bluuebunny/crossref_metadata_2025_split huggingface dataset:
Non-Markovian thermal-bath-induced Brownian motion in velocity space in the presence of a magnetic field at arbitrary direction
Authors: He Guitian , Tang Guoji , Tian Yan , Luo Maokang , Stanley H Eugene 
Published: February 2021

Abstract
In this work, from the perspective of statistical mechanics, the statistical properties of charged-particle motion in a microwave field and a magnetic field with a general direction described by a generalized Langevin equation subjected to an intrinsic noise with a power-law time decay correlation function have been studied. Using the general expansion theorem for the Laplace transform, the drift velocity of a charged particle in three directions can be expressed in terms of the relaxation functions. Based on the linear response theory, the expression of the complex susceptibilities, the spectral amplification, the stationary form of current density, and the power absorption have been obtained. It is noteworthy that the stochastic dynamics of a charged particle could be induced by fractional Gaussian noise. Additionally, the variances and covariances of charged particles have been studied based on the relations between relaxation functions and memory kernel functions.
Numerical Solution of SIRS model for Dengue Fever Transmission in Makassar Citywith Runge Kutta Method
Authors: Sanusi W , Pratama M I , Rifandi M , Sidjara S , Irwan  , Side S 
Published: February 2021

Abstract
The aim of this study was to obtain a SIRS model solution for the spread of dengue fever (DF) using the 4th order Runge-Kutta method. The data used was data on the number of dengue cases in Makassar city. The method used is the 4th order Runge Kutta Method. The results of this study are numerical solutions of the SIRS model on the spread of DF in Makassar city using the Runge Kutta method; Model simulation analysis shown that the estimated number of dengue cases in Makassar city, so that the government can take steps to prevent the spread of dengue in Makassar city.
An Over view on intuitionistic fuzzy topological spaces
Authors: Abdy M , Zenin S , Irwan  
Published: February 2021

Abstract
We present a brief overview some fundamental results on the intuitionistic fuzzy topological spaces, and give some introductory results about fuzzy open set, fuzzy closed set, fuzzy neighborhood, fuzzy interior set, fuzzy continuity, fuzzy compactness and fuzzy connectedness in these spaces.
SEIR Mathematical Model of Seizure fever in Infants Under 5 Years Old in Makassar City
Authors: Side S , Sari I , Sanusi W 
Published: February 2021

Abstract
This study aims to obtain a mathematical model in seizure fever; analyzing the model, simulating a seizure fever model in Makassar City, knowing the prediction of the number of seizure fever in Makassar City. This research is a study of theoretical and applied mathematics; Mathematical models in seizure fever are Suspected, Exposed, Infected and Reovered (SEIR) models; Analysis of the model using the generation matrix, simulation models with Maple software using secondary data on the number of people with seizure fever at the Haji Hospital in Makassar. The results obtained are the SEIR model in seizure fever; The results of the model analysis explain that seizure fever are at a stable stage. Model simulation results show the tendency of seizure fever in Makassar City especially in Tamalate sub-district experiencing a downward trend every year.
Comparing the performance of T
2 chart based on PCA Mix, Kernel PCA Mix, and Mixed Kernel PCA for Network Anomaly Detection
Authors: Mashuri M , Ahsan M , Kuswanto H , Prastyo D D , Khusna H , Wibawati  
Published: February 2021

