey in 2017 in Kota Banda Aceh. The method used to analyzed the data is binary logistic regression method. This method was chosen because the response variable is dichotomous, that is the events of hypertension for adolescents. The results of this research shows that the odds ratio of predictor variables that influenced the incidence of adolescent hypertension was 0.893 (0.835-0.955), 1.052 (1.015-1.091), and nutritional status 0.255 (0.069-0.942) with 95% confidence interval. Based on the model obtained can be known that the probability of hypertension for adolescents and the accuracy level of the model is 96.8%.
Gender Classification Based on Face Recognition using Convolutional Neural Networks (CNNs)
Authors: Yuda R P , Aroef C , Rustam Z , Alatas H 
Published: June 2020

Abstract
Biometrics are physical or behavior characteristics of human that can be used to identify someone. One form of physical characteristics possessed by humans is fingerprints, retinal scanning, face and hand geometry, while one form of behavioral characteristics possessed by humans is handwriting, signatures, mouse usage analysis, walking patterns, etc. Basically, physical characteristics are more easily observed than behavioral characteristics. Therefore, physical characteristics are more often used in many aspects of security. One of the most common physical characteristics is face. By seeing the face, we can find out or predict how old they are, their gender and even their expression. However, there are still many mistakes in predicting a gender through person’s face. In fact, there are still many crimes in falsifying self-identity (such as gender). So, we need a method that is able to classify identity (gender) based on a person’s face appropriately. One method that can be used is Convolutional Neural Networks (CNNs). Later, CNNs will classify a person’s gender (male / female) based on a person’s face image data. And based on.
The Relation between Hölder Continuous Function of Order α ∈ (0,1) and Function of Bounded Variation
Authors: Wibowo S , Kurniawan V Y , Siswanto  
Published: June 2020

Abstract
The Cantor ternary function is the most famous example of a continuous function of bounded variation for which it satisfies the Holder continuous function of order α = log3 2, but does not satisfy for order α = 1. In this paper, based on previous work of Hölder continuous function of order α ∈ (0, 1) and using F
α
- calculus on fractal set F, we show the relation between the Hölder continuous function of order α ∈ (0, 1) and function of bounded variation. In particular, we give the necessary and sufficient condition for the variation of function satisfies Hölder condition or bi-Hölder condition.
An improvement on the chaotic behavior of the Gauss Map for cryptography purposes using the Circle Map combination
Authors: Suryadi MT , Satria Yudi , Prawadika Luqman N 
Published: June 2020

Abstract
Chaos based cryptography has becoming an interesting topic lately, as it utilizes chaotic systems properties for secure key concealment. Many chaotic functions are discovered, constructed, and used time over time for this purpose, which will be our main aim here. Two well known maps that has been known for exhibiting chaotic behaviors are the Gauss Map and the Circle Map, where the Circle Map has unlimited chaos potential, while the Gauss Map’s is much weaker and limited. In this paper, we investigate computationally using Python whether the Gauss Map can be improved by combining it with the Circle Map, allowing exploitation of greater chaotic behaviors. For this purpose, an improved version of the Gauss map is constructed, from which, we plot its bifurcation diagrams and Lyapunov exponents graphics, and show that it has a good potential to be a random number generator (RNG) using the NIST test, as these are the three main aspects of chaotic maps utilized in chaos based cryptography. The results obtained from this observation shows that composing the Circle Map into the Gauss Map, along with several manipulations, generates a significantly improved version of the Gauss Map, as it has a bifurcation diagram with much higher density, much higher Lyapunov exponents, and mostly better P-Values from the NIST tests, although it is still not fully suitable for a RNG. The manipulations done here, which aims to conserve the maps ranges to stay within the chaotic intervals and position the Circle Map to be the “variable” of the Gauss Map, allows the chaotic behaviors from the original maps to be bequeathed and strengthened in the new map.
Classification of thalassemia data using random forest algorithm
Authors: Aszhari F R , Rustam Z , Subroto F , Semendawai A S 
Published: June 2020

Abstract
Thalassemia is a blood disorder that occurred in Southeast Asia. Thalassemia cannot be cured, but early detected thalassemia with screening process is the best way to prevent thalassemia disease. If early detection is done, patients can get the right treatment. It helps them increase their life expectancy and reduce the risk of thalassemia to the next generation. In this paper, we use thalassemia data and propose a random forest method to classify thalassemia disease well and accurately. The result concludes that the random forest algorithm can give the best accuracy, precision and recall which is 100 percent by using multiple five in range of 70 to 85 percent as the training data.
Identification of topics in News Articles Using Algorithm of Porter Stemmer Enhancement and Likelihood Classifier
Authors: Rukmi Alvida Mustika , Andriyani Devi , Mukhlas Imam 
Published: June 2020

