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University Lecturer
01 April 2021 - 30 August 2021
Post Doctoral Researcher
01 January 2021 - 30 August 2021
Post Doctoral Researcher
01 September 2020 - 01 December 2020
Senior Scientist or Principal Investigator
01 January 2016 - 30 August 2021
Rezzy Eko Caraka, received his B.S. degree from Diponegoro University, Indonesia specializing in Statistics, his M.Sc by research degree in School of Mathematical Sciences from the The National University of Malaysia, and his Ph.D. in Information Management from the Chaoyang University of Technology, Taiwan (ROC). He currently is Senior Researcher at BDSRC-Bina Nusantara University. In September 2020, he was the Postdoctoral Researcher with the Research for Basic Sciences, Department of Statistics, Seoul National University, South Korea. In January 2021, he was Postdoctoral Researcher with Seoul National University College of Medicine (SNU-Medicine). In March 2021, He serves as lecturer, Faculty of Business and Economics, Universitas Indonesia. His research interests include, but are not limited to Large-Scale Optimization, Fast Computing, Disaster Risk, Environmental Science, Sustainable Development Goals. He was co-founder of statistical software STATCAL (http://statcal.com/).Recently, he has authored new 10-book series, statistics, machine learning, spatial analysis. Email: [email protected] or [email protected] Personal website : linktr.ee/akaracyzzer
COVID-19, as a global pandemic, has spread across Indonesia. Jakarta, as the capital of Indonesia, is the province with the most positive cases. The government has issued various guidelines, both at the central and regional levels. Since it began in 2021, the planned new measures, called ‘Pemberlakuan Pembatasan Kegiatan Masyarakat Darurat’, or PPKM emergency public activity restrictions, began with the possibility that the number of active cases might decrease. Accordingly, global vaccinations were also carried out, as they were in Indonesia. However, the first phase prioritized frontline health workers and high-risk elderly people. This study conducted a causal impact analysis to determine the effectiveness of PPKM in Jakarta and its vaccination program against the increase in daily new cases. Based on this test, PPKM showed a significant effect on the addition of daily new cases and recovered cases. Conversely, the vaccination program only had a significant impact on recovered cases. A forecast of the COVID-19 cases was conducted and indicated that the daily new cases showed a negative trend, although it fluctuated for the next 7 days, while death and recovered cases continued to increase. Hence, it can be said that the vaccination program has still not shown its effectiveness in decreasing the number of daily new cases while PPKM is quite effective in suppressing new cases.
Toni Toharudin; Resa Pontoh; Rezzy Caraka; Solichatus Zahroh; Panji Kendogo; Novika Sijabat; Mentari Sari; Prana Gio; Mohammad Basyuni; Bens Pardamean. National Vaccination and Local Intervention Impacts on COVID-19 Cases. Sustainability 2021, 13, 8282 .
AMA StyleToni Toharudin, Resa Pontoh, Rezzy Caraka, Solichatus Zahroh, Panji Kendogo, Novika Sijabat, Mentari Sari, Prana Gio, Mohammad Basyuni, Bens Pardamean. National Vaccination and Local Intervention Impacts on COVID-19 Cases. Sustainability. 2021; 13 (15):8282.
Chicago/Turabian StyleToni Toharudin; Resa Pontoh; Rezzy Caraka; Solichatus Zahroh; Panji Kendogo; Novika Sijabat; Mentari Sari; Prana Gio; Mohammad Basyuni; Bens Pardamean. 2021. "National Vaccination and Local Intervention Impacts on COVID-19 Cases." Sustainability 13, no. 15: 8282.
The COVID-19 pandemic has caused effects in many sectors, including in businesses and enterprises. The most vulnerable businesses to COVID-19 are micro, small, and medium enterprises (MSMEs). Therefore, this paper aims to analyze the business vulnerability of MSMEs in Indonesia using the fuzzy spatial clustering approach. The fuzzy spatial clustering approach had been implemented to analyze the social vulnerability to natural hazards in Indonesia. Moreover, this study proposes the Flower Pollination Algorithm (FPA) to optimize the Fuzzy Geographically Weighted Clustering (FGWC) in order to cluster the business vulnerability in Indonesia. We performed the data analysis with the dataset from Indonesia’s national socioeconomic and labor force survey (SUSENAS and SAKERNAS). We first compared the performance of FPA with traditional FGWC, as well as several known optimization algorithms in FGWC such as Artificial Bee Colony, Intelligent Firefly Algorithm, Particle Swarm Optimization, and Gravitational Search Algorithm. Our results showed that FPAFGWC has the best performance in optimizing the FGWC clustering result in the business vulnerability context. We found that almost all of the regions in Indonesia outside Java Island have vulnerable businesses. Meanwhile, in most of Java Island, particularly the JABODETABEK area that is the national economic backbone, businesses are not vulnerable. Based on the results of the study, we provide the recommendation to handle the gap between the number of micro and small enterprises (MSMEs) in Indonesia.
