Articles

Found 12 Documents
Search
Journal : MEDIA STATISTIKA

PEMODELAN REGRESI NONPARAMETRIK MENGGUNAKAN PENDEKATAN POLINOMIAL LOKAL PADA BEBAN LISTRIK DI KOTA SEMARANG Suparti, Suparti; Prahutama, Alan
MEDIA STATISTIKA Vol 9, No 2 (2016): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (150.91 KB) | DOI: 10.14710/medstat.9.2.85-93

Abstract

Semarang is the provincial capital of Central Java, with infrastructure and economic’s growth was high. The phenomenon of power outages that occurred in Semarang, certainly disrupted economic development in Semarang. Large electrical energy consumed by industrial-scale consumers and households in the San Francisco area, monitored or recorded automatically and presented into a historical data load power consumption. Therefore, this study modeling the load power consumption at a time when not influenced by the use of electrical load (t-1)-th. Modeling using nonparametric regression approach with Local polynomial. In this study, the kernel used is a Gaussian kernel. In local polynomial modeling, determined optimum bandwidth. One of the optimum bandwidth determination using the Generalized Cross Validation (GCV). GCV values obtained amounted to 1425.726 with a minimum bandwidth of 394. Modelling generate local polynomial of order 2 with MSE value of 1408.672. Keywords: electrical load, local polinomial, gaussian kernel, GCV.
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI BANYAKNYA KLAIM ASURANSI KENDARAAAN BERMOTOR MENGGUNAKAN MODEL REGRESI ZERO-INFLATED POISSON (Studi Kasus di PT. Asuransi Sinar Mas Cabang Semarang Tahun 2010) Taufan, Muhammad; Suparti, Suparti; Rusgiyono, Agus
MEDIA STATISTIKA Vol 5, No 1 (2012): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (569.023 KB) | DOI: 10.14710/medstat.5.1.49-62

Abstract

Poisson regression is one of model that is often used to model the relationship between response variables in the form of discrete data with a set of predictor variables in the form of continuous, discrete, category, or mixture data. In Poisson regression assumes that the mean of the response variable equal to the variance (equidispersion). But in reality, sometimes found a condition called overdispersion, that the variance value is greater than the mean. One of the cause of overdispersion is excess zero in the response variable. One of model that can be used to overcome this overdispersion problem is Zero-Inflated Poisson (ZIP) regression  model. This model is applied on a case study of motor vehicle insurance in the branch of PT. Asuransi Sinar Mas in Semarang in 2010 to determine the effect of age of car and types of coverage to number of claims filed by the policyholder to the branch of PT. Asuransi Sinar Mas in Semarang. In this case, the occurrence of zeros due to many policyholders did not file a claim to the branch of PT. Asuransi Sinar Mas in Semarang. From the analytical result obtained the conclution that the age of car and types of coverage affect number of claims filed by the policyholder to the branch of PT. Asuransi Sinar Mas in Semarang in 2010.   Keywords: Poisson Regression, Overdispersion, Zero-Inflated Poisson (ZIP) Regression
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (235.567 KB) | DOI: 10.14710/medstat.1.1.1-9

Abstract

If X is predictor variable and Y is response  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, while  the big value of threshold give function estimation very smoothly. Therefore require to be selected value of optimal threshold to determine optimal function estimation.               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 .   Keywords: Nonparametric regression, wavelet thresholding estimator, procedure of False Discovery Rate
PERBANDINGAN METODE REGRESI LINIER MULTIVARIABEL DAN REGRESI SPLINE MULTIVARIABEL DALAM PEMODELAN INDEKS HARGA SAHAM GABUNGAN Afa, Ihdayani Banun; Suparti, Suparti; Rahmawati, Rita
MEDIA STATISTIKA Vol 11, No 2 (2018): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (340.648 KB) | DOI: 10.14710/medstat.11.2.147-158