Abstract
Statistical Process Control (SPC) is not only used to monitor the quality of manufacturing processes and services but also is applied to detect intrusions in the network. Hotelling’s T
2 chart is the SPC method that has been widely developed for intrusion detection. However, in its application, the conventional Hotelling’s T
2 chart has several drawbacks such as less effective when used to monitor large observations and quality characteristics. Conventional Hotelling’s T
2 chart is not perform-well for non-Gaussian distributed data. Also, the current conventional control chart has not been able to monitor the processes which have mixed quality characteristics. To overcome these weaknesses, two types of the control chart is proposed in this study, namely, the multivariate control chart based on Principal Component Analysis (PCA) Mix and Kernel PCA. For Kernel PCA chart, two schemes are developed, that is Kernel PCA Mix and Mixed Kernel PCA control charts. Kernel Density Estimation (KDE) is employed to estimate the control limits of the developed charts. In monitoring the network intrusion, the proposed control charts are applied to well-known NSL-KDD dataset. The evaluation performance shows that the PCA Mix chart can detect attacks occurred on the network more accurate and faster compared to the Kernel PCA Mix and Mixed Kernel PCA charts.
Bringing Real Analysis Subject Into Real Life: an Experimental Research for Prospective Teacher of Mathematics Study Program Using Realistic Mathematics Education
Authors: Ashari N W , Salwah  
Published: February 2021

Abstract
This research focus on describing the method used by the lecturer in understanding Prospective Teacher about the definition of Vε
(a). This research conducted in Mathematics Education Study Program, Cokroaminoto Palopo University. The data collected by using interview, observation sheet, and questionnaire. Realistic Mathematics Education (RME) used by the lecturer to deliver the material of Vε
(a). In this case the lecturer introduced three principles of RME such as guided reinvention, didactical phenomenology, and self-developed model to the prospective teacher. The result shows that prospective teachers are able to understand the definition of Vε
(a). The prospective teacher can solve or proof any problem and theorem related to definition of Vε
(a).
A Hybrid GSTARX-Jordan RNN Model for Forecasting Space-Time Data with Calendar Variation Effect
Authors: Hikmawati F , Suhartono  , Prastyo D D 
Published: February 2021

Abstract
Generalized Space-Time Autoregressive (GSTAR) is one of the space-time models. The GSTAR model has its limitations of not being able to model a nonlinear time series, and this can be overcome by applying a hybrid model on GSTAR. This research aims to propose modeling hybrid Time Series Regression (TSR) and hybrid GSTARX-Jordan RNN, where TSR and GSTARX model as a linear component involving the predictor variable, which is an effect of calendar variation and Jordan-RNN as a nonlinear component. This research focused on a simulation study to evaluate the goodness of the model hybrid GSTARX-Jordan RNN. There were some scenarios experimented, i.e. simulation studies in data that have linear noise and non-linear noise. The results showed that a hybrid GSTARX-FFNN, GSTARX-DLNN, and GSTARX-Jordan RNN model is the best model for predicting simulation data containing trend, seasonality, calendar variations, and nonlinear noise series compared with TSR, and GSTARX models. In general, it is in line with the results of the 2018 M4 forecasting competition show that combined models or hybrid models tend to provide more accurate forecast performance than forecast results with individual models.
Evaluating the K-Means Analysis in Clustering Area Based on Estates Productivity in Tana Luwu Using Silhouette Index
Authors: Poerwanto B 
Published: February 2021

Abstract
Tana Luwu is an area in South Sulawesi Province that consists of 4 districts or city, namely Luwu, East Luwu, North Luwu, and Palopo. Those regions have abundant natural resources, one of which is plantation crops. In this manuscript, the first objective was to group the 45 sub-districts in those districts into 2 to 5 clusters based on their productivity on providing plantation crops using K-Means Algorithm. Variables used in this study are coconut, palm, coffee, pepper, cocoa, cloves, and land area. Secondly, after the clusters were formed, those were evaluated using Silhouette Index to find out the most optimum number of clusters. As the result, it was found that the optimum number was two with Silhouette Index 0,8068 while the value of 3 clusters is 0,6468. For the values of 4 clusters and 5 clusters are 0,5029 and 0,5509 respectively.
A MGSTAR: An Extension of The Generalized Space-Time Autoregressive Model
Authors: Suhartono  , Nahdliyah N , Akbar M S , Salehah N A , Choiruddin A 
Published: February 2021