Rezzy Caraka; Robert Kurniawan; Bahrul Nasution; Jamilatuzzahro Jamilatuzzahro; Prana Gio; Mohammad Basyuni; Bens Pardamean. Micro, Small, and Medium Enterprises’ Business Vulnerability Cluster in Indonesia: An Analysis Using Optimized Fuzzy Geodemographic Clustering. Sustainability 2021, 13, 7807 .
AMA StyleRezzy Caraka, Robert Kurniawan, Bahrul Nasution, Jamilatuzzahro Jamilatuzzahro, Prana Gio, Mohammad Basyuni, Bens Pardamean. Micro, Small, and Medium Enterprises’ Business Vulnerability Cluster in Indonesia: An Analysis Using Optimized Fuzzy Geodemographic Clustering. Sustainability. 2021; 13 (14):7807.
Chicago/Turabian StyleRezzy Caraka; Robert Kurniawan; Bahrul Nasution; Jamilatuzzahro Jamilatuzzahro; Prana Gio; Mohammad Basyuni; Bens Pardamean. 2021. "Micro, Small, and Medium Enterprises’ Business Vulnerability Cluster in Indonesia: An Analysis Using Optimized Fuzzy Geodemographic Clustering." Sustainability 13, no. 14: 7807.
Design: At the heart of time series forecasting, if nonlinear and nonstationary data are analyzed using traditional time series, the results will be biased. At the same time, if just using machine learning without any consideration given to input from traditional time series, not much information can be obtained from the results because the machine learning model is a black box. Purpose: In order to better study time series forecasting, we extend the combination of traditional time series and machine learning and propose a hybrid cascade neural network considering a metaheuristic optimization genetic algorithm in space–time forecasting. Finding: To further show the utility of the cascade neural network genetic algorithm, we use various scenarios for training and testing while also extending simulations by considering the activation functions SoftMax, radbas, logsig, and tribas on space–time forecasting of pollution data. During the simulation, we perform numerical metric evaluations using the root-mean-square error (RMSE), mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE) to demonstrate that our models provide high accuracy and speed up time-lapse computing.
Rezzy Caraka; Hasbi Yasin; Rung-Ching Chen; Noor Goldameir; Budi Supatmanto; Toni Toharudin; Mohammad Basyuni; Prana Gio; Bens Pardamean. Evolving Hybrid Cascade Neural Network Genetic Algorithm Space–Time Forecasting. Symmetry 2021, 13, 1158 .
AMA StyleRezzy Caraka, Hasbi Yasin, Rung-Ching Chen, Noor Goldameir, Budi Supatmanto, Toni Toharudin, Mohammad Basyuni, Prana Gio, Bens Pardamean. Evolving Hybrid Cascade Neural Network Genetic Algorithm Space–Time Forecasting. Symmetry. 2021; 13 (7):1158.
Chicago/Turabian StyleRezzy Caraka; Hasbi Yasin; Rung-Ching Chen; Noor Goldameir; Budi Supatmanto; Toni Toharudin; Mohammad Basyuni; Prana Gio; Bens Pardamean. 2021. "Evolving Hybrid Cascade Neural Network Genetic Algorithm Space–Time Forecasting." Symmetry 13, no. 7: 1158.
Background and objectives: The impacts of COVID-19 are like two sides of one coin. During 2020, there were many research papers that proved our environmental and climate conditions were improving due to lockdown or large-scale restriction regulations. In contrast, the economic conditions deteriorated due to disruption in industry business activities and most people stayed at home and worked from home, which probably reduced the noise pollution. Methods: To assess whether there were differences in noise pollution before and during COVID-19. In this paper, we use various statistical methods following odds ratios, Wilcoxon and Fisher’s tests and Bayesian Markov chain Monte Carlo (MCMC) with various comparisons of prior selection. The outcome of interest for a parameter in Bayesian inference is complete posterior distribution. Roughly, the mean of the posterior will be clear with point approximation. That being said, the median is an available choice. Findings: To make the Bayesian MCMC work, we ran the sampling from the conditional posterior distributions. It is straightforward to draw random samples from these distributions if they have regular shapes using MCMC. The case of over-standard noise per time frame, number of noise petition cases, number of industry petition cases, number of motorcycles, number of cars and density of vehicles are significant at α = 5%. In line with this, we prove that there were differences of noise pollution before and during COVID-19 in Taiwan. Meanwhile, the decreased noise pollution in Taiwan can improve quality of life.
Rezzy Caraka; Yusra Yusra; Toni Toharudin; Rung-Ching Chen; Mohammad Basyuni; Vilzati Juned; Prana Gio; Bens Pardamean. Did Noise Pollution Really Improve during COVID-19? Evidence from Taiwan. Sustainability 2021, 13, 5946 .
AMA StyleRezzy Caraka, Yusra Yusra, Toni Toharudin, Rung-Ching Chen, Mohammad Basyuni, Vilzati Juned, Prana Gio, Bens Pardamean. Did Noise Pollution Really Improve during COVID-19? Evidence from Taiwan. Sustainability. 2021; 13 (11):5946.
