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All Journal REiD (Research and Evaluation in Education) Jurnal Matematika Sains dan Teknologi Jurnal Pendidikan Terbuka Dan Jarak Jauh Psychology, Evaluation, and Technology in Educational Research
Timbul Pardede
Universitas Terbuka
Agriculture, Biological Sciences & Forestry Computer Science & IT Education Mathematics Social Sciences Other

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KAJIAN METODE BERBASIS MODEL PADA ANALISIS KELOMPOK DENGAN PERANGKAT LUNAK MCLUST Timbul Pardede
Jurnal Matematika Sains dan Teknologi Vol. 14 No. 2 (2013)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (645.484 KB) | DOI: 10.33830/jmst.v14i2.378.2013

Abstract

Ward method and K-mean method are clustering method in which grouping only base on distance measure among observed objects, without considering statistical aspects. Model-based clustering is a method that use statistical aspects, as its theoretical basis i.e. probability maximum criterion. This model has tenmodels with a variety of geometrical characteristics. Data partition is conducted by utilizing EM (expectation-maximization) algorithm. Then by using Bayesian Information Criterion (BIC) the best model is obtained. This research aimed to assess the effectiveness of ten models from the model-based clustereng and then tocompare result of grouping methods between model-based clustering with Ward clustering and K-mean clustering. This study used simulated data and applied data. Simulated data are generated with the R programs versions 2.14.1. Proses analysis was performed by using the Mclust programs vesions 4.0 with an interface the R programs versions 2.14.1. The results showed that model-based clustering was more effective in separating the condition of one separate group and two overlap groups than ward clustering and K-mean clustering. Metode Ward dan metode K-rataan adalah metode kelompok yang teknik-teknik pengelompokannya hanya memperhatikan ukuran jarak antar objek-objek pengamatan tanpa mempertimbangkan aspek statistiknya. Metode kelompok berbasis model adalah metode kelompok yang didasarkan pada aspek statistik, yaitu kriteria kemungkinan maksimum. Metode kelompok berbasis model mempunyai sepuluh model dengan berbagai macam sifat geometris. Penyekatan data dilakukan dengan menggunakan algoritma Ekspektasi-Maksimum (EM), kemudian dengan pendekatan Bayesian Information Criterion (BIC) diperoleh model terbaik. Penelitian ini bertujuan untuk mengkaji efektivitas dari sepuluh metode berbasis model dan kemudian membandingkan hasil pengelompokannya dengan metode Ward dan metode K-rataan. Penelitian ini menggunakan data simulasi yang dibangkitkan melali program R versi 2.14.1 dan dianalisis dengan menggunakan program Mclust versi 4.0 dengan interface program R. Hasil penelitian menunjukkan bahwa metode kelompok berbasis model lebih efektif memisahkan kelompok-kelompok yang saling tumpang tindih dibandingkan dengan metode gerombol Ward dan K-rataan.
PERBANDINGAN METODE MODEL-BASED DENGAN METODE K-MEAN DALAM ANALISIS CLUSTER Timbul Pardede
Jurnal Matematika Sains dan Teknologi Vol. 8 No. 2 (2007)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (510.454 KB)

Abstract

K-mean method is a clustering method in which grouping techniques are based only on distance measure among observed objects, without considering statistical aspects. Model-based clustering is a method that use statistical aspects, as its theoretical basis i.e. probability maximum criterion. This model has several variations with a variety of geometrical characteristics obtained by mean Gauss component. Data partition is conducted by utilizing EM (expectation-maximization) algorithm. Then by using Bayesian Information Criterion (BIC) the best model is obtained. This research aimed to comparing result of grouping methods between model-based clustering and K-mean clustering. The results showed that model-based clustering was more effective in separating overlap groups than K-mean.
SISTEM UJIAN ONLINE SEBAGAI UPAYA PENINGKATAN PELAKSANAAN UJIAN DALAM PENDIDIKAN TERBUKA JARAK JAUH *) Timbul Pardede; Sri Listyarini
Jurnal Pendidikan Terbuka Dan Jarak Jauh Vol. 12 No. 1 (2011)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (741.245 KB)

Abstract

Universitas Terbuka (UT) as a higheropen anddistanceeducationinstitution has beencarrying outstudents assessmentin the form ofpaper and pencil and online examinations. This studywas conductedto determinethe effectiveness ofthe implementation of theOnline ExaminationSystem(Sistem Ujian Online=SUO), in terms of the reliability(robustness) of SUO application, the readiness ofHuman Resources inimplementingan online exam, the infrastructure that supporting SUO, and the studentresponses. The results showed that SUO applicationand support application had beenwell developedbyUT, proven that the SUO havebeenin operation in 30 out of 37UTregional centersin 2010. Studentsdid notface manyproblems in registering in the online exam.Infrastructureandhuman resourceswere considered satisfactory. Respondentsalso said that SUO was veryflexiblein terms of choosing theexamschedule andgetting the immediate feedback. Based on the research results, a SUO modelhad been developed and to be implementedatUT. The SUOmodel isdescribed inseveralbusinessprocesses, which includethe preparation, execution, processingexam results, as well assupervisionandevaluation. In the future, UTstillneeds toimprovefacilities, infrastructure andquality of human resources thatcansupport theonline exam. The implementation of online examrelies heavily oninformationtechnology (IT) and itis expectedthatSUOis adaptablewith this rapidly changed technology.
Polytomous scoring correction and its effect on the model fit: A case of item response theory analysis utilizing R Agus Santoso; Timbul Pardede; Ezi Apino; Hasan Djidu; Ibnu Rafi; Munaya Nikma Rosyada; Heri Retnawati; Gulzhaina K. Kassymova
Psychology, Evaluation, and Technology in Educational Research Vol. 5 No. 1 (2022)
Publisher : Research and Social Study Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33292/petier.v5i1.148

