E jurnal Matematika

Number Vol.4 (2), 2015

31-36
PEMODELAN RISIKO PENYAKIT PNEUMONIA PADA BALITA DI PROVINSI JAWA TIMUR DENGAN PENDEKATAN GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION

Authors: Evi Noviyantari Fatimah, I Komang Gde Sukarsa, Made Susilawati

Abstract

View in PDF

Number of views: 460

This research is aim to determine the comparison of logistic regression models and models Geographically Weighted Logistic Regression and the factors that significantly affect the risk of pneumonia in toddlers in East Java Province. Logistic regression is a statistical analysis that is used to describe the response variable is categorical with the independent variables are categorical or continuous. The main problem of this method if it’s applied in data that is affected of geographic location or spatial data. One of many method to solve the spatial data is Geographically Weighted Logistic Regression (GWLR). GWLR is a statistical method for analyze the data to account for spatial factor. The results showed that there are no significant differences between the logistic regression model with GWLR model. Factors that significantly affect the risk of pneumonia in toddlers in East Java Province is the percentage of low birth weight, the percentage of toddlers who get measles immunization, the percentage of toddlers who get vitamin A, and the percentage of toddlers who get DPT+HB immunization. Keywords: Logistic Regression, Geographically Weighted Logistic Regression, Pneumonia

37-42
KAJIAN TERHADAP TINGKAT PEMERATAAN PENDIDIKAN MENGGUNAKAN ANALISIS BIPLOT KLASIK DAN BIPLOT KEKAR

Authors: Ni Luh Ardila Kusumayanti, I Komang Gde Sukarsa, Tjokorda Bagus Oka, I Putu Eka Nila Kencana

Abstract

View in PDF

Number of views: 488

The aim of this research is to find the better from classical and robust biplot in determine dominant indicators of educational equity in Bali, NTB and NTT Provinces. This research based on secondary data obtain from Central Bureau of Statistics for year 2012/2013. Educational equity was portraited by Classical and Robust Biplot. The results of this research showed Robust Biplot is better method which goodness of fit is 90,64% meanwhile Classical Biplot as much as 83,62%. The Robust Biplot showed Students- Junior or Islamic Middle School Ratio and Students-Senior or Islamic High School were dominant indicators to educational equity in Bali, NTB and NTT Provinces. Keywords: educational equity, education indicators, classical biplot, robust biplot.

43-48
MENENTUKAN HARGA KONTRAK BERJANGKA NILAI TUKAR RUPIAH TERHADAP DOLLAR AS MENGGUNAKAN DISTRIBUSI LOGNORMAL

Authors: Gede Sumendra, Komang Dharmawan, I Nyoman Widana

Abstract

View in PDF

Number of views: 439

The purpose of this study is to determine the fair price of a futures contract for the IDR (Rupiah) against the USD using lognormal distribution simulation. This result is compared with interest rate parity theorem. The first step of this study is to determine the values of the parameters which are optimized using Maximum Likelihood Estimation (MLE). The parameters obtained in the form of the mean (μ) and variance (σ^2). Further, parameters obtained are simulated using lognormal distribution to determine the exchange rate simulation (S_t). Then price of future contract is also calculated using interest rate parity theorem. The price of the futures contracts (F_t) is determined by lognormal distribution simulated and price of interest rate futures contracts using parity theorem. The results of this study show that future contract price over the fair use lognormal distribution of R_p 12.215 compared to the interest rate parity theorem which R_p 12.400, with the initial contract price (S_0) of R_p 12.185. Keywords: derivatives, futures contracts, the exchange rate against the USD, lognormal distribution, interest rate parity theorem.

49-53
PENERAPAN REGRESI PROBIT BIVARIAT UNTUK MENDUGA FAKTOR-FAKTOR YANG MEMENGARUHI KELULUSAN MAHASISWA (Studi Kasus: Mahasiswa Fakultas MIPA Unversitas Udayana)

Authors: Ni Gusti Ketut Trisna Pradnyantari, I Komang Gde Sukarsa, Ni Luh Putu Suciptawati

Abstract

View in PDF

Number of views: 486

The aim of this research to estimate the factors that affect students graduation using bivariate probit regression. Bivariate probit regression is a statistical method that involves two response variables which are qualitative and the independent variables are qualitative, quantitative, or a combination of both. In bivariate probit regression model, the result obtained is the probability of the response variable. The result of this research are the factors that affect significantly for students graduation based on study period are majors, sex, and duration of the thesis, while the factors that significantly for students graduation based on GPA are the entry system, duration of the thesis and the number of parents’ dependents. Keywords: Bivariate Probit Regression, Study Period, Grade Point Average (GPA), Students Graduation