Chicago/Turabian StyleRezzy Caraka; Yusra Yusra; Toni Toharudin; Rung-Ching Chen; Mohammad Basyuni; Vilzati Juned; Prana Gio; Bens Pardamean. 2021. "Did Noise Pollution Really Improve during COVID-19? Evidence from Taiwan." Sustainability 13, no. 11: 5946.
Design: Health issues throughout the sustainable development goals have also been integrated into one ultimate goal, which helps to ensure a healthy lifestyle as well as enhances well-being for any and all human beings of all social level. Meanwhile, regarding the clime change, we may take urgent action to its impacts. Purpose: Nowadays, climate change makes it much more difficult to control the pattern of diseases transmitted and sometimes hard to prevent. In line with this, Centres for Disease Control (CDC) Taiwan grouped the spread of disease through its source in the first six main groups. Those are food or waterborne, airborne or droplet, vector-borne, sexually transmitted or blood-borne, contact transmission, and miscellaneous. According to this, academics, government, and the private sector should work together and collaborate to maintain the health issue. This article examines and connects the climate and communicable aspects towards Penta-Helix in Taiwan. Finding: In summary, we have been addressing the knowledge center on the number of private companies throughout the health care sector, the number of healthcare facilities, and the education institutions widely recognized as Penta Helix. In addition, we used hierarchical likelihood structural equation modeling (HSEMs). All the relationship variables among climate, communicable disease, and Penta Helix can be interpreted through the latent variables with GoF 79.24%.
Rezzy Caraka; Maengseok Noh; Rung-Ching Chen; Youngjo Lee; Prana Gio; Bens Pardamean. Connecting Climate and Communicable Disease to Penta Helix Using Hierarchical Likelihood Structural Equation Modelling. Symmetry 2021, 13, 657 .
AMA StyleRezzy Caraka, Maengseok Noh, Rung-Ching Chen, Youngjo Lee, Prana Gio, Bens Pardamean. Connecting Climate and Communicable Disease to Penta Helix Using Hierarchical Likelihood Structural Equation Modelling. Symmetry. 2021; 13 (4):657.
Chicago/Turabian StyleRezzy Caraka; Maengseok Noh; Rung-Ching Chen; Youngjo Lee; Prana Gio; Bens Pardamean. 2021. "Connecting Climate and Communicable Disease to Penta Helix Using Hierarchical Likelihood Structural Equation Modelling." Symmetry 13, no. 4: 657.
Background: In the heart data mining and machine learning, dimension reduction is needed to remove the multicollinearity. Meanwhile, it has been proven to improves the interpretation of the parameter model. In addition, dimension reduction is also can increase the time of computing in high dimensional data. Methods: In this paper, we perform high dimensional ordination towards event counts in intensive care hospital , following emergency department (ED 1), First Intensive Care Unit (ICU1), Second Intensive Care Unit (ICU2), Respiratory Care Intensive Care Unit (RICU), Surgical Intensive Care Unit (SICU), Subacute Respiratory Care Unit (RCC), Trauma and Neurosurgery Intensive Care Unit (TNCU), Neonatal Intensive Care Unit (NICU) which use the Generalized Linear Latent Variable Models (GLLVM’s). Results: During the analysis, we measure the performance and calculate the time computing of GLLVM with employing variational approximation and Laplace approximation, and compare the different distributions including Negative Binomial, Poisson, Gaussian, ZIP, and Tweedi, respectively. Conclusions: In a nutshell, GLLVM’s leads as best performance reaching the accuracy 98% comparing other methods. In line with this, we get the best model negative binomial and Variational approximation which provides the best accuracy by accruacy value of AIC, AICc, and BIC. In a nutshell, our best model is GLLVM-VA Negative Binomial with AIC 7144.07 and GLLVM-LA Negative Binomial with AIC 6955.922.
Rezzy Eko Caraka; Rung Ching Chen; Youngjo Lee; Su-Wen Huang; Shyue-Yow Chiou; Bens Pardamean. FastGLLVM: Big Data Ordination Towards Intensive Care Event Count Cases. 2021, 1 .
AMA StyleRezzy Eko Caraka, Rung Ching Chen, Youngjo Lee, Su-Wen Huang, Shyue-Yow Chiou, Bens Pardamean. FastGLLVM: Big Data Ordination Towards Intensive Care Event Count Cases. . 2021; ():1.
Chicago/Turabian StyleRezzy Eko Caraka; Rung Ching Chen; Youngjo Lee; Su-Wen Huang; Shyue-Yow Chiou; Bens Pardamean. 2021. "FastGLLVM: Big Data Ordination Towards Intensive Care Event Count Cases." , no. : 1.
