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Covariance Based approach SEM with Bollen-Stine Estimation (Case Study Analysis of The Effect of Teacher and Principal Competence on Achievement of National Standards) Kasmuri Kasmuri; I Made Tirta; Yuliani Setia Dewi
Jurnal ILMU DASAR Vol 16 No 2 (2015)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (378.054 KB) | DOI: 10.19184/jid.v16i2.899

Abstract

Applications of covariance Based SEM (CB-SEM) generally use the maximum likelihood, based upon the assumption on the normal distribution of data. One alternative that could be applied if the data were not normally distributed is estimation using  Bollen-Stine bootstrap approach. In this study, the method is applied to reveal the influence of teacher competence, the principal competence, to the value of achievement of national education standards in secondary schools in Banyuwangi.The objective of this paper was to determine and analyze the relationship and to know the  the most dominant indicators of  measure latent variables between the  the principal, teachers competences on national standards of educational attainment in secondary schools in Banyuwangi. The results  indicate that all of the indicator of variables are  valid and reliable to measure corresponding latent variables. Each latent variable has the most dominan indicator. For the principal competence  latent variables the most dominant  indicator is the entrepreneurial competence, for teachers competency the most dominant is personal competence, whereas for  national education standards, the most dominant  standard of facilities. Principal competence  has indirect influence on national education standard achievement, but directly affect the competence of teachers.  Teacher competence directly influence national education standards.Keywords: Power Competence Teachers, Competence Principal, National Education Standards,  covariance Based SEM, Bollen-Stine Bootstrap Estimates
Structural Equation Modeling of the Factors Affecting the Nutritional Status of Children Under Five in Banyuwangi Region using Recursive (one-way) GSCA I Made Tirta; Nawal Ika Susanti; Yuliani Setia Dewi
Jurnal ILMU DASAR Vol 16 No 1 (2015)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1517.157 KB) | DOI: 10.19184/jid.v16i1.534

Abstract

Structural Equation Modeling is one among popular multivariate analysis, especially applied in pschology and marketing. There are two main types of Structural Equation Modeling namely covariance-based or CB-SEM and variance-based or Partial Least Square (PLS)- SEM. Both types have advantages and disadvantage. To overcome its limitation, Generalized Structured Component Analysis (GSCA) was then proposed as an extension of PLS-SEM. In estimating the parameters, GSCA uses Alternating Least Squares (ALS) and in estimating the standard error of the parameter estimates it uses the bootstrap method. In this paper, GSCA is applied to study the causality model of Infant nutritional status, in relation with socio-economic status and infantcare status in Banyuwangi Region. The results show that both socio-economic and infantcare status have significant positive influence on infant nutritional status.Keywords:  Alternating least square, generalized structural component analysis,  nutritional status of infants,  structural equation modelling
The Efficiency of First (GEE1) and Second (GEE2) Order “Generalized Estimating Equations” for Longitudinal Data Rizka Dwi Hidayati; I Made Tirta; Yuliani Setia Dewi
Jurnal ILMU DASAR Vol 15 No 1 (2014)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (799.799 KB) | DOI: 10.19184/jid.v15i1.553

Abstract

The approach of GEE focuses on a linear model for the mean of the observations in the cluster without full specification  the distribution of full-on observation. GEE is a marginal model where is not based on the full likelihood of the response, but only based on the relationship between the mean (first moment) and variance (second moment) as well as the correlation matrix. The advantage of  GEE is that the mean of  parameter are estimated consistently regardless whether  the correlation structure is specified correctly or not, as long as the mean has the correct specifications. However, the efficiency may be reduced when the working correlation structure is wrong. GEE was designed to focus on the marginal mean and correlation structure as nuisiance treat. Implementation of GEE is usually limited to the number of working correlation structure (eg AR-1, exchangeable, independent, m-dependent and unstructured). To increase the efficiency of the GEE, has introduced a variation called the Generalized Estimating Equations order 2 (GEE2). GEE2 has been introduced to overcome the problem that considers correlation GEE as nuisiance, by applying the second equation to estimate covariance parameters and  solved simultaneously with the first equation. This study used simulation data which are designed based on the the AR-1 and Exchangeable correlation structure, then estimation are done  using theAR1 and exchangeable. For GEE2,  estimation done by adding model for correlation link. The result is a link affects the efficiency of the model correlation is shown with standard error values ​​generated by GEE2 method is smaller than the GEE method.
Interface web development for analysis of item response theory with mixed model approach and application on bank soal MGMP T C P Utama; I Made Tirta; M Fatekurrahman
International Conference on Mathematics and Science Education of Universitas Pendidikan Indonesia Vol 3 (2018): Promoting 21st Century Skills Through Mathematics and Science Education
Publisher : Pascasarjana Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1197.755 KB)

