Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

2.986

P-Index

This Author published in this journals

All Journal Media Statistika MATEMATIKA SAINS DAN MATEMATIKA Jurnal Gaussian Jurnal Ilmiah Bening : Belajar Bimbingan Dan Konseling GUIDENA: Jurnal Ilmu Pendidikan, Psikologi, Bimbingan dan Konseling JURNAL PENDIDIKAN TAMBUSAI JURNAL KAJIAN TEKNIK SIPIL Abdi Laksana : Jurnal Pengabdian Kepada Masyarakat MURHUM : JURNAL PENDIDIKAN ANAK USIA DINI Journal Of Human And Education (JAHE) Tsaqofah: Jurnal Penelitian Guru Indonesia EJOIN : Jurnal Pengabdian Masyarakat Innovative: Journal Of Social Science Research Jurnal Pengabdian Masyarakat Indonesia Sejahtera Alamtana

Tarno Tarno

Jurusan Statistika FSM Undip

Author-ID : 401717

Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Public Health Social Sciences Transportation Other

Published : 35 Documents Claim Missing Document

Claim Missing Document

Articles

Title

UJI STASIONERITAS DATA INFLASI DENGAN PHILLIPS-PERON TEST Maruddani, Di Asih I; Tarno, Tarno; Anisah, Rokhma Al
MEDIA STATISTIKA Vol 1, No 1 (2008): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The classical regression model was devised to handle relationships between stationary variables. It should not be applied to nonstationary series. A time series is therefore said to be stationary is its mean, variance, and covariances remain constant over time. A problem associated with nonstationary variables, and frequently faced by econometricians when dealing with time series data, is the spurious regression. An apparent indicator of such spurious regression was a particularly low level for the Durbin-Watson statistics, combined with an acceptable R2. Statistical test for stationarity have proposed by Dickey and Fuller (1979). The distribution theory supporting the Dickey-Fuller test assumes that the errors are statistically independent and have a constant variance. Phillips and Peron (1988) developed a generalization of the Dickey-Fuller procedure that the error terms are correlated and not have constant variance. In this paper, we use Phillips-Peron test for inflation data in Indonesia for the time period 1996-2003. The data showed upward trend and the error terms are correlated. The empirical results showed that the inflation data in Indonesia is a nonstationary series. Â Keywords : stationarity, non autocorrelation, Phillips-Peron Test, inflation

PEMILIHAN THRESHOLD OPTIMAL PADA ESTIMATOR REGRESI WAVELET THRESHOLDING DENGAN METODE CROSS VALIDASI Suparti, Suparti; Tarno, Tarno; Hapsari, Paula Meilina Dwi
MEDIA STATISTIKA Vol 2, No 2 (2009): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

If x is a predictor variable and y is a responseÂ variable of Â the regression model y = f (x)+ ÃŽ withÂ f is a regression function which not yet been known and ÃŽ is independent random variable with mean 0 and variance , hence function f can be estimated by parametric and nonparametric approach. In this paper function f is estimated with a nonparametric approach. Nonparametric approach that used is a wavelet shrinkage or a wavelet threshold method. In the function estimation with a wavelet threshold method, Â the value ofÂ threshold has Â the most important role to determine Â level of smoothing estimator. The small threshold give function estimation very no smoothly, whileÂ the big value of threshold give function estimation very smoothly. Therefore the optimal value of threshold should be selected to determine the optimal function estimation. One of the methods to determine the optimal value of threshold by minimize a cross validation function. The cross validation method that be used is two-fold cross validatiaon. In this cross validation, it compute the predicted value by using a half of data set. The original data set is split Â into two subsets of equal size : one containing only the even indexed data, and the other, the odd indexed data. The odd data will be used to predict the even data, and vice versa. Based on Â the result of data analysis, the optimal threshold with cross validation method is not uniq, but they give the Â uniq of wavelet thersholding regression estimation. Â Keywords : Nonparametric Regression, Wavelet Threshold Estimator, Cross Validation.

PEMILIHAN PARAMETER THRESHOLD OPTIMAL DALAM ESTIMATOR REGRESI WAVELET THRESHOLDING DENGAN PROSEDUR FALSE DISCOVERY RATE (FDR) Suparti, Suparti; Tarno, Tarno; Haryono, Yon
MEDIA STATISTIKA Vol 1, No 1 (2008): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

If X is predictor variable and Y is responseÂ variable of following model Y = f (X) +e with function f is regression which not yet been known and e is independent random variable with mean 0 and variant , hence function of f can estimate with parametric and nonparametric approach. At this paper estimate f with nonparametric approach. Nonparametric approach that used is wavelet shrinkage or wavelet thresholding method. At function estimation with method of wavelet thresholding, what most dominant determine level of smoothing estimator is value of threshold. The small threshold give function estimation very no smoothly, whileÂ the big value of threshold give function estimation very smoothly. Therefore require to be selected value of optimal threshold to determine optimal function estimation. Â Â Â Â Â Â Â Â Â Â Â Â One of the method to determine the value of optimal threshold is with procedure of False Discovery Rate ( FDR). In procedure of FDR, the optimal threshold determined by selection of level of significance. Smaller mount used significance progressively smoothly its . Â Keywords: Nonparametric regression, wavelet thresholding estimator, procedure of False Discovery Rate

ESTIMASI MODEL UNTUK DATA DEPENDEN DENGAN METODE CROSS VALIDATION Tarno, Tarno
MEDIA STATISTIKA Vol 1, No 2 (2008): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