One of information needed in weather forecast is air temperature. This value might change any time. Prediction of air temperature is very valuable for some communities and occasions. Therefore, high accuracy prediction is needed. Since the information about air temperature might vary over time, it is necessary to implement methods that can adapt to this situation. The use of neural network methods such as long short term memory (LSTM), nowadays, becomes popular in facing big data including unexpected fluctuation on the data. Thus, the model is used in this paper which provides long series data on air temperature. In addition, recently, Facebook announced an accurate method of forecasting, called Prophet model’s, for data which have trend, seasonality, holidays, missing data, not to mention outliers. Hence, the forecast of five-year daily air temperatures in Bandung on this paper is modeled by LSTM and Facebook Prophet. The result shows that, for minimum temperature, Prophet performs better on maximum air temperature while LSTM performs better on minimum air temperature. However, the difference on the value of RMSE is not too large significant.
Toni Toharudin; Resa Septiani Pontoh; Rezzy Eko Caraka; Solichatus Zahroh; Youngjo Lee; Rung Ching Chen. Employing long short-term memory and Facebook prophet model in air temperature forecasting. Communications in Statistics - Simulation and Computation 2021, 1 -24.
AMA StyleToni Toharudin, Resa Septiani Pontoh, Rezzy Eko Caraka, Solichatus Zahroh, Youngjo Lee, Rung Ching Chen. Employing long short-term memory and Facebook prophet model in air temperature forecasting. Communications in Statistics - Simulation and Computation. 2021; ():1-24.
Chicago/Turabian StyleToni Toharudin; Resa Septiani Pontoh; Rezzy Eko Caraka; Solichatus Zahroh; Youngjo Lee; Rung Ching Chen. 2021. "Employing long short-term memory and Facebook prophet model in air temperature forecasting." Communications in Statistics - Simulation and Computation , no. : 1-24.
The diagnosis of a hazard can be classified into three key domains, particularly regarding the natural hazards, non-natural hazards and social hazards. The disasters which have actually happened in West Papua require considerable attention and consideration of the Indonesian Government, despite since they have handled as much as they can to provide solutions and make people feel secure and pleasant. In this paper, using location-based social vulnerability calculation in West Papua involves the components of Information, Technology, and Communication, Food Access, Natural Disaster, Social Protection Statement, Access to Financial Services, Description of the source of household income, Number of event floods, number of earthquake disasters, COVID-19 death cases, and Number of incidents of protest which are obtained from the National Socio-Economic Survey (SUSENAS) 2017 official statistics. After employ clustering of variables around latent variables with connectivity value of 3.9400794, Dunn 0.9373, and Silhouette 0.6333. Each factor provide a sign indicating a positive or negative effect on social vulnerability and finally a location cluster will be formed based on the index obtained.
Rezzy Eko Caraka; Youngjo Lee; Rung Ching Chen; Toni Toharudin; Prana Ugiana Gio; Robert Kurniawan; Bens Pardamean. Cluster Around Latent Variable for Vulnerability Towards Natural Hazards, Non-Natural Hazards, Social Hazards in West Papua. IEEE Access 2020, 9, 1972 -1986.
AMA StyleRezzy Eko Caraka, Youngjo Lee, Rung Ching Chen, Toni Toharudin, Prana Ugiana Gio, Robert Kurniawan, Bens Pardamean. Cluster Around Latent Variable for Vulnerability Towards Natural Hazards, Non-Natural Hazards, Social Hazards in West Papua. IEEE Access. 2020; 9 (99):1972-1986.
Chicago/Turabian StyleRezzy Eko Caraka; Youngjo Lee; Rung Ching Chen; Toni Toharudin; Prana Ugiana Gio; Robert Kurniawan; Bens Pardamean. 2020. "Cluster Around Latent Variable for Vulnerability Towards Natural Hazards, Non-Natural Hazards, Social Hazards in West Papua." IEEE Access 9, no. 99: 1972-1986.
The decreasing area of mangroves is an ongoing problem since, between 1980 and 2005, one-third of the world’s mangroves were lost. Rehabilitation and restoration strategies are required to address this situation. However, mangroves do not always respond well to these strategies and have high mortality due to several growth limiting parameters. This study developed a land suitability map for new mangrove plantations in different Southeast Asian countries for both current and future climates at a 250-m resolution. Hydrodynamic, geomorphological, climatic, and socio-economic parameters and three representative concentration pathway (RCP) scenarios (RCP 2.6, 4.5, and 8.5) for 2050 and 2070 with two global climate model datasets (the Centre National de Recherches Météorologiques Climate model version 5 [CNRM-CM5.1] and the Model for Interdisciplinary Research on Climate [MIROC5]) were used to predict suitable areas for mangrove planting. An analytical hierarchy process (AHP) was used to determine the level of importance for each parameter. To test the accuracy of the results, the mangrove land suitability analysis were further compared using different weights in every parameter. The sensitivity test using the Wilcoxon test was also carried out to test which variables had changed with the first weight and the AHP weight. The land suitability products from this study were compared with those from previous studies. The differences in land suitability for each country in Southeast Asia in 2050 and 2070 to analyze the differences in each RCP scenario and their effects on the mangrove land suitability were also assessed. Currently, there is 398,000 ha of potentially suitable land for mangrove planting in Southeast Asia, and this study shows that it will increase between now and 2070. Indonesia account for 67.34% of the total land area in the “very suitable” and “suitable” class categories. The RCP 8.5 scenario in 2070, with both the MIROC5 and CNRM-CM5.1 models, resulted in the largest area of a “very suitable” class category for mangrove planting. This study provides information for the migration of mangrove forests to the land, alleviating many drawbacks, especially for ecosystems.