Abstract

The development of the world of education today is progressing very rapidly, the era of technology is increasingly modern, so that teachers must have adequate competence in every process of teaching and learning activities. One type of measurement often done in education are measurement of the students’ performance both for cognitive and effective aspects. These measures are extremely important therefore must use a good measuring tool and the results also easy to interpret. The measurement of students performance mostly use tests. Items response theory have evolved from traditional one to modern theories to apply more realistic models which are known as item response theory. However the use of modern test theory much rely on availability of the computer software. In this paper we report the development of a web-GUI interface that can be used to analyze polytomous responses, using Hierarchical Generalized Linear Models which will also contains theories and interpretations of the results. This web-GUI interface is expected to help teachers to understand and to do the analysis of polytomous responses more easily.
On the Development of Web-GUI Interface for Analyzing Polytomous Responses Tika Clarinta Putri Utama; Indriasih Yanuwijaya; I Made Tirta
Pancaran Pendidikan Vol 7, No 2 (2018)
Publisher : The Faculty of Teacher Training and Education The University of Jember Jember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (314.069 KB) | DOI: 10.25037/pancaran.v7i2.169

Abstract

One type of measurement often done in education are measurement of the students’ performance both for cognitive and affective aspects. These measures are extremely important therefore must use a good measuring tool and the results also easy to interpret. The measurement of students performance mostly use tests. Measurement test have evolved from traditional one to modern theories to apply more realistic models which are known as item response theory. However the use of modern test theory much rely on availability of the computer software. In this paper we report the development of a web-GUI interface that can be used to analyze polytomous responses, especially using partial credit and graded response models which will also contains theories and interpretations of the results. This web-GUI interface is expected to help teachers to understand and to do the analysis of polytomous responses more easily.
Modeling Student Mathematics Achievement in Senior High School Based on Selection Results Using Gee 2 Method with Natural Spline Erfan Syahuri; I Made Tirta; Budi Lestari; Dian Anggraeni
Pancaran Pendidikan Vol 6, No 3 (2017)
Publisher : The Faculty of Teacher Training and Education The University of Jember Jember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (854.303 KB) | DOI: 10.25037/pancaran.v6i3.54

Abstract

Every school has a vision and mission to become the superior institution so that it can compete and gain trust from the public. To achieve that, one of the efforts of the school is doing the selection of new students at the beginning of each academic year. In Lumajang region, admission of new students (PPDB) are selected using several components, such as national test scores (NUN) and Mapping/Placement test (MP). This research explores the best model of the relationship between selection components (and other conditions of students at the time of selection) and academic achievement during high school (in the form semester mathematics grade) starting from semester 1 till 5 at 3 schools in Lumajang regions. We apply Generalized Estimating Equation order 2 (GEE2) with Natural Spline. The results show that (i) the three schools, have different model and PGRI has the highest mean, followed by SMA1and SMA3, as shown by significant negative estimates of the coefficients. (i) Altough it is relatively small, distance from school has negatif contribution to the mathematics grade as shown by negatif (but significant) coefficient; (ii) The Junior High School NUN has nonlinear (and nonparametric) contribution as shown by the graphical representation and coefficient of natural spline. (iii) Score of Placement Test contribute positively and significantly to the the smester mathematics grade.
KLASIFIKASI DATA DIAGNOSIS COVID-19 MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) DAN GENERALIZED LINEAR MODEL (GLM) Yeni Rismawati; I Made Tirta; Yuliani Setia Dewi
UNEJ e-Proceeding 2022: E-Prosiding Seminar Nasional Matematika, Geometri, Statistika, dan Komputasi (SeNa-MaGeStiK)
Publisher : UPT Penerbitan Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Covid-19 is still a global concern. From the first time, this virus was detected, on December 31, 2019. As of March 20, 2022, there were 460 million positive cases of Covid-19, with 6.06 million deaths worldwide. The high number of Covid-19 cases is due to the rapid spread of this virus. One way to prevent the spread of this virus is by early detection of the disease and mapping the influence factors .The classification method with the support vector machine (SVM) method in machine learning can predict individuals diagnosed as positive for Covid-19 and who do not use the factors considered influential. Traditionally this can also be done with a generalized linear model (GLM). This study aims to compare two methods (SVM and GLM) in predicting individuals diagnosed as positive for Covid-19. In addition, this study also conducted an ensemble between SVM and GLM to determine whether the ensemble performed could produce better accuracy than the single classifier (SVM and GLM). The results showed that the accuracy with SVM and GLM was relatively high. However, SVM is slightly superior with 98.91% accuracy, and GLM with 95.64% accuracy. Meanwhile, the ensemble of both models achieved 98.91% accuracy, as high as SVM. Keywords: Covid-19, Klasifikasi, Machine Learning SVM, GLM
PENERAPAN MODEL LEAST SQUARE SUPPORT VECTOR MACHINE (LSSVM) UNTUK PERAMALAN KASUS COVID-19 DI INDONESIA Lutfi Ardining Tyas; I Made Tirta; Yuliani Setia Dewi
Jurnal Gaussian Vol 12, No 2 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.2.304-313