Abstract
Every piece of information contained in a story sometimes has a variety of themes and seems not specific so there is difficulty in digesting information simultaneously. This requires grouping based on the topic relevance of the news. This grouping can make it easier for readers to get the information in accordance with the topic you want to read. Each news group must have different information characteristics so that we need a special algorithm that is able to handle topic discovery and classification using training data on many Indonesian news articles. This research will apply an algorithm of Porter Stemmer Enhancement in the stemming process and Likelihood method for news classification based on categories and identification of topics. Based on the test results using 900 training data and 90 test data, obtained a fairly high accuracy, namely 95.56% for category classification and 97.78% for topic identification.
Sentiment analysis of customer response of telecommunication operator in Twitter using DCNN-SVM Algorithm
Authors: Firdausi I E , Mukhlash I , Gama Athyah D S , Hidayat N 
Published: June 2020

Abstract
Along with the development of the times, social media is in great demand by various circles of society because social media allows users to express their thoughts or feelings freely. It is important for a company to know public responses about the product or service offered. With this public response, companies can analyze customer needs and plan more satisfying products or services. To be able to know the sentiments of responses, it is necessary to classify responses. Therefore, in this study used the Deep Convolutional Neural Network (DCNN) method as a feature extraction and Support Vector Machine (SVM) as its classification. The performance results of this research are 63% for accuracy, 63% for precision, and 50% for recall of test data.
KLEVER: An experiment to measure BR(

KL→π0νν¯

) at the CERN SPS
Authors: Moulson M 
Published: June 2020

Abstract
Precise measurements of the branching ratios for the flavor-changing neutral current decays

K
→
π
ν

ν
¯

can provide unique constraints on CKM unitarity and, potentially, evidence for new physics. It is important to measure both decay modes,

K
+

→

π
+

ν

ν
¯

and

K
L

→

π
0

ν

ν
¯

, since different new physics models affect the rates for each channel differently. The NA62 experiment at the CERN SPS will measure the BR for the charged channel to better than 20%. The BR for the neutral channel has never been measured. We are designing the KLEVER experiment to measure BR(

K
L

→

π
0

ν

ν
¯

) to ∼20% using a high-energy neutral beam at the CERN SPS. The boost from the high-energy beam facilitates the rejection of background channels such as K

L
→ π
0
π
0 by detection of the additional photons in the final state. On the other hand, the layout poses particular challenges for the design of the small-angle vetoes, which must reject photons from K

L
decays escaping through the beam exit amid an intense background from soft photons and neutrons in the beam. We present findings from our design studies, with an emphasis on the challenges faced and the potential sensitivity for the measurement of BR(

K
L

→

π
0

ν

ν
¯

).
A new charged particle detector for the KOTO experiment at J-PARC
Authors: Kim H M 
Published: June 2020

Abstract
We report the performance of a new scintillator array Downstream Charged Veto (DCV) for the J-PARC KOTO experiment to suppress the K

L
→ π
+
π
−
π
0 decay background. Since the background originates from undetected charged pions passing through the beam hole of the electromagnetic calorimeter, the DCV detector was installed in vacuum downstream of the calorimeter. The DCV is composed of two plastic scintillator pipes read out by Multi Pixel Photon Counters through wavelength shifting fibers. The light yield was found to be 60 photoelectrons/0.8 MeV in a test bench when cosmic-rays pass through the center of the DCV. Energy calibration was performed using cosmic-rays after installation.
Object detection for autonomous search and rescue quadrotor application
Authors: Taqwim Safrudin Ahsanu , Darma Surya , Sukirno Santoso 
Published: June 2020