Luri Syahid; Anjar Sakti; Riantini Virtriana; Ketut Wikantika; Wiwin Windupranata; Satoshi Tsuyuki; Rezzy Caraka; Rudhi Pribadi. Determining Optimal Location for Mangrove Planting Using Remote Sensing and Climate Model Projection in Southeast Asia. Remote Sensing 2020, 12, 3734 .
AMA StyleLuri Syahid, Anjar Sakti, Riantini Virtriana, Ketut Wikantika, Wiwin Windupranata, Satoshi Tsuyuki, Rezzy Caraka, Rudhi Pribadi. Determining Optimal Location for Mangrove Planting Using Remote Sensing and Climate Model Projection in Southeast Asia. Remote Sensing. 2020; 12 (22):3734.
Chicago/Turabian StyleLuri Syahid; Anjar Sakti; Riantini Virtriana; Ketut Wikantika; Wiwin Windupranata; Satoshi Tsuyuki; Rezzy Caraka; Rudhi Pribadi. 2020. "Determining Optimal Location for Mangrove Planting Using Remote Sensing and Climate Model Projection in Southeast Asia." Remote Sensing 12, no. 22: 3734.
The H-likelihood method proposed by Lee and Nelder (1996) is extensively used in a wide range of data. In terms of direction, repetitive measured data within classification can be examined employing hierarchical generalized linear models (HGLMs). Whether we are concerned in multiple endpoints which are correlated, instead Multivariate Double Hierarchical Generalized Linear Models (DHGLM) can be taken into consideration. This article addresses the implementation of this principle to vector selection and support machines. Based on the analysis with the fish morphology class Sardinella lemuru ( Bali sardinella ) and setting the best epsilon 0.7 cost 4 parameter reaching best performance: 0.2327401. Predictive value of fish sex was calculated 0.997319 and Region under the curve: 0.8967. At the same time, we extend the large-scale case studies for stress testing of the SVM method by using three datasets from UCI machine learning repository including the bank marketing dataset, the car evaluation database and human activity recognition using smartphones dataset. In a nutshell by employing SVM-DHGLM increased the accuracy, precision, recall, for feature selection and classification. Long story short, the $H$ -likelihood provides an excellent and usable structure for statistical inference of the unobservable general deterministic model, while preserving the advantages of the original probability structure for fixed parameters. We presume that more new groups of models will be created and that the $H$ -likelihood will be commonly used for their inferences and the application in big data and machine learning.
Rezzy Eko Caraka; Youngjo Lee; Rung Ching Chen; Toni Toharudin. Using Hierarchical Likelihood towards Support Vector Machine: Theory and Its Application. IEEE Access 2020, 8, 1 -1.
AMA StyleRezzy Eko Caraka, Youngjo Lee, Rung Ching Chen, Toni Toharudin. Using Hierarchical Likelihood towards Support Vector Machine: Theory and Its Application. IEEE Access. 2020; 8 ():1-1.
Chicago/Turabian StyleRezzy Eko Caraka; Youngjo Lee; Rung Ching Chen; Toni Toharudin. 2020. "Using Hierarchical Likelihood towards Support Vector Machine: Theory and Its Application." IEEE Access 8, no. : 1-1.
Feature selection becomes prominent, especially in the data sets with many variables and features. It will eliminate unimportant variables and improve the accuracy as well as the performance of classification. Random Forest has emerged as a quite useful algorithm that can handle the feature selection issue even with a higher number of variables. In this paper, we use three popular datasets with a higher number of variables (Bank Marketing, Car Evaluation Database, Human Activity Recognition Using Smartphones) to conduct the experiment. There are four main reasons why feature selection is essential. First, to simplify the model by reducing the number of parameters, next to decrease the training time, to reduce overfilling by enhancing generalization, and to avoid the curse of dimensionality. Besides, we evaluate and compare each accuracy and performance of the classification model, such as Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA). The highest accuracy of the model is the best classifier. Practically, this paper adopts Random Forest to select the important feature in classification. Our experiments clearly show the comparative study of the RF algorithm from different perspectives. Furthermore, we compare the result of the dataset with and without essential features selection by RF methods varImp(), Boruta, and Recursive Feature Elimination (RFE) to get the best percentage accuracy and kappa. Experimental results demonstrate that Random Forest achieves a better performance in all experiment groups.
Rung-Ching Chen; Christine Dewi; Su-Wen Huang; Rezzy Eko Caraka. Selecting critical features for data classification based on machine learning methods. Journal of Big Data 2020, 7, 1 -26.