Abstract

Forecasting is about predicting the future based on historical data and any information that might affects the forecasts. This article applies the LSSVM model to forecast Covid-19 cases in Indonesia. The purpose of this study is to find out how the LSSVM model applied and the model performances for forecasting Covid-19 cases in Indonesia, using time series data and the factors that influence it, as input features. The factor data used in this study are mobility data and daily fully vaccinated data. The research has three main objectives; first, calculate the correlation between confirmed cases data and past data (lag) of mobility and vaccination. Second, is the selection of input features based on the highest correlation coefficient value of each variable. Third, do LSSVM modeling and Covid-19 case forecasting with the optimal model. RBF kernel and grid-search algorithm with 10-fold cross-validation are used to tune model parameters. The results show that the LSSVM model provides good performance for Covid-19 forecasting and the optimal LSSVM model for forecasting Covid-19 cases in Indonesia is using time lag 14 for the mobility factor and time lag 24 for the vaccination factor.
Analysis of the Death Risk of Covid-19 Patients Using Extended Cox model Romarizka, Cyndy; Fatekurohman, Mohamat; Tirta, I Made
Jurnal ILMU DASAR Vol 24 No 1 (2023)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/jid.v24i1.33074

Abstract

Globally, in 2021, there were 170,051,718 COVID-19 cases and 3,540,437 patients who died. The high mortality rate of patients infected with COVID-19 gives an idea to research the analysis of the factors that influence the death of Covid-19 patients. The data used in this study is data on Covid-19 patients obtained from the Mexican Government, with response variables namely time and status and predictor variables, namely patient laboratory results in the form of a history of illness that has been suffered by Covid-19 patients so that they adopt the extended model to evaluate the data. The data in this study are heterogeneous and large in number so that data clustering is carried out into 3 clusters, namely low emergency clusters, medium emergency clusters and high emergency clusters using K-means clustering. Because the study could not find the factors that influence the death of Covid-19 patients, two clusters were chosen, namely the medium emergency cluster and the high emergency cluster. So that the factors that influence the death of Covid-19 patients in the medium emergency cluster are sorted by the highest hazard ratio, namely pneumonia, old age, renal chronic, diabetes, Chronic Obstructive Pulmonary Disease (COPD), immune system, hypertension, cardiovascular, obesity, gender, and asthma. In the high emergency cluster, sorted by the highest hazard ratio is the immune system, renal chronic, cardiovascular, COPD, tobacco, hypertension, obesity, gender, and pneumonia.
PERAMALAN PERTUMBUHAN PENDUDUK KABUPATEN SITUBONDO DENGAN MODEL ARIMA, DERET ARITMATIK, DERET GEOMETRI DAN DERET EKSPONENSIAL “THE FORECASTING GROWTH OF THE POPULATION IN SITUBONDO BY USING ARIMA, ARITMATICS, GEOMETRICS AND EXPONENTIAL” As’ad, A; Tirta, I Made; Dewi, Yuliani Setia
Kadikma Vol 4 No 1 (2013): April 2013
Publisher : Department of Mathematics Education , University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/kdma.v4i1.1123

Abstract

Abstract. ARIMA models as the population forecasting in Situbondo is a model of ARIMA(3, 3, 3) and mathematically, it is stated as; =2,445–1,6632–0,148+0,9732–1,0746+ 0,4676+– 1,0635. Forecasting the population in Situbondo is 667646 people in 2012 and in 2013 is 677852 people. Some other approaches in determining the population is the Arithmetic growth formula, the result of forecasting in 2012 is 657540 people and in 2013 is 661626 people, Based on Geometric growth formula, the result of forecasting in 2012 is 19696459 people and in 2013 is 35211214 people and Based on Exponential growth formula the result of forecasting in 2012 is 657611 people and in 2013 is 661799 people. If we compare the data of the forecasted result of ARIMA model with the Aritmatics growth formula and Exponential growth formula, show that the data of the population with the last ten actual data is relatively similiar.The closed last ten actual data forecasting of population is the aritmatics growth formula, whereas the data of the population result for next two year based on the Geometric growth formula got the forecasted result which is different from the forecasted result of ARIMA model, Aritmatics growth formula and Exponential growth formula. Key Words:forecasting, arima models, arithmetic, geometric, exponential