AbstractUp to now, Generalized Space-Time Autoregressive (GSTAR) models are focused only for univariate spatial-temporal data. This research proposes an extension of GSTAR for multivariate spatial-temporal data, known as Multivariate GSTAR or MGSTAR. Three studies were conducted in this research, i.e., theoretical, simulation, and applied studies. These studies were initially developed based on bivariate spatial-temporal data. A theoretical study was done by developing MGSTAR based on the framework of Vector Autoregressive (VAR) models. In this proposed MGSTAR model, the parameter estimation was obtained by implementing Ordinary Least Square (OLS) method. The simulation study showed that OLS method yielded unbiased estimator. Furthermore, the MGSTAR models have applied for forecasting CO and PM10 at three stations in Surabaya City. The results showed that MGSTAR model could explain well the dynamic relationship between variables and locations. However, based on Root Mean Square Error Prediction (RMSEP), the results showed that MGSTAR model yielded less accurate forecast than ARIMA model due to MGSTAR employed simpler order of Autoregressive. Further research is needed to expand the MGSTAR model with a higher order of Autoregressive, particularly to handle trend and seasonal order.
Modeling of Children Ever Born in Indonesia Using Fourier Series Nonparametric Regression
Authors: Octavanny M A D , Budiantara I N , Kuswanto H , Rahmawati D P 
Published: February 2021

Abstract
Nonparametric regression aims to determine the relationship between response and predictor when the data does not follow a specific pattern. Fourier series is a nonparametric regression approach which has the flexibility to follow the characteristics of data. The purpose of this study is to obtain the estimator of the nonparametric regression using Fourier series and apply the model to the fertility data. The fertility rate represented by children ever born is one of the demographic factors that determine the decline in population growth rate. The data were obtained from the Indonesia Demographic and Health Survey 2017 with children ever born as a response. The predictors are the proportion of women graduating from junior high school, the proportion of women having sex before the age of 18, the proportion of women using a modern contraceptive method, and the infant mortality rate. The relationship between response and predictors tends to have a repetitive pattern with a certain trend. The best nonparametric regression model of children ever born in Indonesia is obtained by using 3 oscillation parameters for each predictor variable with GCV = 0.0534 and R-square = 80.04%.
Feature Selection using Simulated Annealing with Optimal Neighborhood Approach
Authors: Syaiful A , Sartono B , Afendi F M , Anisa R , Salim A 
Published: February 2021

Abstract
The one of the metaheuristic approaches that can be used was simulated annealing (SA) algorithm which inspired by annealing metallurgical process. This algorithm shows advantages in finding global optimum of given function which will be used in feature selection. In this study, we will trying to combine the neighborhood size and limited approach by using data simulation comparing between two function which is Akaike Index Criterion (AIC) function and Bayesian Index Criterion (BIC) function. The result of this experiment shows that the selected variables using optimal neighborhood size and limit the selected variable provide the result of goodness model around 98% of accuracy and specificity and 94% of sensitivity compared with simulated annealing algorithms without any modification using both AIC function and BIC function, and in the simulation also shows that BIC function give better result than AIC function.
Prediction Insolvency of Insurance Companies Using Random Forest
Authors: Rustam Z , Saragih G 
Published: February 2021

Abstract
Insurance companies have an important role in economy because they support every insured companies, so it is guaranteed financially, therefore is important to predict the insolvency of insurance companies. Insolvency prediction is like an early warning system for insurance companies. Rustam and Yaurita had predicted insolvency of insurance company by using Spanish non-life insurance firm data from Prof. Dr. Maria Jesus Segovia using support vector machines and fuzzy kernel c-means as classifiers (2018). Furthermore, this research is novel based on the use of random forest as a classifier. The result obtained is reported in percentage of accuracy, both in training and testing of random forest, which was 100% while used 50% training data with entropy and the number of estimators is 100 as hyperparameters. This classification, therefore, shows the best value in contrast with prior methods, even though only 50% of training data set was used.
Schizophrenia Classification Using Fuzzy Kernel C-Means
Authors: Hartini S , Rustam Z 
Published: February 2021

