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Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Mulawarman Jl. Barong Tongkok, Kampus Gunung Kelua Kota Samarinda, Provinsi Kalimantan Timur 75123
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Eksponensial
Published by Universitas Mulawarman
ISSN : 20857829     EISSN : 27983455     DOI : https://doi.org/10.30872/
Core Subject : Economy, Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other
Jurnal Eksponensial is a scientific journal that publishes articles of statistics and its application. This journal This journal is intended for researchers and readers who are interested of statistics and its applications.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 11 Documents
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Search results for , issue "Vol 7 No 2 (2016)" : 11 Documents clear
Penerapan Metode Fuzzy Time Series Using Percentage Change Nurul Hidayah; Ika Purnamasari; Memi Nor Hayati
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

In 1993, Song and Chissom introduce fuzzy times series is capable of handling the problem of data forecasting if historical data are the values ​​of linguistic. The study uses the modeling outline by way of fuzzy relation equations and approximate reasoning to predict the number of students. In this study, the approach to the theory of fuzzy time series used is fuzzy time series using percentage change developed by Stevenson and Porter in 2009. The case studies used in this study is the population of East Kalimantan Province. This study aims to determine how the application of fuzzy time series method using percentage change in the population of East Kalimantan from 1980 until 2013. Forecasting is done menggukan linguistic value of the fuzzy set which is formed of the differences and converted into a percentage of the universe of discourse as a value data. Based on the results of the application of the method using fuzzy time series of the percentage change obtained 12 fuzzy set which is linguistics of the data, the accuracy of forecasting value from 1981 to 2013 using MAPE (Avarage Forcasting Error Rate) that is equal to 0.557%.
Estimasi Parameter Model ARIMA untuk Peramalan Debit Air Sungai Menggunakan Least Square dan Goal Programming Dewi Wulan Sari; Rito Goejantoro; Sri Wahyuningsih
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Forecasting is a technique to make a desicion in the future considered by data from the past and present. This forecasting is in hydrology sector which is river flow forecasting. River flow forecasting is one way to anticipate the instability of the river flow. The aim of this research was to determine the best ARIMA model based on analysis of the river flow of Karang Mumus, Samarinda. This research will explain the procedure of ARIMA model building using the Least Square and Goal Programming to predict the river flow of Karang Mumus, Samarinda. The data used montly from January until December. The model of ARIMA (2,1,2)to predict the river flow of Karang Mumus using Goal Programming is : Zt=μ-0,0492Zt-1-0,0523Zt-2-0,9969Zt-3+0,9247at-1+0,9339at-2+at ARIMA (2,1,2) for river flow forecasting using Goal Programming is : Zt=1,17Zt-1-0,17Zt-2+at+0,31at-1 The best ARIMA model for river flow forecasting of Karang Mumus is ARIMA (2,1,2) using Least Square method. Result for river flow forecasting of Karang Mumus river in Samarinda from January until Desember 2015 are 1.733 m3, 1.729 m3, 1.730 m3, 1.730 m3, 1.729 m3, 1.730 m3, 1.732 m3, 1.729 m3, 1.730 m3, 1.732 m3, 1.729 m3, dan 1.730 m3.
Metode Regresi Robust Dengan Estimasi Method of Moment (Estimasi-MM) Pada Regresi Linier Berganda Hisintus Suban Hurint; Ika Purnamasari; Memi Nor Hayati
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Method of Ordinary Least Square (OLS) on the regression analysis is a method which is often used to estimate the parameters. In the OLS method, there are several assumptions that must be fulfilled, these assumptions are often not fulfilled when the data contains outlier, so need a method that are robust to the presence of outliers. In this research, studied method of robust regression with MM-estimation. MM-estimation is a combination of estimation methods that have a high breakdown point, namely the Scale estimation(S-estimation) and Least Trimmed Square estimation (LTS estimation) and the method that have higher efficiency point, namely the Maximum Likelihood Type estimation (M-estimation). The first step in the MM-estimation is to find the S-estimator, then set the parameter regression using the M-estimation. The purpose of this study was to determine the effect of price index of foodstuffs ( ), the price index of education ), and the price index of health ) to the CPI for the province of east borneo, where the CPI data contains outliers, namely observation to 13, 31,and 32.