This paper discuss about application of cross-validation method for modeling of dependent data. One of the data that categorized into dependent data is a time series. To construct the mathematical model for a time series data, we must have at least 50 series. In practices we often have some problem as long as we collect the time series data. So we donâ€™t get ideal data related to number of sample. To solve this problem, we can generate observation data. There are several methods that can be used to generate data such as cross-validation and bootstrap. Application of cross-validation method to generate time series data canâ€™t be done randomly, but we must generate the data based on balanced incomplete block design. The basic principle of cross-validation method is the data divided into two parts those are construction data and validation data. Construction data are drawn from observation data based on moving block and then we construct the model with Box-Jenkins method and verify the model with validation data. Do this process for different blocks as replication samples of cross-validation method, such that we can construct the best model that minimized loss function for prediction errors. Â Key words: time series data, estimate model, cross-validation

PEMODELAN DATA INFLASI INDONESIA PADA SEKTOR TRANSPORTASI, KOMUNIKASI, DAN JASA KEUANGAN MENGGUNAKAN METODE KERNEL DAN SPLINE Suparti, Suparti; Tarno, Tarno
MEDIA STATISTIKA Vol 8, No 2 (2015): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In this research, we study data modeling of Indonesian inflation in theÂ transportation, communication and financial services sector using the kernel and spline models. Determination of the optimal models based on the smallest of GCVÂ value and determination of the best model based on the smallest out sampels of Mean Square Error (MSE) value. By modeling the yoy (year on year) inflation data in Indonesia in the transportation, communication and financial services sector In January 2007 to January 2015, shows that the kernel modelÂ using Gaussian kernel function obtained optimal model with a bandwidthÂ 0.24 and the optimal spline model with order 5 andÂ 4 points knots. Based on out sampels dataÂ in February to August 2015, obtained out sampelsÂ MSE value of the spline model is smaller than the kernel model. So that the spline model is better than the kernel modelÂ to analyzeÂ the inflation dataÂ of transportation, communication and financial services sector.Keywords: Inflation, Transportation, Communication and Financial Services Sector, Kernel, Spline, GCV, MSE.

RISK ASSESSMENT OF STOCKS PORTFOLIO THROUGH ENSEMBLE ARMA-GARCH AND VALUE AT RISK (CASE STUDY: INDF.JK AND ICBP.JK STOCK PRICE) Tarno, Tarno; Trimono, Trimono; Maruddani, Di Asih I; Wilandari, Yuciana; Utami, Rianti Siswi
MEDIA STATISTIKA Vol 14, No 2 (2021): 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.14.2.125-136

Stocks portfolio is a form of investment that can be used to minimize the risk of loss. In a stock portfolio, the Value at Risk (VaR) can be predicted through the portfolio return. If portfolio return variance is heteroskedastic risk prediction can be done by using VaR with ARIMA-GARCH or Ensemble ARIMA-GARCH model approach. Furthermore, the accuracy of VaR is tested through Backtesting test. In this study, the portfolio is formed from PT Indofood CBP Sukses Makmur (ICBP.JK) and PT Indofood Sukses Makmur Tbk (INDF.JK) stocks from 01/01/2018 to 07/30/2021. The results showed that the best model isÂ Ensemble ARMA-GARCH with MSE 1.3231Ã—10-6. At confidence level of 95% and 1 day holding period, the VaR of the Ensemble ARMA-GARCH was -0.0213. Based on the Backtesting test, it is proven to be very accurate to predict the value of loss risk because the value of the Violation Ratio (VR) is equal to 0.

Co-Authors Abdul Hoyyi Abdul Rahim Acoci, Acoci Adnan Adnan Agus Rusgiyono Agusalim, Agusalim Agustien, Stevani Akbar, Azaz Aris Sugiharto Aris Susanto Arsyil Hendra Saputra Budi Warsito Dadang, Rahmat Deby Fakhriyana, Deby Desti, Desti Di Asih I Maruddani Diah Safitri Dwi Hasti Ratnasari Dwi Ispriyanti Edison Edison Ervina, Evi Fadillah Yamin, Kurnia Farisatma, Farisatma Faslia, Faslia Febriani, Karina Gawise, Gawise Halim, Rossa Fitria Hartati Hartati Hasbi Yasin Imani, Nanda Diva Lingkar Irman Matje Irwan Irwan Jesica, Haniela Puja Jeti, La Junaidin Junaidin Maimuna Maimuna, Maimuna Manan, Manan Matje, Irman Nur Indah Yuli Astuti, Nur Indah Yuli Nurihsan, Achmad Juntika Paula Meilina Dwi Hapsari Payitno, Isnu Harjo Purnamasari, Wa Ode Dian Rachim, Febyani Rita Rahmawati Rokhma Al Anisah Rosilawati, Elsa Dwi Saefudin, Encep Saleh, Rachman Samantha, Kenia Saputra, Bagus Arya Sari, Sasmita Kartika Setiyowati, Eka Suardin Suardin, Suardin Suarti, Suarti Subanar . Subandono, Joko Suciyanti, Suciyanti Sudarno Sudarno Sugito - Sukmawati, Dina Sulasmi, Andi Suparti Suparti Syamsu Yusuf, Syamsu TITI RACHMI, TITI Tri Wahyu Kuningsih, Tri Wahyu Triastuti Wuryandari Trimono, Trimono Ulfah Sulistyowati Utami, Rianti Siswi Wati, Isna Wijaya, Fatma Pandu Wijaya, Rasman Sastra Yamin, Kurnia Fadillah Yon Haryono Yuciana Wilandari Yusi Riksa Yustiana, Yusi Riksa Yusnan, Muhammad Zubair, Zubair

Title Search

Found 6 Documents Search Journal : Media Statistika

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 6 Documents
Search
Journal : Media Statistika