cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota semarang,
Jawa tengah
INDONESIA
Media Statistika
Published by Universitas Diponegoro
ISSN : -     EISSN : 24770647     DOI : -
Core Subject : Science,
Decision Sciences, Operations Research & Management
Arjuna Subject : -
Articles 271 Documents
 19   20   21   22   23 
ESTIMATION OF SEMIPARAMETRIC REGRESSION CURVE WITH MIXED ESTIMATOR OF MULTIVARIABLE LINEAR TRUNCATED SPLINE AND MULTIVARIABLE KERNEL Hesikumalasari Hesikumalasari; I Nyoman Budiantara; Vita Ratnasari; Khaerun Nisa'
MEDIA STATISTIKA Vol 15, No 1 (2022): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.15.1.12-23

Abstract

The response variable of the regression analysis has a linear relationship with one of the variable predictors, however the unknown relationship pattern with the other predictor variables. Consequently, it can be approached by using semiparametric regression model. The predictor variable that has a linear relationship with the response variable can be approached by using linear parametric curve called parametric component. Meanwhile, the unknown relationship between the response variable with another predictor variable can be approached by using nonparametric curve called nonparametric component. If the predictor variable in nonparametric component is more than one, then it can be approached by using a different nonparametric curve named combined or mixed estimator. In this research, nonparametric component is approached using mixed estimator of multivariable linear truncated spline and multivariable kernel. The objective of this research is to estimate the model of semiparametric regression curve with mixed estimator of multivariable truncated spline and multivariable kernel. Estimation of this mixed model using ordinary least square method.
FORECASTING COVID-19 IN INDONESIA WITH VARIOUS TIME SERIES MODELS Gumgum Darmawan; Dedi Rosadi; Budi Nurani Ruchjana; Resa Septiani Pontoh; Asrirawan Asrirawan; Wirawan Setialaksana
MEDIA STATISTIKA Vol 15, No 1 (2022): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.15.1.83-93

Abstract

In this study, Covid-19 modeling in Indonesia is carried out using a time series model. The time series model used is the time series model for discrete data. These models consist of Feedforward Neural Network (FFNN), Error, Trend, and Seasonal (ETS), Singular Spectrum Analysis (SSA), Fuzzy Time Series (FTS), Generalized Autoregression Moving Average (GARMA), and Bayesian Time Series. Based on the results of forecast accuracy calculation using MAPE (Mean Absolute Percentage Error) as model evaluation for confirmed data, the most accurate case models is the bayesian model of 0.04%, while all recovered cases yield MAPE 0.05%, except for FTS = 0.06%. For data for death cases SSA and Bayesian Models, the best with MAPE is 0.07%.
ESTIMATION OF IBNR AND RBNS RESERVES USING RDC METHOD AND GAMMA GENERALIZED LINEAR MODEL Tiara Yulita; Adhitya Ronnie Effendie
MEDIA STATISTIKA Vol 15, No 1 (2022): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.15.1.24-35

Abstract

Estimation of claims reserves is a very important role for insurance companies because the information will be used to assess the insurance company’s ability to meet future claim payment obligations. In practice, claims reserves are divided into two Incurred but Not Reported (IBNR) and Reported but Not Settled (RBNS). Reserving by Detailed Conditioning (RDC) is one of the individual methods that can estimate claims reserves of both the IBNR and RBNS, which involves detailed condition so-called claim characteristics, and some information else so-called background variable. The result of estimating claims reserves using RDC with background variable is not stable because many combinate of calculation from each background variable. The purpose of this study is to overcome these problems, which we can combine RDC and Gamma Generalized Linear Model (GLM) as an effective method for estimating claims reserves. By using Bootstrapping Individual Claims Histories (BICH) method, the results show that estimation of claims reserves using RDC and Gamma GLM gives the fewest value of Mean Square Error of Prediction (MSEP) rather than RDC with Poisson GLM, RDC, and Chain Ladder. Where the smaller the value of the resulting MSEP estimate, the closer to the actual claim reserve value.
FUZZY VECTOR AUTOREGRESSION FOR FORECASTING FARMER EXCHANGE RATE IN CENTRAL JAVA PROVINCE Nurhayadi Nurhayadi
MEDIA STATISTIKA Vol 15, No 1 (2022): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.15.1.94-103