AMA StyleRung-Ching Chen, Christine Dewi, Su-Wen Huang, Rezzy Eko Caraka. Selecting critical features for data classification based on machine learning methods. Journal of Big Data. 2020; 7 (1):1-26.
Chicago/Turabian StyleRung-Ching Chen; Christine Dewi; Su-Wen Huang; Rezzy Eko Caraka. 2020. "Selecting critical features for data classification based on machine learning methods." Journal of Big Data 7, no. 1: 1-26.
A retrospective study of the abdominal aortic aneurysm (AAA) with EVAR treated patients. The third-party collected the data from twelve vascular centres in Indonesia during 2012-2017. Patient demographics and computed tomography data were evaluated with Osirix MD Software. During five years, we had 148 EVAR cases done using Endurant stent graft (Medtronic). In this paper, we perform Bayesian modelling and selection of feature selection by Boruta. Before performing the models, we will determine the selection of dependent variables start with the Age, Class, and Sex. It will get what is important to be dependent and independent. The difference between Bayesian and the classical method is the introduction of prior information in the form of probability distributions. In addition, to determine the parameters using the Bayesian method obtained from the probability statement. Parameter estimation in Bayesian is no longer a point estimate but, on the contrary, is a statistical distribution. In other words, Bayesian states that a parameter is a variable that has a distribution. Bayesian has become a popular method in modern statistical analysis. Bayesian is applied to a broad spectrum in the scientific and research fields. Bayesian data analysis involves learning from data that uses probability models for many observations and some information to be studied. In other words, analysing statistical models are by combining prior knowledge about the model or parameters of the model. In a nutshell, the simulation results obtained modelling with Bayesian-ZIP-MCMC R2 87.52 and Bayesian-Boruta R2 88.28%.
Rezzy Eko Caraka; Nyityasmono Tri Nugroho; Shao-Kuo Tai; Rung-Ching Chen; Toni Toharudin; Bens Pardamean. Feature importance of the aortic anatomy on endovascular aneurysm repair (EVAR) using Boruta and Bayesian MCMC. Communications in Mathematical Biology and Neuroscience 2020, 2020, 1 .
AMA StyleRezzy Eko Caraka, Nyityasmono Tri Nugroho, Shao-Kuo Tai, Rung-Ching Chen, Toni Toharudin, Bens Pardamean. Feature importance of the aortic anatomy on endovascular aneurysm repair (EVAR) using Boruta and Bayesian MCMC. Communications in Mathematical Biology and Neuroscience. 2020; 2020 ():1.
Chicago/Turabian StyleRezzy Eko Caraka; Nyityasmono Tri Nugroho; Shao-Kuo Tai; Rung-Ching Chen; Toni Toharudin; Bens Pardamean. 2020. "Feature importance of the aortic anatomy on endovascular aneurysm repair (EVAR) using Boruta and Bayesian MCMC." Communications in Mathematical Biology and Neuroscience 2020, no. : 1.
As the sharing economy has emerged, the way customer perceives the service is shifting toward a combination of offline and online. The need for the service provider to understand its nature as well as the pertinent aspects regarding its characteristics is crucial. Previous research validated the influence of perceived online and offline service quality toward customer satisfaction and loyalty. However, with the distinctive dimensions of OFA service quality, its effects on customer satisfaction and the role of social innovativeness in satisfaction and loyalty linkage remain unexplored. Hence, this study attempts to investigate these relationships using the data obtained from customers of any OFA in Malaysia. Purposive sampling was employed and 227 collected responses were analyzed using variance-based partial least square path modeling. The results confirm the direct effect of online and offline service quality on customer loyalty and full mediation role of customer satisfaction. Besides, social innovativeness is found negatively moderates customer satisfaction and loyalty relationship. Implications and contributions of the study are also discussed.
Yusra Yusra; Caraka Rezzy Eko; Arawati Agus; Mohd Ariffin Ahmad Azmi; Gio Prana Ugiana; Chen Rung Ching; Youngjo Lee. An investigation of online food aggregator (OFA) service: Do online and offline service quality distinct? Serbian Journal of Management 2020, 15, 277 -294.
AMA StyleYusra Yusra, Caraka Rezzy Eko, Arawati Agus, Mohd Ariffin Ahmad Azmi, Gio Prana Ugiana, Chen Rung Ching, Youngjo Lee. An investigation of online food aggregator (OFA) service: Do online and offline service quality distinct? Serbian Journal of Management. 2020; 15 (2):277-294.
Chicago/Turabian StyleYusra Yusra; Caraka Rezzy Eko; Arawati Agus; Mohd Ariffin Ahmad Azmi; Gio Prana Ugiana; Chen Rung Ching; Youngjo Lee. 2020. "An investigation of online food aggregator (OFA) service: Do online and offline service quality distinct?" Serbian Journal of Management 15, no. 2: 277-294.
