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All Journal Jurnal Ilmu Dasar Jurnal Gaussian KADIKMA BERKALA SAINSTEK Prosiding Seminar Matematika dan Pendidikan Matematik UNEJ e-Proceeding International Conference on Mathematics and Science Education of Universitas Pendidikan Indonesia Majalah Ilmiah Matematika dan Statistika (MIMS) JURNAL BIOSHELL Pancaran Pendidikan

I Made Tirta

Program Studi Magister Matematika, FMIPA, Universitas Jember

Author-ID : 403778

Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Control & Systems Engineering Education Immunology & microbiology Languange, Linguistic, Communication & Media Mathematics Physics Other

Published : 32 Documents Claim Missing Document

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Articles

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Application of Black Scholes Method in Determining Agricultural Insurance Premium Based On Climate Index Using Historical Burn Analysis Method Aminatus Sholiha; Mohamat Fatekurohman; I Made Tirta
BERKALA SAINSTEK Vol 9 No 3 (2021)
Publisher : Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/bst.v9i3.22920

Climate index insurance is an insurance that provides reimbursement for losses due to decreased harvest rates or crop failures caused by weather. The use of Historical Burn Analysis (HBA) method in determining climate index based on rainfall resulted in a concept of the agricultural insurance payment in Pasuruan Regency. The application of The Black Scholes method in determining agricultural insurance premiums is obtained when rainfall more than 17 mm the premium is Rp 221,234. If the rainfall are 13 mm ≥ RR < 17 mm, the nominal premium paid by farmers to the insurance party is Rp 147,489. Respondents in the study were farmers who owned rice fields. Instrument quality testing (questionnaire) using validity test and reliability test using the help of SPSS statistical software. It can be concluded that the questionnaire is valid and reliable. Based on the results of the questionnaire, farmers considered that the nominal agricultural insurance premiums are in accordance with farmers' income.

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

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

Discriminant AnalysisFor Cluster Validation In A Case Study of District Grouping In Jember Regency Based On Poverty Fikriana Nur Istiqomah; Made Tirta; Dian Anggareni
Jurnal ILMU DASAR Vol 20 No 2 (2019)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Cluster validation is a procedure to evaluate the results of cluster analysis quantitively and objectively on a data. The validation process is very important to get the results of a good and appropriate grouping. In the validation process, the author uses internal validation, stability, and discriminant analysis test. This study aims to obtain validation results from the hierarchy and kmeans method. This data grouping uses “iris” simulation data, which results from the grouping method used can be applied to the original data to see which vaidation method is used for all data and produce an optimal grouping. The result of the study show that in the “iris” data, a single linkage link is an appropriate grouping method because the result of the grouping are optimal for all validations and classification of group members whose groups are significant. In District poverty data in Jember Regency with a single linkage link optimal grouping was obtained and complete linkage links were also used as a method that resulted in optimal groupig for all validation. Cluster validation discriminant analysis test is appropriate for various types of data in general annd shows that single linkage methods are better than other methods for grouping and validation methods for “iris” data and District data in Jember Regency based on variabels of poverty status. Keywords: Cluster Analysis, Diskriminant Analysis, Multivariate Analysis, Validation Cluster.

The Development of Web-based Graphical User Interface for Learning and Fitting Generalized Estimating Equation with Spline Smoothers Made Tirta; Dian Anggraeni
Jurnal ILMU DASAR Vol 19 No 1 (2018)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Statistical modeling (regression analyses) have been growing rapidly into various directions to accommodate various data conditions. For longitudinal or repeated measures data, one of the suitable models is GEE (Generalized Estimating Equation). In practice, to do complex modeling such as GEE, the use of statistical software is necessary and it is available on free open source software R. However, GEE modeling on R can only be access through command line interface (CLI), and most practical researchers very much rely on Graphical User Interface (GUI) based statistics software. To make access to GEE (both order 1 and 2) much easier, we developed, using Shiny toolkit, two types of web-based GUI, standard pull down menu type and e-module type (with narrative theories) that can be utilized for learning and fitting GEE. This paper discusses the features of the interfaces and illustrates the use of them. Keywords: longitudinal data, Generalized Estimating Equation (GEE), exponential families, statistical modeling, correlated response, nonparametric, natural splines, shiny toolkit

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

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

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)

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

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

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

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

Co-Authors A AsÃ¢Â€Â™ad Agus Isdarmawan Agustina Pradjaningsih Aminatus Sholiha Anak Agung Gede Sugianthara Andina Ishmah Almira, Andina Ishmah Budi Lestari Budi Lestari Cyndy Romarizka Dian Anggareni Dian Anggraeni Dian Anggraeni Erfan Syahuri Fikriana Nur Istiqomah Habibi, Azwar Haeruddin Haeruddin Holipah, Holipah Indriasih Yanuwijaya Istiqomah, Fikriana Nur Kasmuri Kasmuri Kasmuri Kasmuri, Kasmuri Lutfi Ardining Tyas M Fatekurrahman M. Isnaini Marino Marino, Marino Miftahul Ulum Mohamad Fatekurohman Mutiarasari, Yusna Nawal Ika Susanti Nawal Ika Susanti, Nawal Ika Rizka Dwi Hidayati T C P Utama Tika Clarinta Putri Utama Yeni Rismawati Yuliani Setia Dewi

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