54-58
PENERAPAN REGRESI LOGISTIK ORDINAL UNTUK MENGANALISIS TINGKAT KEPARAHAN KORBAN KECELAKAAN LALU LINTAS KABUPATEN BULELENG

Authors: Dewa Ayu Made Dwi Yanti Purnami, I Komang Gde Sukarsa, G. K. Gandhiadi

Abstract

View in PDF

Number of views: 498

Ordinal logistic regression is a statistical method for analyzing the respone variables that have an ordinal scale consisting of three or more categories. This method is an extension of logistic regression with a binary respone variable. In this study the cases studies was the severity of traffic accident victims in Buleleng. The severity of the victims were divided into three categories: minor injuries, serious injuries and died. This research also used six predictor variables, namely age, hours of accident, education, gender, the status of location, and the venicles involved. Models obtained from this study is: and This shows that the variables age, hours of accident, education and the status of location have a significan effect on the severity of traffic accident victims. Keywords: Ordinal logistic regression, severity of traffic accident victims

59-66
MODEL NON LINIER GARCH (NGARCH) UNTUK MENGESTIMASI NILAI VALUE at RISK (VaR) PADA IHSG

Authors: I Komang Try Bayu Mahendra, Komang Dharmawan, Ni Ketut Tari Tastrawati

Abstract

View in PDF

Number of views: 449

In investment, risk measurement is important. One of risk measure is Value at Risk (VaR). There are many methods that can be used to estimate risk based on VaR framework. One of them Non Linier GARCH (NGARCH) model. In this research, determination of VaR used NGARCH model. NGARCH model allowed for asymetric behaviour in the volatility such that “good news” or positive return and “bad news” or negative return. Based on calculations of VaR, the higher of the confidence level and the longer the investment period, the risk was greater. Determination of VaR using NGARCH model was less than GARCH model. eywords: GARCH model , NGARCH model, Value at Risk

67-73
PENENTUAN NILAI VALUE at RISK PADA SAHAM IHSG MENGGUNAKAN MODEL GEOMETRIC BROWNIAN MOTION DENGAN LOMPATAN

Authors: I Gede Arya Duta Pratama, Komang Dharmawan, Luh Putu Ida Harini

Abstract

View in PDF

Number of views: 453

The aim of this research was to measure the risk of the IHSG stock data using the Value at Risk (VaR). IHSG stock index data typically indicates a jump. However, Geometric Brownian Motion (GBM) model can not catch any of the jumps. To view the jumps, it is necessary that the model was then developed into a Geometric Brownian Motion (GBM) model with Jumps. On the GBM model with Jumps, returns the data are discontinuous. To determine the value of VaR, the value of return to perform the simulation model of GBM with Jumps is required. To represent processes that contain jumps, discontinuous Poisson process using the Peak-Over Threshold is required. To determine the parameters of model, calibration of historical data using the Maximum Likelihood Estimation (MLE) method is performed. VaR value for GBM model with Jumps with a 95% and 99% confidence level are -0,0580 and -0,0818 while VaR value for GBM model with a 95% and 99% confidence level are -0,0101 and -0,0199. VaR for GBM model with Jumps with a confidence level of 95% and 99% show greater than the model VaR for GBM. Keywords: Geometric Brownian Motion with Jumps, Return, Value at Risk

74-82
PENDUGAAN DATA HILANG DENGAN METODE YATES DAN ALGORITMA EM PADA RANCANGAN LATTICE SEIMBANG

Authors: Ni Made Sariayu, Made Susilawati, Kartika Sari

Abstract

View in PDF

Number of views: 540

Missing data often occur in agriculture and animal husbandry experiment. The missing data in experimental design makes the information that we get less complete. In this research, the missing data was estimated with Yates method and Expectation Maximization (EM) algorithm. The basic concept of the Yates method is to minimize sum square error (JKG), meanwhile the basic concept of the EM algorithm is to maximize the likelihood function. This research applied Balanced Lattice Design with 9 treatments, 4 replications and 3 group of each repetition. Missing data estimation results showed that the Yates method was better used for two of missing data in the position on a treatment, a column and random, meanwhile the EM algorithm was better used to estimate one of missing data and two of missing data in the position of a group and a replication. The comparison of the result JKG of ANOVA showed that JKG of incomplete data larger than JKG of incomplete data that has been added with estimator of data. This suggest thatwe need to estimate the missing data. . Keywords: Missing Data, Yates method, EM algorithm, Balanced Lattice Design.