Rainfall is significant in influencing human life. Therefore, it is necessary to predict or forecast rainfall in decision making. Forecasting rainfall can be calculated by the average rainfall of an area and by using the time-series method. Moreover, the government has a climatology station to measure rainfall at specific points or locations in various regions. In Indonesia, they are considered to have potential and represent the surrounding area. However, rainfall outside the climatology station area is not known for sure, while for specific purposes, information about rain is needed at other points. This research work focuses on the application of machine learning methods to the problem of computing prediction on time series as input variables. More specifically, we employ moving average (MA) and long short-term memory (LSTM) method to predict the rainfall in Winangun, North Sulawesi, Indonesia. LSTM is a neural network development that can be used for time-series data modelling. Based on the simulation, the combination of these methods, in-sample data reaches the R 2 95.11%, and out-sample data reach R 2 90.46% respectively.
Rezzy Eko Caraka; Rung Ching Chen; Budi Darmawan Supatmanto; Arnita; Muhammad Tahmid; Toni Toharudin. Employing Moving Average Long Short Term Memory for Predicting Rainfall. 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) 2019, 1 -5.
AMA StyleRezzy Eko Caraka, Rung Ching Chen, Budi Darmawan Supatmanto, Arnita, Muhammad Tahmid, Toni Toharudin. Employing Moving Average Long Short Term Memory for Predicting Rainfall. 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI). 2019; ():1-5.
Chicago/Turabian StyleRezzy Eko Caraka; Rung Ching Chen; Budi Darmawan Supatmanto; Arnita; Muhammad Tahmid; Toni Toharudin. 2019. "Employing Moving Average Long Short Term Memory for Predicting Rainfall." 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) , no. : 1-5.
Rezzy Eko Caraka; Rung Ching Chen; Toni Toharudin; Bens Pardamean; Hasbi Yasin; Shih Hung Wu. Prediction of Status Particulate Matter 2.5 Using State Markov Chain Stochastic Process and HYBRID VAR-NN-PSO. IEEE Access 2019, 7, 161654 -161665.
AMA StyleRezzy Eko Caraka, Rung Ching Chen, Toni Toharudin, Bens Pardamean, Hasbi Yasin, Shih Hung Wu. Prediction of Status Particulate Matter 2.5 Using State Markov Chain Stochastic Process and HYBRID VAR-NN-PSO. IEEE Access. 2019; 7 ():161654-161665.
Chicago/Turabian StyleRezzy Eko Caraka; Rung Ching Chen; Toni Toharudin; Bens Pardamean; Hasbi Yasin; Shih Hung Wu. 2019. "Prediction of Status Particulate Matter 2.5 Using State Markov Chain Stochastic Process and HYBRID VAR-NN-PSO." IEEE Access 7, no. : 161654-161665.
Geographically, Indonesia is a meeting point of three continental plates. Scilicet, the Eurasian Plate, the Indo-Australian Plate, and the Pacific Plate. Therefore, Indonesia is part of the infamous volcanic zone called the ”Ring of Fire” and one of the areas prone to natural disasters such as volcanic eruptions, earthquakes, tsunamis, floods, and landslides. This study aims to capture the spatial pattern and identify the causes of social vulnerability in the districts/cities in Indonesia using the biclustering method. The data is extracted from the Indonesian National Socio-Economic Survey (SUSENAS) by BPS-Statistics in 2014. The biclustering result indicates that each district/city has its own social vulnerability characteristics and shows that the vulnerable aspects of each district/city are different. The adjacent observations tend to have social vulenrability characteristics. The results of this study can be used as a reference for national disaster mitigation policy in Indonesia.
Puspita Anggraini Kaban; Robert Kurniawan; Rezzy Eko Caraka; Bens Pardamean; Budi Yuniarto; Sukim. Biclustering Method to Capture the Spatial Pattern and to Identify the Causes of Social Vulnerability in Indonesia: A New Recommendation for Disaster Mitigation Policy. Procedia Computer Science 2019, 157, 31 -37.
AMA StylePuspita Anggraini Kaban, Robert Kurniawan, Rezzy Eko Caraka, Bens Pardamean, Budi Yuniarto, Sukim. Biclustering Method to Capture the Spatial Pattern and to Identify the Causes of Social Vulnerability in Indonesia: A New Recommendation for Disaster Mitigation Policy. Procedia Computer Science. 2019; 157 ():31-37.
Chicago/Turabian StylePuspita Anggraini Kaban; Robert Kurniawan; Rezzy Eko Caraka; Bens Pardamean; Budi Yuniarto; Sukim. 2019. "Biclustering Method to Capture the Spatial Pattern and to Identify the Causes of Social Vulnerability in Indonesia: A New Recommendation for Disaster Mitigation Policy." Procedia Computer Science 157, no. : 31-37.
Tulisan ini memaparkan path analysis dengan STATCAL, disertai perbandingan hasil dengan SPSS, LISREL dan AMOS.
Prana Ugiana Gio; Rezzy Eko Caraka. PATH ANALYSIS DENGAN STATCAL. 2019, 1 .
AMA StylePrana Ugiana Gio, Rezzy Eko Caraka. PATH ANALYSIS DENGAN STATCAL. . 2019; ():1.