Abstract

Computer technology has developed to a very advanced measure. Calculations using complex formulas are no longer an obstacle for industry and researchers. Along with advances in computing technology, the development of fuzzy system models is also experiencing rapid progress. This paper proposes a fuzzy model combined with Vector Autoregression. The fuzzy membership function is built by selecting the median of each set to be the center of the fuzzy set. The function chosen as the membership function is Gaussian. The fuzzy Vector Autoregression model obtained was applied to the Farmer's Exchange Rate in Central Java Province. The accuracy of the model is measured based on the Mean Absolute Percentage Error. The results of model trials on FER Central Java in 2014-2020, show a pretty good forecast, namely forecasting with MAPE around 5%, and not exceeding 10%.
EXTRA TREES METHOD FOR STOCK PRICE FORECASTING WITH ROLLING ORIGIN ACCURACY EVALUATION Dani Al Mahkya; Khairil Anwar Notodiputro; Bagus Sartono
MEDIA STATISTIKA Vol 15, No 1 (2022): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.15.1.36-47

Abstract

Stock is an investment instrument that has risk in its management. One effort to minimize this risk is to model and make further forecasts of stock price movements. Time series data forecasting with autoregressive models is often found in several cases with the most popular approach being the ARIMA model. The tree-based method is one of the algorithms that can be used to forecast both in classification and regression. One ensemble approach to tree-based methods is Extra Trees. This study aims to forecast using the Extra Trees algorithm by evaluating forecasting accuracy with Rolling Forecast Origin on BRMS stock price data. Based on the results obtained, it is known that Extra Trees produces a fairly good accuracy for forecasting up to 6 days after training data with a MAPE of less than 0.1%.
STRUCTURAL EQUATION MODELING FOR ANALYZING THE TECHNOLOGY ACCEPTANCE MODEL OF STUDENTS IN ONLINE TEACHING DURING THE COVID-19 PANDEMIC Suwardi Annas; Ruliana Ruliana; Wahidah Sanusi
MEDIA STATISTIKA Vol 15, No 1 (2022): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.15.1.104-115

Abstract

Online teaching can be a solution in the learning process during the pandemic to stop the spreading of the Covid-19 infection. Universitas Negeri Makassar (UNM) as an educational institution provided a Learning Management System (LMS) to support the online teaching and learning process with the platform name SYAM-OK. In this research, we examine the behavioral model of a student's acceptance of the use of an information system SYAM-OK in online teaching. 120 students in the sample used online teaching fully during the pandemic. The data was obtained from an online questionnaire using a google form whose contents were based on Technology Acceptance Model (TAM).  The variable of TAM consists of Perceived Ease of Use, Perceived Usefulness, Attitude Towards, Behavioral Intention, and Actual Use. The Structural Equation Modeling (SEM) PLS method was used in this research for modeling the relationship between TAM variables. Based on the results of the SEM we obtained that Perceived Usefulness significantly affects the Attitude Towards and Attitude Towards significantly affects the behavioral intention. By using the bootstrapping and T statistics, we conclude that SEM has identified the significant effects between variables of TAM. 
THE INTERPLAY BETWEEN CLUSTERS, COVARIATES, AND SPATIAL PRIORS IN SPATIAL MODELLING OF COVID-19 IN SOUTH SULAWESI PROVINCE, INDONESIA Aswi Aswi; Muhammad Arif Tiro; Sudarmin Sudarmin; Sukarna Sukarna; Susanna Cramb
MEDIA STATISTIKA Vol 15, No 1 (2022): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.15.1.48-59

Abstract

A number of previous studies on Covid-19 have used Bayesian spatial Conditional Autoregressive (CAR) models. However, basic CAR models are at risk of over-smoothing if adjacent areas genuinely differ in risk. More complex forms, such as localised CAR models, allow for sudden disparities, but have rarely been applied to modelling Covid-19, and never with covariates. This study aims to evaluate the most suitable Bayesian spatial CAR localised models in modelling the number of Covid-19 cases with and without covariates, examine the impact of covariates and spatial priors on the identified clusters and which factors affect the Covid-19 risk in South Sulawesi Province. Data on the number of confirmed cases of Covid-19 (19 March 2020 -25 February 2022) were analyzed using the Bayesian spatial CAR localised model with a different number of clusters and priors. The results show that the Bayesian spatial CAR localised model with population density included fits the data better than a corresponding model without covariates. There was a positive correlation between the Covid-19 risk and population density. The interplay between covariates, spatial priors, and clustering structure influenced the performance of models. Makassar city and Bone have the highest and the lowest relative risk (RR) of Covid-19 respectively.
COMPARISON OF SMOTE RANDOM FOREST AND SMOTE K-NEAREST NEIGHBORS CLASSIFICATION ANALYSIS ON IMBALANCED DATA Jus Prasetya; Abdurakhman Abdurakhman
MEDIA STATISTIKA Vol 15, No 2 (2022): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.15.2.198-208