Abstract

In item response theory, the number of response categories used in polytomous scoring has an effect on the fit of the model used. When the initial scoring model yields unsatisfactory estimates, corrections to the initial scoring model need to be made. This exploratory descriptive study used response data from Take Home Exam (THE) participants in the Statistical Methods I course organized by the Open University, Indonesia, in 2022. The stages of data analysis include coding the rater’s score; analyzing frequency; analyze the fit of the model based on graded, partial, and generalized partial credit models; analyze the characteristic response function (CRF) curve; scoring correction (rescaling); and re-analyze the fit of the model. The fit of the model is based on the chi-square test and the root mean square error of approximation (RMSEA). All model fit analyzes were performed by using R. The results revealed that scoring corrections had an effect on model fit and that the partial credit model (PCM) produced the best item parameter estimates. All results and their implications for practice and future research are discussed.
The effect of scoring correction and model fit on the estimation of ability parameter and person fit on polytomous item response theory Agus Santoso; Timbul Pardede; Hasan Djidu; Ezi Apino; Ibnu Rafi; Munaya Nikma Rosyada; Harris Shah Abd Hamid
Research and Evaluation in Education Vol 8, No 2 (2022)
Publisher : Sekolah Pascasarjana Universitas Negeri Yogyakarta & HEPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/reid.v8i2.54429

Abstract

Scoring quality has been recognized as one of the important aspects that should be of concern to both test developers and users. This study aimed to investigate the effect of scoring correction and model fit on the estimation of ability parameters and person fit in the polytomous item response theory. The result of 165 students in the Statistics course (SATS4410) test at one of the universities in Indonesia was used to answer the problems in this study. The polytomous data obtained from scoring the test results were analyzed using the Item Response Theory (IRT) approach with the Partial Credit Model (PCM), Graded Response Model (GRM), and Generalized Partial Credit Model (GPCM). The effect of scoring correction and model fit on the estimation of ability and person fit was tested using multivariate analysis. Among the three models used, GRM showed the best fit based on p-value and RSMEA. The results of the analysis also showed that there was no significant effect of scoring correction and model fit on the estimation of the test taker’s ability and person fit. From the results of this study, we recommend the importance of evaluating the levels or categories used in scoring student work on a test.
Gaining a deeper understanding of the meaning of the carelessness parameter in the 4PL IRT model and strategies for estimating it Timbul Pardede; Agus Santoso; Heri Retnawati; Ibnu Rafi; Ezi Apino; Munaya Nikma Rosyada
Research and Evaluation in Education Vol 9, No 1 (2023)
Publisher : Sekolah Pascasarjana Universitas Negeri Yogyakarta & HEPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/reid.v9i1.63230

Abstract

Three popular models are used to describe the characteristics of the test items and estimate the ability of examinees under the dichotomous IRT model, namely the one-, two-, and three-parameter logistic models. The three-item parameters are discriminating power, difficulty, and pseudo-guessing. In the development of the dichotomous IRT model, carelessness or upper asymptote parameter was proposed, which forms a four-parameter logistic (4PL) model to accommodate a condition where high ability examinee gives an incorrect response to a test item when he/she should be able to respond to the test item correctly. However, the carelessness parameter and the 4PL model have not been widely accepted and used due to several factors, and people’s understanding of that parameter and strategies for estimating it is still inadequate. Therefore, this study aims to shed light on ideas underlying the 4PL model, the meaning of the carelessness parameter, and strategies used to estimate that parameter based on the extant literature. The focus of this study was then extended to demonstrating practical examples of estimating item and person parameters using the 4PL model using empirical data on responses of 1,000 students from the Indonesia Open University (Universitas Terbuka) on 21 of 30 multiple-choice items on the Business English test, a paper-and-pencil test. We mainly analyzed empirical data using the ‘mirt’ package in RStudio. We present the results of the analysis in a coherent manner so that IRT users would have a sufficient understanding of the 4PL model and the carelessness parameter, and they can estimate item and person parameters under the 4PL model.
Co-Authors Agus Santoso Djidu, Hasan Ezi Apino Gulzhaina K. Kassymova Harris Shah Abd Hamid Heri Retnawati Ibnu Rafi Listyarini, Sri Munaya Nikma Rosyada