Chicago/Turabian StylePrana Ugiana Gio; Rezzy Eko Caraka. 2019. "PATH ANALYSIS DENGAN STATCAL." , no. : 1.
Rezzy Eko Caraka; Sakhinah Abu Bakar; Muhammad Tahmid; Hasbi Yasin; Isma Dwi Kurniawan. Neurocomputing fundamental climate analysis. TELKOMNIKA (Telecommunication Computing Electronics and Control) 2019, 17, 1818 .
AMA StyleRezzy Eko Caraka, Sakhinah Abu Bakar, Muhammad Tahmid, Hasbi Yasin, Isma Dwi Kurniawan. Neurocomputing fundamental climate analysis. TELKOMNIKA (Telecommunication Computing Electronics and Control). 2019; 17 (4):1818.
Chicago/Turabian StyleRezzy Eko Caraka; Sakhinah Abu Bakar; Muhammad Tahmid; Hasbi Yasin; Isma Dwi Kurniawan. 2019. "Neurocomputing fundamental climate analysis." TELKOMNIKA (Telecommunication Computing Electronics and Control) 17, no. 4: 1818.
Predicting stock price has been a challenging project for many researchers, investors, and analysts. Most of them are interested in knowing the stock price trend in the future. To get a precise and winning model is the wish of them. Recently, Neural Network has been a prevalent means for stock prediction. However, there are many ways and different predicting models such as Convolutional Neural Networks (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). In this paper, we propose a novel idea that average previous five days stock market information (open, high, low, volume, close) as a new value then use this value to predict, and use the predicted value as the average of the stock price information for the next five days. Moreover, we utilize Technical Analysis Indicators to consider whether to buy stocks or continue to hold stocks or sell stocks. We use Foxconn company data collected from Taiwan Stock Exchange for testing with the Neural Network Long Short-Term Memory (LSTM).
Chun Yuan Lai; Rung-Ching Chen; Rezzy Eko Caraka. Prediction Stock Price Based on Different Index Factors Using LSTM. 2019 International Conference on Machine Learning and Cybernetics (ICMLC) 2019, 1 -6.
AMA StyleChun Yuan Lai, Rung-Ching Chen, Rezzy Eko Caraka. Prediction Stock Price Based on Different Index Factors Using LSTM. 2019 International Conference on Machine Learning and Cybernetics (ICMLC). 2019; ():1-6.
Chicago/Turabian StyleChun Yuan Lai; Rung-Ching Chen; Rezzy Eko Caraka. 2019. "Prediction Stock Price Based on Different Index Factors Using LSTM." 2019 International Conference on Machine Learning and Cybernetics (ICMLC) , no. : 1-6.
Rainfall variation in the tropics is caused by several factors, such as: geographic, topographical, and orographic. Therefore, the importance of rainfall analysis is needed to know the factors also local characteristics that affect fluctuations in daily rainfall / monthly in each particular area. Rainfall is one element of weather that has a vital role in various sectors in Indonesia. In the agriculture sector, rainfall prediction is used to know the schedule prediction of cropping pattern to optimize food crop production result. In the land, sea and air transport sector, the weather factor that rainfall has a role in the level of safety. In this paper, we used daily rainfall data in Manado, North Sulawesi province in January 2017-December 2017. In short, we combined of SARIMA, and Localized Multi Kernel Support Vector Regression (LMKL SVR) with linear kernel and polynomial kernel reached accuracy model R2 98.76%. On the one hand, after obtained rainfall prediction, we compared with actual rainfall data in January 2018 -February 2018 (59 data). Mainly, Rainfall is difficult to predict even though the model obtained has good accuracy. Still, after validation data forecast and actual data, there is a very far different with RMSE amount 24.43 because the data climate is very dynamic also there are variables that need to be analyzed in building prediction model of rainfall
Rezzy Eko Caraka; Sakhinah Abu Bakar; Muhammad Tahmid. Rainfall forecasting multi kernel support vector regression seasonal autoregressive integrated moving average (MKSVR-SARIMA). THE 2018 UKM FST POSTGRADUATE COLLOQUIUM: Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2018 Postgraduate Colloquium 2019, 2111, 020014 .
AMA StyleRezzy Eko Caraka, Sakhinah Abu Bakar, Muhammad Tahmid. Rainfall forecasting multi kernel support vector regression seasonal autoregressive integrated moving average (MKSVR-SARIMA). THE 2018 UKM FST POSTGRADUATE COLLOQUIUM: Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2018 Postgraduate Colloquium. 2019; 2111 (1):020014.
Chicago/Turabian StyleRezzy Eko Caraka; Sakhinah Abu Bakar; Muhammad Tahmid. 2019. "Rainfall forecasting multi kernel support vector regression seasonal autoregressive integrated moving average (MKSVR-SARIMA)." THE 2018 UKM FST POSTGRADUATE COLLOQUIUM: Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2018 Postgraduate Colloquium 2111, no. 1: 020014.