Abstract
Schizophrenia is a severe mental disorder that induces the mind, feeling, and behavior. Earlier treatment for this disease is essential; therefore, the ability to predict schizophrenia also becomes essential. Fuzzy kernel c-means was proposed in this research, utilizing the data obtained from Northwestern University, which consists of 171 schizophrenia and 221 non-schizophrenia samples. There are several kernel functions; however, RBF and polynomial kernel function were used. As the evaluation, k-fold cross-validation with k = 3, 5, 7, and 10 was used, and each of the performances was analyzed. From the experiments, it was concluded that fuzzy kernel c-means using RBF kernel with σ = 0.01 and σ = 1 provides better performance than polynomial kernel with a similar running time with fuzzy c-means.
Classification of Infarction using Random Forest
Authors: Rukmawan S H , Aszhari F R , Rustam Z , Pandelaki J 
Published: February 2021

Abstract
Stroke is a condition caused by disruption in the blood supply to the brain. When the flow of blood is decreasing and resulting dead brain tissue that is called an infarction. If this condition not treated immediately and don’t get the right treatment will cause the death of the brain. Therefore, the classification of infarction is important to help increase the life expectancy of the patients. In this study, we are using infarction data from the Department of Radiology at Dr. Cipto Mangunkusumo Hospital and propose a random forest method to help the health sector for detecting infarction quickly and accurately. The best result by using the random forest method is 94.44 percent with 65 percent as training data.
The Application of Cooperative Learning Type Group Investigation to Improve Students’ Learning Activities and Learning Outcomes
Authors: Fitriani  
Published: February 2021

Abstract
This study aims to improve students’ learning activities and learning outcomes by using cooperative learning model type group investigation. This research is a classroom action research which involves 23 students of XI in SMAN 2 Siak Hulu. The research instrument consists of observation sheet, syllabus, lesson plans, guide book, group worksheet, and individual worksheet. The research is conducted in two cycles, cycle 1 and cycle 2. The research finding suggests that group investigation method can increase students’ learning activities and learning outcome. During the research, the teacher needed to consider time management in delivering the material, discussion. Then, students’ activities must be observed thoroughly. The teacher must be competent because this model requires teachers who can carry out a casual, varied and more student-focused teaching style.
Correlation between Teacher Guidance and Parent with Chemical Literation Ability of Junior High School Student
Authors: Sugiarti  , Mulbar U , Adnan  , Bahri A 
Published: February 2021

Abstract
The ability of chemical literacy of junior high school students needs to get better attention so that they can easily understand chemistry lessons at the next level. Teachers and parents should be involved in guiding students to learn so that their understanding structures are well organized and clear. This research was quantitative descriptive study that aims to find out the chemical literacy ability, form of teacher and parent guidance, correlation between teacher and parent guidance on literacy chemical of student of grade VIII on atomic, ionic, and molecular topic. The research sample of 234 students was taken by simple random for four schools at South Sulawesi. The design of this study is a survey research with the study population was all grade VIII junior high school students in South Sulawesi. The ability of chemical literacy tested consists of the ability to understand the question methods that lead to scientific knowledge and the ability to organize, analyze and interpret quantitative data and scientific information with a total of 10 indicators. The instruments used included a questionnaire, and multiple choices test forms of 10 items that had been tested for validity and reliability. The provision of questionnaires aims to determine the role of teachers in the learning process at school and the role of parents in guiding students at home. The test is used to determine the literacy ability of students. Data were analyzed using descriptive quantitative statistics. The results of the data analysis found that average of the chemical literacy ability of junior high school students in South Sulawesi is in the category. The average correlation between teacher guidance with students were high category, and the average correlation between parent guidance with students were enough category.
Measurement of Crack Damage Dimensions on Asphalt Road Using Gabor Filter
Authors: Sulistyaningrum D R , Setiyono B , Anita J N , Muheimin M R 
Published: February 2021