Abstract

The composite stock price index or Indonesia Composite Index (ICI) is a composite index of all stocks listed on the Indonesia Stock Exchange and its movements indicate conditions that occur in the capital market. For investors, the ICI movement is one of the important indicator to make a decision whether the stocks will be sold, held or bought new shares. The ICI movement (y) was influenced by several factors including Inflation (x1), Exchange Rate (x2) and SBI interest rate (x3). This study aims to compare the ICI modeling  using the parameric and nonparametric approaches, namely multivariable linear regression and multivariable spline regression. Determination of the better model is based on the smaller MSE and the larger R2. The best regression model is multivariable spline regression with x1, x2 and x3, each with a sequence orde (3,2,2) and the number of knot points (1,2,2).Keywords: Indonesia Composite Index, Multiple Linear Regression, Multivariable Spline Regression, MSE, R2
PEMODELAN VOLATILITAS UNTUK PENGHITUNGAN VALUE AT RISK (VaR) MENGGUNAKAN FEED FORWARD NEURAL NETWORK DAN ALGORITMA GENETIKA Yasin, Hasbi; Suparti, Suparti
MEDIA STATISTIKA Vol 7, No 2 (2014): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (580.333 KB) | DOI: 10.14710/medstat.7.2.53-61

Abstract

High fluctuations in stock returns is one problem that is considered by the investors. Therefore we need a model that is able to predict accurately the volatility of stock returns. One model that can be used is a model Generalized Autoregressive Conditional Heteroskedasticity (GARCH). This model can serve as a model input in the model Feed Forward Neural Network (FFNN) with Genetic Algorithms as a training algorithm, known as GA-Neuro-GARCH. This modeling is one of the alternatives in modeling the volatility of stock returns. This method is able to show a good performance in modeling the volatility of stock returns. The purpose of this study was to determine the stock return volatility models using a model GA-Neuro-GARCH on stock price data of PT. Indofood Sukses Makmur Tbk. The result shows that the determination of the input variables based on the ARIMA (1,0,1) -GARCH (1,1), so that the model used FFNN consists of 2 units of neurons in the input layer, 5 units of neurons in the hidden layer neuron layer and 1 unit in the output layer. then using a genetic algorithm with crossover probability value of 0.4, was obtained that the Mean Absolute Percentage Error (MAPE) of 0,0039%. Keywords: FFNN, Genetic Algorithm, GARCH, Volatility
ANALISIS DATA INFLASI INDONESIA MENGGUNAKAN MODEL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DENGAN PENAMBAHAN OUTLIER Suparti, Suparti; Sa'adah, Alfi Faridatus
MEDIA STATISTIKA Vol 8, No 1 (2015): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (553.653 KB) | DOI: 10.14710/medstat.8.1.1-11

Abstract

The inflation data is one of the financial time series data which often has high volatility. It is caused by the presence of outliers in the data. Therefore, it is necessary to analyze forecasting that can make all the assumptions are fulled without having to ignore the presence of outliers. The aim of this study is analyzing the inflation data in Indonesia using ARIMA model with the outlier detection. By modeling annual inflation data in December 2006 to December 2013 there are two types of outlier that are additive outlier (AO) and level shift (LS) outlier. The results show that The ARIMA model with the addition of outlier are better than the ARIMA model without outlier. The ARIMA ([1.12], 1.0) model with the addition of 19 outliers meet to the all assumptions that are the significance parameters, normality, homoscedasticity, and independence of residuals as well as the smallest MSE value. Keywords: Inflation, ARIMA, Outlier, MSE
ANALISIS DATA INFLASI DI INDONESIA MENGGUNAKAN MODEL REGRESI SPLINE Suparti, Suparti
MEDIA STATISTIKA Vol 6, No 1 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (651.863 KB) | DOI: 10.14710/medstat.6.1.1-9