Abstract
Indonesia is located in the ring of fire zone. It causes a high risk of disaster threats. Volcanoes and earthquakes are unpredictable sources of natural disaster in a massive size, which many times caused an inaccessible area. We need to prepare how to overcome this situation. Quadrotor is an aerial vehicle that can access the disaster location easily and passing through many obstacles. Its autonomous ability can increase searching efficiency. Object detection will be a primary parameter which responsible to move the quadrotor. In this paper, we proposed to use cascaded Canny Edge with SURF clarified with SIFT to get more robust detection in real-time application. Our method is using image detection in guiding the quadrotor movements. First, we detect the object by cascaded Canny Edge with SURF and ORB, both of them checks whether the target is true or not. When both methods approved that target have been detected, then the process uses only the cascaded Canny Edge with SURF methods to locate the victims and to decrease the image processing time. Output of this image detection method applies into our quadrotor control movements, by using simple Proportional Integral Derivative (PID) control. This visual feedback control guides the quadrotor flies to the target and note the victim position and reported in its log. This paper will also provide data comparison between our method with the only SURF and the only ORB method to do the same task.
Comparison between two-stage and three-stage Peltier thermoelectric coolers driven by pulse width modulation
Authors: Kamasi Daniel D. , Zainulloh Muhammad , Nadhir Ahmad , Sakti Setyawan P. 
Published: June 2020

Abstract
Thermoelectric device is one of electronic devices commonly used as temperature sensors, electrical generators, and cooling devices. Peltier thermoelectric cooler as a promising device offers greater performance in terms of electronic cooler, especially for computer components. However, the performance of single-stage thermoelectric cooler is limited. For certain optoelectronic devices, it cannot achieve a desired temperature to pump heat from the devices. In addition, to drive temperature of the thermoelectric coolers, a complex interface circuit should be made. In this research, two-stage and three-stage cascaded Thermoelectric Coolers driven by Pulse Width Modulation using Arduino and H-bridge Motor Driver were examined. The three-stage Peltier thermoelectric cooler showed better performance than the two-stage, where it achieved higher temperature difference between hot and cold. Both cascaded thermoelectric coolers had distinct temperatures in each interstage connection, with increasing values from near cold side to near hot side.
Fog prediction using artificial intelligence: A case study in Wamena Airport
Authors: Dewi Ristiana , Prawito  , Harsa Hastuadi 
Published: June 2020

Abstract
Fog is one of the atmospheric phenomena that affect airport operations. It can reduce visibility which impacts flight operations (taxiing, take-off, landing). Therefore, fog prediction is needed to support flight safety. The biggest challenge in making weather predictions is the chaotic and complicated process of the atmosphere. This research tries to use artificial intelligence (AI) to predict fog events at Wamena Airport. Design of model prediction using hourly synoptic data set from January 2015 till May 2018. Variables input such as dry ball temperature, wet ball temperature, dew point, relative humidity, cloud cover, wind direction, wind speed, visibility, and present weather for the past six hours ago are used to predict fog or no fog events. We performed a grid search parameter tuning on five algorithms such as Distributed Random Forest (DFR), Deep Learning (DL), Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), and Extreme Randomized Tree (XRT). The best model is obtained from the ensemble model Stacked Ensemble (SE) with an accuracy of above 90% for the fog forecast from one to three hours later.
Monitoring distribution system of carbon monoxide and surface ozone based on GPS and microcontroller
Authors: Indrawan A , Sudarmaji A , Ihsan F R 
Published: June 2020

Abstract
Jakarta is the most populous city in Indonesia. This is directly proportional to the increasing number of motorized vehicles in Jakarta. The number of Jakarta motorized vehicles causes air pollution in Jakarta to become uncontrollable. Air pollution is the presence of one or more physical, chemical or biological substances in the atmosphere in an amount that can endanger the health of living things. Ozone and Carbon Monoxide, including dangerous elements that exist in air pollution. Ozone is a pollutant that causes significant respiratory disease. Carbon Monoxide is dangerous because it can form compounds with Hemoglobin to form HbCO, and this is toxic to the blood. We can find out the quality of the air somewhere, through the content of Ozone and Carbon Monoxide in the area. At present the number of Ozone and Carbon Monoxide gauges in Indonesia is still small, so that not all regions have been monitored for air quality conditions. The purpose of this design is to determine the level of air quality and to monitor ozone gas and carbon monoxide automatically based on Global Positioning System (GPS) and portable. This system was built using Arduino Mega 2560 (used as a controller), ozone sensor, Carbon Monoxide gas sensor and GPS (used as a timing and determinant of location). Programming is done using the Arduino IDE application. The measurement results are stored using a microSD Card, and displayed on the LCD. This system can inform the condition of air quality in an area, because this tool is portable (can be carried anywhere), in real time and data generated every second.
Measurements and analysis of crystal structures of activated carbon of empty fruit bunch from oil palm biomass waste
Authors: Ikhsan M , Ramli R 
Published: June 2020