Abstract
Cracks are a type of road defect that is often found on highways. Crack dimension information is needed in the process of road maintenance and repair. Image processing methods have been widely used for visual detection of damage. One of them is the Gabor filter method, which has been used for texture-based defect detection. In this study, the detection and measurement of crack dimensions on the asphalt road have been carried out using Gabor Filter. The steps to measure crack dimensions consist of image acquisition, pre-processing, segmentation, labeling, and dimension measurement. The results of experiments on 15 crack images showed that the method’s accuracy reached 95.191% for the crack area length and 87,935% for the crack area width.
Reactive PowerControl of Grid-Connected Photovoltaic Power Generation
Authors: Jin LiJun , Gong XueJiao , Sun QiYa , Sha MaiChao 
Published: February 2021

Abstract
In order to solve the problem of grid-connected point voltage exceeding the limit caused by large-scale photovoltaic power stations connected to the grid, and to increase the penetration rate of photovoltaics in the grid, photovoltaic power stations should have more flexible reactive voltage regulation capabilities to provide reactive power support to the grid. To this end, this paper establishes an equivalent aggregation model for large-scale photovoltaic power plants. Under the premise that the photovoltaic power station is not equipped with reactive power compensation devices and the inverter reactive power output is zero, it is analyzed that due to the existence of line and transformer impedance, photovoltaic access reduces the grid the problem of voltage stability. Based on the above reasons, this paper proposes a three-layer reactive power control strategy for photovoltaic power plants from the perspective of the cooperation between the reactive power compensation device and the grid-connected inverter. This strategy coordinates the reactive power output between the reactive power compensation device and the photovoltaic power generation unit and between the inverters of a single photovoltaic power generation unit. Under this control strategy, the photovoltaic power plant can regulate the grid voltage more effectively, and the active and reactive power losses of the grid are minimized on the premise that the grid voltage is maintained within the required range. Combined with the photovoltaic array power reduction operation strategy, the stable operation of the power grid is ensured under the premise of a certain reactive power output capacity of the photovoltaic power station. Finally, an example is used to verify the correctness and effectiveness of the strategy.
Research on Transaction Clearing Method for Power Distribution Network Considering Reactive Power Support Ability of Renewable Energy
Authors: Huang Kangqian , Xiang Dejun , Huang Jingyin , Hu Xin , Zhou Rui , Huang Zhisheng 
Published: February 2021

Abstract
In order to solve the transaction clearing problem caused by the insufficient reactive power support ability of renewable energy, the reactive power regulation characteristics of renewable energy are studied and a power distribution network transaction clearing method considering renewable energy reactive power support ability is proposed. Wind power, photovoltaic and other renewable energy sources are generally connected to the power grid through power electronic equipment. Within the capacity control range, there are two adopted control modes including constant voltage and constant reactive power. According to the operation needs of power distribution network, a transaction clearing model aiming at the lowest power energy purchase cost is proposed. A transaction clearing method based on reactive power relaxation is proposed to improve the solving efficiency. Finally, a case study based on the REDS-32 buses system shows that the transaction clearing method can effectively consider the reactive power support ability of renewable energy sources, which could ensure that the transaction clearing results are close to the actual operation requirements and avoid the problem of bus voltage overruns affecting the safe operation of power distribution network.
Design and analysis of field test of surface unmanned boat radio
Authors: Han Qifei , Zhao Honghua , Lin Yifei , Gong Xuyin , Zhao Jian , Hu Zhitong 
Published: February 2021

Abstract
Communication distance and real-time monitoring are important performances of surface USV radio stations, which can better play the role of smooth communication information between surface USV and shore-based platform and real-time monitoring of USV dynamics, usually in the field. Test verification. This article analyzes the main problems in the field communication distance and real-time monitoring test of the surface unmanned boat radio station, introduces the evaluation method of the field wireless electromagnetic environment monitoring, designs the network environment of the shore-based computer and the surface unmanned boat industrial control computer, and designs the surface Field test plan for man- ship radio station. The test analysis shows that the design scheme is feasible and the communication between the surface unmanned boat and the shore-based platform is smooth, which can better monitor the dynamics of the surface unmanned boat, thereby improving the operation quality of the surface unmanned boat.