Abstract

The inflation data is one of the financial time series data that has a high volatility, so if the data is modeled with parametric models (AR, MA and ARIMA), sometimes occur problems because there was an assumption that cannot be satisfied. The developed model of parametric to cope with the volatility of the data is the ARCH and GARCH models. This alternative parametric models still requires the normality assumption in the data that often cannot be satisfied by financial data. Then a nonparametric method that does not require strict assumptions as parametric methods is developed. This research aims to conduct a study in Indonesia inflation data modeling using nonparametric methods is spline regression model with truncated spline bases. Goodness of a spline regression model is determined by an orde and knots location . However, the knots location are more dominant in spline regression model. One way to get the optimal knots location are by minimizing the value of Generalized Cross Validation (GCV). By modeling the annual inflation data of Indonesia in December 2006 - December 2011, the inflation target in 2012 is 4.5% + 1% can be achieved while the inflation target in 2013 is 4.5% + 1% cannot be achieved, because that prediction in 2013 is 8.55%. It was caused by government policy to raise the price of basic electricity and the fuel prices in 2013. Keywords : Inflation, Spline Regression Model, Generalized Cross Validation.
ANALISIS DATA INFLASI DI INDONESIA PASCA KENAIKAN TDL DAN BBM TAHUN 2013 MENGGUNAKAN MODEL REGRESI KERNEL Suparti, Suparti
MEDIA STATISTIKA Vol 6, No 2 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (314.708 KB) | DOI: 10.14710/medstat.6.2.91-101

Abstract

The inflation data is one of the financial time series data that has a high volatility, so if the data is modeled with parametric models (AR, MA and ARIMA), sometimes occur problems because there was an assumption that cannot be satisfied. Then a nonparametric method that does not require strict assumptions as parametric methods is developed. This study aims to analyze inflation in Indonesia after the goverment raised the price of electricity basic and fuel price in 2013 using kernel regression models. This method was good for data modeling inflation in Indonesia before. The goodness of a kernel regression model is determined by the chosen kernel function and wide bandwidth used. However, the most dominant is the selection of the wide bandwidth. In this study, determination of the optimal bandwidth by minimizing the Generalized Cross Validation (GCV). By model the annual inflation data (Indonesia) December 2006 - December 2011, the inflation target in 2012 is (4,5 + 1 )% can be achieved both exactly and predictly, while the inflation target in 2013 is (4,5 + 1 )% cannot be achieved neither exactly nor predictly. The inflation target in 2013 can’t be achieve because since the beginning of 2013, there was a government policy to raise the price of electricity and the middle of 2013, there was an increase in fuel prices. The prediction of Indonesia inflation in 2014 by Gauss kernel is 6,18%. Keywords: Inflation, Kernel Regression Models, Generalized Cross Validation
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (341.567 KB) | DOI: 10.14710/medstat.2.2.56-69

Abstract

If x is a predictor variable and y is a response  variable of  the regression model y = f (x)+ Î with  f is a regression function which not yet been known and Î 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,  the value of  threshold has  the most important role to determine  level of smoothing estimator. The small threshold give function estimation very no smoothly, while  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  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  the result of data analysis, the optimal threshold with cross validation method is not uniq, but they give the  uniq of wavelet thersholding regression estimation.   Keywords : Nonparametric Regression, Wavelet Threshold Estimator, Cross Validation.
BIPLOT UNTUK MENGETAHUI KARAKTERISTIK KABUPATEN/KOTA DI JAWA TENGAH BERDASARKAN PRODUKSI BAWANG PUTIH, BAWANG MERAH, CABE BESAR DAN CABE RAWIT Safitri, Diah; Suparti, Suparti; Pratiwi, Esti; Estiningrum, Tyas
MEDIA STATISTIKA Vol 7, No 1 (2014): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (124.049 KB) | DOI: 10.14710/medstat.7.1.47-52