Penggunaan Metode Nonparametrik Untuk Membandingkan Fungsi Survival Pada Uji Gehan, Cox Mantel, Logrank, Dan Cox F Fitriani Fitriani; Sri Wahyuningsih; Yuki Novia Nasution
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Survival analysis is a statistical method that aims to study and model the relationship between risk factors and the study time students to reach graduation. In this study conducted a survival analysis using a nonparametric method. They are Gehan Test, Cox Mantel Test, Logrank Test, and Cox F Test on data of students of Mulawarman University Faculty of Mathematical and Natural Science majoring in Statistics and majoring in Computer Science 2010. The purpose of this research was to compare the of period of study survival function students majoring in Statistics and majoring in Computer Science . This study was conducted using data of 167 students majoring in Statistics and majoring in Computer Science. The results showed that students of majoring in Computer Science longer in studying compared with students majoring in Statistics. For students majoring in Statistics who participated in the selection to go to college through the SBMPTN and SMMPTN study longer than SNMPT. While those who while majoring in Computer Sciences who participated in the selection to go to college through three pathways had the same study time.
Pemodelan Status Kesehatan Pasien Medical Check Up Klinik Handil Muara Jawa Dengan Regresi Logistik Biner Rakhmanto Anugrah Darmawan; Darnah Andi Nohe; Desi Yuniarti
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Health is a major human needs are also priorities in human life. many types of health providers available to the community as an example of health care clinics were organized promotive, preventive, curative and rehabilitative. Handil clinics serve patients Muara Jawa Medical Check-Up for the workers of the company or the public. To analyze the factors that affect the health of a patient Medical Check Up can use logistic regression analysis. Logistic regression analysis is an analysis that describes the relationship between the response variable is binary with explanatory variables that can be either qualitative or quantitative variables variables. Based on the research results, we concluded that of the testing parameters, only gender and companies that significantly affect the patient's health status.
Bootstrap Aggregating Multivariate Adaptive Regression Splines Marisa Nanda Rahmaniah; Yuki Novia Nasution; Ika Purnamasari
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

MARS is one of the classification methods that focus on the high dimension and discontinuity of the data. The level of accuracy in MARS can be improved by using Bagging method (Bootstrap Agregating). This method is used to improve stability, accuracy and strength’s of prediction. This study discusses the MARS bagging applications in analyzing the issue of accreditation, which the accreditation level of a schools can be predicted based on the identifier components. Therefore, in this study will be identified these components to create a classification model. The data used is the accreditation data of the primary school in East Kalimantan Province 2015 issued by the Accreditation Board of the Provincial Schools (BAP-S/M) of East Kalimantan Province. This study obtained six components that affect the determination of the accreditation of schools at primary school level. The components are the variables that contribute to the classification. The variables are a standard component of content (X1), a standard component of the process (X2), a standard component of graduates (X3), standard components of teachers and staffs (X4), a standard component of infrastructure (X5) and standard component of financial (X7). Based on the result of the classification accuracy of MARS method (using Apparent Error Rate (APER), it is amounted to 78.87%, while the classification accuracy (using APER) with method of bagging of the best MARS models amounted to 89.44%. This means that the method of bagging MARS gives better classification accuracy of the classification than MARS.