Abstract
The purpose of this research is to measure and analyze the properties of activated carbon from biomass waste of palm oil empty fruit bunch by using x-ray diffraction (XRD). Biomass waste that has been clean and dried is carbonated at 400°C for 10 minutes to produce charcoal. Next, the charcoal is activated through a chemical process with the activating agent NaOH by soaking it for twelve hours. Activated carbon with 2N activated agent NaOH have the best properties because it has a largest surface area (27.07). The results of this research are useful for technological developments of activated carbon, and provide an economic added value to biomass waste.
Implementation of artificial neural networks for very short range weather prediction
Authors: Wanugroho G B , Martarizal  , Putra R M 
Published: June 2020

Abstract
Weather conditions are a significant factor for various sectors such as transportation safety, development, health, etc. Therefore, high development is needed in forecasting future weather conditions. Many ways are used to predict weather conditions. Along with the development of technology now, weather prediction can be made using Artificial Intelligence (AI) technology or artificial intelligence so that the results obtained are more optimal. In this study, the artificial neural network used has a feedforward neural network algorithm using training data consisting of temperature, air pressure, air humidity, wind speed, hourly wind speed at the Juanda Meteorological Station in Surabaya in Januari 2019 with the target is intensity of rainfall. Furthermore, the data was released in the period of 1 January 2019 to 31 Januari 2019. Based on the analysis results, the Artificial Neural Network model has a fairly good performance in predicting an increase in rainfall in Surabaya. The best model is considered by a model with architecture 7 - 60 - 1 with an estimate correlation is 0.87, with an error value of -0.03. With this model, it is expected to become one of the forecaster considerations in making special weather forecasts at intervals every hour.
Measurement and analysis of air quality impacts caused by CO and NO2 on Margonda Raya street Depok
Authors: Larasari H.A. , Soekirno S. , Simanullang R. 
Published: June 2020

Abstract
Based on data from the Central Statistics Agency (BPS) in 2019, the population of the City of Depok has now jumped to 2,254,513 people. Analysis of air quality is important for the people of Depok. The biggest ambient air parameters are Nitrogen Dioxide (NO2) and Carbon Monoxide (CO). Land transportation activities are among the biggest causes of decreasing air quality in the city of Depok. One type of land transportation is motor vehicles. A high growth rate of motor vehicles can cause a serious impact on the environment. Traffic jams and noise can cause air pollution. This research was conducted around Margonda Raya Street. In this research, the components used are Arduino Mega 2560 Microcontroller, SHT11 Temperature and Humidity Sensor, MQ-7 CO Sensor, MQ-135 NO2 Sensor, Wi-Fi Module ESP8266, Wind Direction and Wind Speed JL-FS2 Sensor. Data from the designed tool will then be sent to the cloud server which will then be collected in a web-based application. The results will be analyzed to determine for fluctuations in CO and NO2 gas concentrations and the impact of CO and NO2 on air quality around Margonda Raya Street Depok. The MQ-7 and MQ-135 Sensor test results show a very strong correlation value with the value R2 = 0.9906 for MQ-7 and R2 = 0.9988 for MQ-135. The results of the test show that in the Region has the highest CO value of 202.67 around 3:00 to 4:00 p.m. and the highest NO2 value of 227.34 around 2:00 p.m., the data values of CO and NO2 are still in the medium category, this is related to the decline in community activities on weekends.
Calibration of mechanical systems of in-house dynamic thorax phantom for radiotherapy dosimetry
Authors: Wijanarko S , Sudarmaji A , Pawiro S A 
Published: June 2020

Abstract
The purpose of this study is to calibration a mechanical system of In-house Dynamic Thorax Phantom so that it can be used as a dosimetry on the Radiotherapy Real-Time Position Management (RPM) technique. In order to ensure the RPM technique had properly function, we need to employ a quality control tool that represents moving objects on the human body. Inspired by the CIRS Dynamic Thorax Phantom model 008A, we developed an In-house Dynamic Thorax Phantom using three Nema-17 stepper motors with 200 steps in a cycle as a mechanical system. In addition, the first stepper motor was used to move objects in superior-inferior direction; the second one was used to move the RPM marker in anterior-posterior direction; while the third one was used to move objects in rotation. Calibration of mechanical system was performed by finding out the number of counts needed each direction. Especially for rotational system, they were calibrated with Optical Interrupt Device. The result of calibration of the mechanical system between the actual readings compared with the system specification is 0.0% and < 1% for the measurement testing.