Abstract

Biplot is a graphical representation of a data matrix. Garlic, onions, chili, and thai pepper are important plant in Indonesia because most people in Indonesia especially in Central Java consume garlic, onions, chili, and thai pepper every day. In this research, districts in Central Java seen characteristics are based on the productions of garlic, onions, chili, and thai pepper using biplot. There are highly correlation between chili and thai pepper, which means districts that have highly productions of chili will also tend to have highly production of thai pepper. There are some districts have the production of  garlic, onions, chili, and thai pepper relatively low, and there are some of the city has zero production of  garlic, onions, chili, and thai pepper.   Keywords: Biplot, Production of  garlic, onions, chili, thai pepper
Co-Authors A. Sulaksono, A. Abdul Hoyyi Afa, Ihdayani Banun Agus Cahyono Agus Rusgiyono Agustina, Dwi Ampuni Ahmad Reza Aditya Akhmad Zaki Alan Prahutama Alanindra Saputra Alvita Rachma Devi Amanda Devi Paramitha, Amanda Devi Aminah Asngad Any Setyaningsih, Any Arief Rachman Hakim Asismarta Asismarta, Asismarta Azizah, Adilla Nur Bayu Ariawan Budi Warsito Bunga Maharani, Bunga C Yuwono Sumasto, C Yuwono Deden Aditya Nanda, Deden Aditya Destiyani, Eka Di Asih I Maruddani Diah Budiati Diah Safitri Dini Puspita Dwi Ispriyanti Dwi Wahyuningsih, Dwi Dyah Ayu Kusumaningrum Ebeit Devita Simatupang Elyas Darmawan Ernawati, Devi Ernik Yuliana Esti Pratiwi Fadilah, Eka Fajar Heru Setiawan, Fajar Heru Farah, Sania Anisa Farikhin Farikhin Femadiyanti, Siti Fadhilla Fina Fitriyana Firda Megawati, Firda Fitri Juniaty Simatupang, Fitri Juniaty Fitriyatno Fitriyatno Gita Suci Ramadhani Habibah, Immawati Ainun Hafii Risalam, Hafii Hamid, Lukman Hanifa Eka Oktafiani, Hanifa Eka Happy Suci Puspitasari Hasbi Yasin Icha Puspitasari Ikrima, Hanjar Indra Satria, Indra Iwan Ali Sofwan Izzudin Khalid, Izzudin Karimawati, Nurul Kartika, Aninda Ayu Kartikaningtiyas Hanunggraheni Saputri, Kartikaningtiyas Hanunggraheni Khoirunnisa Nur Fadhilah, Khoirunnisa Nur Khoirunnisa, Siti Intan Lailly Rahmatika, Lailly Lestariningsih, Eni Dwi Lina Agustina, Lina Lintang Afdianti Nurkhasanah, Lintang Afdianti Lismiyati Marfuah, Lismiyati Lulus Darwati, Lulus Ma'sum, M. Ali Maman Suryaman Moch. Abdul Mukid Mu'affa, Lamik Nabil Muhammad Taufan Muqorobin, Masculine Muhammad Musandingmi Elok Nurul Islam, Musandingmi Elok Nurul Mustafid Mustafid Mustofa, Achmad Natanael, Dimas Kevin Ndaru Dian Darmawanti Nonik Brilliana Primastuti Novia Agustina, Novia Onny Kartika Hitasari, Onny Kartika Paula Meilina Dwi Hapsari Putri Agustina Rahmawati, Resti Rahmawati, Rizky Dwi Ramadhan, Setyoko Prismanu Rambat Rambat, Rambat Ratih Binadari, Ratih Renti Oktaria, Renti Ria Sutitis, Ria Riana Ayu Andam Pradewi Richy Priyambodo Rinjani, Silvia Nur Rita Rahmawati Riyan Eko Putri Rizani, Nurul Fitria Fitria Rukun Santoso Sa'adah, Alfi Faridatus Sadjati, Ida Malati Sadjati, Ida Malati Safitri, Wardani Ana Sanitoria Nadeak, Sanitoria Sari, Shinta Karunia Permata Seta Satria Utama Setiawan, Fuad Alfaridzi Setiawati, Teti Setya Ayu Rahmawati Siti Anisah Sofyan Anif Sri Budiasih, Sri Sri Sumiyati Sri Wahyuningrum Sudargo Sudargo, Sudargo Sudarno Sudarno Sugito Sugito Suhartini, Arianti Sulton Syafii Katijaya Sunardi Sunardi Surasmi, Wuwuh Asrining Swasnita Swasnita, Swasnita Syariati, Dian T. Mart, T. Tarno Tarno Tatik Widiharih Tedjo, Martyanto Testiana Deni Wijayatiningsih Tiani Wahyu Utami Triastuti Rahayu Triastuti Wuryandari Triyanto Triyanto Tyas Estiningrum Umi Sulistyorini Adi, Umi Sulistyorini Vera Handayani Victoria Dwi Murti Wasis Wicaksono Widari Widari, Widari Wulan Safitri, Wulan Yasir Sidiq Yon Haryono Yuciana Wilandari Yudi Ari Wibowo Yuningsih Yuningsih Yusuf Arifka Rahman, Yusuf Arifka Zia, Nabila Ghaida Zubaidah, Lailia