Abstract

In machine learning study, classification analysis aims to minimize misclassification and also maximize the results of prediction accuracy. The main characteristic of this classification problem is that there is one class that significantly exceeds the number of samples of other classes. SMOTE minority class data is studied and extrapolated so that it can produce new synthetic samples. Random forest is a classification method consisting of a combination of mutually independent classification trees. K-Nearest Neighbors which is a classification method that labels the new sample based on the nearest neighbors of the new sample. SMOTE generates synthesis data in the minority class, namely class 1 (cervical cancer) to 585 observation respondents (samples) so that the total observation respondents are 1208 samples. SMOTE random forest resulted an accuracy of 96.28%, sensitivity 99.17%, specificity 93.44%, precision 93.70%, and AUC 96.30%. SMOTE K-Nearest Neighborss resulted an accuracy of 87.60%, sensitivity 77.50%, specificity 97.54%, precision 96.88%, and AUC 82.27%. SMOTE random forest produces a perfect classification model, SMOTE K-Nearest neighbors classification produces a good classification model, while the random forest and K-Nearest neighbors classification on imbalanced data results a failed classification model.
MANAGING HEART RELATED DISEASE RISKS IN BPJS KESEHATAN USING COLLECTIVE RISK MODELS Gede Ary Prabha Yogesswara; Danang Teguh Qoyyimi; Abdurakhman Abdurakhman
MEDIA STATISTIKA Vol 15, No 2 (2022): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.15.2.175-185

Abstract

BPJS Kesehatan is a legal entity established to administer the health service program using the insurance system. Heart related diseases is a disease with the largest coverage cost in Indonesia. It can be calculated by using the collective risk model as an approximation of the aggregate loss model. This model is a compound distribution from claim frequency and claim severity, where claim frequency be the primary distributions. The Poisson distribution can be used to the distribution of the heart disease claim frequency. Whereas, the distribution of the heart disease claim severity has a lognormal distribution. The model obtained can explain the aggregate loss of heart disease claims properly.
SPRATAMA MODEL FOR INDONESIAN PARAPHRASE DETECTION USING BIDIRECTIONAL LONG SHORT-TERM MEMORY AND BIDIRECTIONAL GATED RECURRENT UNIT Titin Siswantining; Stanley Pratama; Devvi Sarwinda
MEDIA STATISTIKA Vol 15, No 2 (2022): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.15.2.129-138

Abstract

Paraphrasing is a way to write sentences with other words with the same intent or purpose. Automatic paraphrase detection can be done using Natural Language Sentence Matching (NLSM) which is part of Natural Language Processing (NLP). NLP is a computational technique for processing text in general, while NLSM is used specifically to find the relationship between two sentences. With the development Neural Network (NN), nowadays NLP can be done more easily by computers. Many models for detecting and paraphrasing in English have been developed compared to Indonesian, which has less training data. This study proposes SPratama Model, which models paraphrase detection for Indonesian using a Recurrent Neural Network (RNN), namely Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU). The data used is "Quora Question Pairs" taken from Kaggle and translated into Indonesian using Google Translate. The results of this study indicate that the proposed model has an accuracy of around 80% for the detection of paraphrased sentences.

Page 21 of 28 | Total Record : 271

 19   20   21   22   23 

Filter by Year

2008 2025


Reset
Filter By Issues
All Issue Vol 18, No 1 (2025): Media Statistika Vol 17, No 2 (2024): Media Statistika Vol 17, No 1 (2024): Media Statistika Vol 16, No 2 (2023): Media Statistika Vol 16, No 1 (2023): Media Statistika Vol 15, No 2 (2022): Media Statistika Vol 15, No 1 (2022): Media Statistika Vol 14, No 2 (2021): Media Statistika Vol 14, No 1 (2021): Media Statistika Vol 13, No 2 (2020): Media Statistika Vol 13, No 1 (2020): Media Statistika Vol 12, No 2 (2019): Media Statistika Vol 12, No 1 (2019): Media Statistika Vol 11, No 2 (2018): Media Statistika Vol 11, No 1 (2018): Media Statistika Vol 10, No 2 (2017): Media Statistika Vol 10, No 1 (2017): Media Statistika Vol 9, No 2 (2016): Media Statistika Vol 9, No 1 (2016): Media Statistika Vol 8, No 2 (2015): Media Statistika Vol 8, No 1 (2015): Media Statistika Vol 7, No 2 (2014): Media Statistika Vol 7, No 1 (2014): Media Statistika Vol 6, No 2 (2013): Media Statistika Vol 6, No 1 (2013): Media Statistika Vol 5, No 2 (2012): Media Statistika Vol 5, No 1 (2012): Media Statistika Vol 4, No 2 (2011): Media Statistika Vol 4, No 1 (2011): Media Statistika Vol 3, No 2 (2010): Media Statistika Vol 3, No 1 (2010): Media Statistika Vol 2, No 2 (2009): Media Statistika Vol 2, No 1 (2009): Media Statistika Vol 1, No 2 (2008): Media Statistika Vol 1, No 1 (2008): Media Statistika More Issue