Model Regresi Logistik Spasial Tiara Nurul Ma’ala; Desi Yuniarti; Memi Nor Hayati
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Logistic regression modeling procedure is applied to model the response variable (Y) which is based on one or more categorical explanatory variable (X) which is categorical or continuous. In the application of logistic regression is often found that there are spatial influences that affect the model. The existence of spatial relationships between regions that cause necessary to accommodate the spatial diversity into the model, so that the analysis used logistic regression spatial. First law of geography says that everything is related to everything else, but near things are more related than distant things. Then, when a region becomes a major cause of the spread of a disease is suspected, the region will provide the spread of a disease to the new area adjacent to it. The way to find out the adjacent area with the same characteristics can be done with spatial logistic regression method.The spread of TB disease in Samarinda City is quite high. TB is a chronical disease which has been known by the public and feared of its infection. This study’s aim is to determine the appropriate model to estimate the spread of TB disease. From this model it is known that the factors that influence the number of people with TB disease in every village in Samarinda City in the year 2013 are the number of primary school in every village and the spatial effect. This means that there is the influence of spatial factors to the spread of TB disease in every village in Samarinda City in the Year 2013.
Penerapan Metode Projected Unit Credit dan Entry Age Normal pada Asuransi Dana Pensiun Bayu Nanda Permana; Yuki Purnamasari; Ika Purnamasari
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Actuarial calculation method in pension funding is divided into two major categories, namely Accrued Benefit Cost Method and Projected Benefit Cost Method. One example method which is included in Accrued Benefit Cost Method is the Projected Unit Credit Method, and one of the method which is included in Projected Benefit Cost Method is the Entry Age Normal Method. Both methods are used to determine the amount of normal cost and actuarial liabilitiy which are the basis in determining pension benefits. The purpose of this study was to compare the value of normal cost and actuarial liabilities of the two methods. The data used in this research is the employee data from PT. INHUTANI I Berau Branch. The result showed that normal cost using Projected Unit Credit method continued increases with the salary received, meanwhile if using the Entry Age Normal method the amount of normal cost is same for each year to an employee. On the other hand, actuarial liability using Projected Unit Credit Method is smaller than using Entry Age Normal for each employee in each year.
Model Dinamis: Autoregressive Dan Distribusi Lag Muhajir Choir Nurahman; Sri Wahyuningsih; Desi Yuniarti
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Regression model using time series data not only use the effect of changing the independent variables on the dependent variable in the same period and for the same period of observation, but also use the period of time before. The purpose of this study was to determine the dynamic model autoregressive and distribution lag by type of infinite lag, and to know the effect of US dollar exchange rate against GDP in 1993-2013. Based on the analysis of data has that GDP and US dollar exchange rate has a rising trend pattern, and obtained by a simple regression model. But this model can not be used because of two assumptions have not been met and that there are heteroscedasticity and autocorrelation. So this model should be transformed using log, and log transformation model is obtained from a simple regression. The transportation model can be used as desiredint his model is only one assumption are not met and that there are autocorrelation. Then sub sequently estimating models and obtained Koyckas well as all assumptions are met, namely residual normal distribution, no problem heteroscedasticity and autocorrelation. Thus, the obtained dynamic distribution models also lag within finite lag types.
Perbandingan Hasil Klasifikasi Menggunakan Regresi logistik dan Analisis Diskriminan Kuadratik Pada Kasus Pengklasifikasian Jurusan Di SMA Negeri 8 Samarinda Tahun Ajaran 2014/2015 Cristine Uli Artha; Yuki Novia Nasution; Ika Purnamasari
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Logistic Regression Analysis and Discriminant Analysis represent the statistical method for the classification of a number of object. In the case of classification especially if there's only two response categories, logistic regression is used more precisely if the assumption of multivariate normality in data cannot be fullfiled. The assumption of normality multivariate distribution and equality of variance covariance matrices represent the important matter in discriminant analysis for getting of high accuracy of classification. Discriminant analysis method that is used in inequality of variance covariance matrices is called quadratic discriminant analysis. The purpose of this study was to determine the classification results by using Logistic Regression and Quadratic Discriminant Analysis and compares the classification accuracy. The data that is used in the study is the average raport of the first and second semester of the class X at SMA Negeri 8 Samarinda academic year 2014/2015. Data consists of 190 students with two independent variables and four dependent variables. Based on research results, obtained results for the value of class accuracy is Logistic Regression 83.16% and Quadratic Discriminant Analysis 84.21%.

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