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<![CDATA[Pemantauan Peramalan Akseptor KB Baru Provinsi Kalimantan Timur Menggunakan Simple Moving Average dan Weighted Moving Average dengan Metode Tracking Signal ]]>
<![CDATA[Eric Sapto Raharjo]]>;
<![CDATA[Memi Nor Hayati]]>;
<![CDATA[Sri Wahyuningsih]]>
<![CDATA[ EKSPONENSIAL Vol 7 No 1 (2016) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[Simple moving average (SMA) is the basic method used to measure seasonal variations. This method is done by moving the average value counted along the time series. Weighted moving average (WMA) includes selecting weights may be different for each data value and then calculating the weighted average time period of k, the value obtained as the smoothed value.The purpose of this study was to determine the method and the best forecasting model with the results of forecasting on new data on the number of new acceptors KB using tracking signal. Results of this study is to model 3 SMA method is the best monthly tracking signal with a value of -0.0349 to -0.0178 β = 0.1 and β = 0.2 for the forecasting results for the period of January, February, and March 2015 amounted to 8.151, 8.131, and 7.485. For model 3 monthly WMA method is best with a variety of weights W1 = 0.25; W2 = 0.35; W3 = 0,40 tracking signal has a value of -0.0451 to -0.0439 β = 0.1 and β = 0.2 for the forecasting results for the period of January, February, and March 2015 for 8.044, 7.893, and 7.517 , In this case the method of 3-month SMA model is the most appropriate method to forecast the number of new acceptors KB East Kalimantan province.]]>
<![CDATA[Penerapan Generalized Poisson Regression I Untuk Mengatasi Overdispersi Pada Regresi Poisson ]]>
<![CDATA[Iim Masfian Nur]]>;
<![CDATA[Desi Yuniarti]]>;
<![CDATA[Memi Nor Hayati]]>
<![CDATA[ EKSPONENSIAL Vol 7 No 1 (2016) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[Poisson Regression model is commonly used to analyze count data is assumed to have Poisson distribution where the mean and variance values are equal or also called equdispersion. In fact, this assumption is often violated, because the value of variance is greater than the mean value, this condition is called overdispersion. Poisson regression which is applied to the data that contains overdispersion will imply the value of standard error becomes underestimates, so the conclusion is not valid. One of the models that can be used for overdispersion data is Generalized Poisson Regression I (GPR I). This research discuss the handling of overdispersion on Poisson regression using GPR I, with case study modeling the number of cervical cancer cases in East Kalimantan in 2013. In this research GPR I models meet the criteria for suitability of regression compared Poisson regression models because it has a smaller AIC value.]]>
<![CDATA[Aplikasi Classification and Regression Tree (CART) dan Regresi Logistik Ordinal dalam Bidang Pendididikan ]]>
<![CDATA[David Siahaan]]>;
<![CDATA[Sri Wahyuningsih]]>;
<![CDATA[Fidia Deny Tisna Amijaya]]>
<![CDATA[ EKSPONENSIAL Vol 7 No 1 (2016) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[CART method is a nonparametric statistical methods which is for obtaining accurate data group in the classification analysis. CART main goal is to get an accurate data as a group identifier of a classification. CART can be applied in three main steps, namely the establishment of a classification tree, trimming the classification tree, and determination of optimal classification tree. Ordinal logistic regression is a statistical method for analysis response variables that have an ordinal scale consisting of three or more categories. Predictor variables that can be included in the model can be either continuous or categorical data consisting of two or more variables. This study wanted to know the classification results FMIPA UNMUL predicate graduation, the main factor that affect the predicate graduation FMIPA UNMUL who graduated in 2014, and a comparison of the accuracy of the classification results between CART and ordinal logistic regression. The results showed that gender (X1), region origin (X2), major (X3), the status of secondary school (X4), and duration of the study period (X5) is the primary identifier graduation predicate FMIPA UNMUL, whereas gender (X1 ) and duration of the study period (X5) is a factor that affects the predicate graduation. Ordinal logistic regression model was able to predict with 65% accuracy, while the CART method has a predictive accuracy of 54.9%]]>
<![CDATA[Peramalan Jumlah Penduduk Kota Samarinda Dengan Menggunakan Metode Pemulusan Eksponensial Ganda dan Tripel Dari Brown ]]>
<![CDATA[Reyham Nopriadi Gurianto]]>;
<![CDATA[Ika Purnamasari]]>;
<![CDATA[Desi Yuniarti]]>
<![CDATA[ EKSPONENSIAL Vol 7 No 1 (2016) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[Forecasting is a process or method to predict an event that will occur in the future. Exponential smoothing is a method of moving average forecasting that conduct weighting decreases exponentially toward the value of the older observations. In this study discusses the Brown’s double exponential smoothing and Brown’s triple exponential smoothing method in predicting the population of the city of Samarinda in 2014, 2015 and 2016 are very necessary for the government to determine the population of the city of Samarinda. Double exponential smoothing method and triple from Brown is a method of extrapolation or by using a time series of past history in making the forecast for the future which used as a guide in decision-making processes. Results obtained using the method of Brown's double exponential smoothing using the parameter alpha of 0,52 was obtained that the forecast of total population in 2014 was 843.653 residents, in 2015 was 898.647 residents, and in 2016 was 944.716 residents with an average value deviation absolute (MAD) is 12.937 and the average -rata absolute percentage error (MAPE) is 2,4548. In the triple exponential smoothing method Brown’s using parameters alpha 0,4 obtained results forecast the total population in 2014 was 854.766 residents, in 2015 was 898.647 residents, and in 2016 was 944.716 residents with an average value deviation absolute (MAD) is 14.709 and the average percentage The absolute error (MAPE) is 2,7589.]]>
<![CDATA[Perbandingan Metode C-Means dan Fuzzy C-Means Dalam Pengelompokkan Wilayah Desa/Kelurahan di Kabupaten Kutai Kartanegara ]]>
<![CDATA[Nissa Irabawati]]>;
<![CDATA[Sri Wahyuningsih]]>;
<![CDATA[Rudy Ramadani Syoer]]>
<![CDATA[ EKSPONENSIAL Vol 7 No 1 (2016) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[Cluster analysis is a multivariate statistical technique that has the main purpose to classify objects based on common characteristics. With this analysis, the object will be grouped such that each object is the closest similarity to other objects are in the same group. In the clustering process by using no hierarchical C-Means formation of partition is done such that each object explicitly declared as a member of one group and not a member of any other group. But sometimes can not put an object just in one partition, because in fact the object is located between two or more other partitions, so it needs to be weighted based on its fuzzy membership level. In this way, it is to define a method in the formation of the group will be more flexible. The concept is called fuzzy clustering, the fuzzy way each object can be members of multiple groups. The difference lies in the assumptions used as a basis for allocation. One technique that is not part of the method of using the hierarchical nature of fuzzy clustering technique is using Fuzzy C-Means (FCM). This study will examines comparative method C-Means and FCM clustering in a case study, namely the grouping of the village/urban village in Kutai Kartanegara regency based on the characteristics of facilities/infrastructure and socio-economic factors of the population. The results showed that in some respects, FCM was superior than the C-Means, especially in generating the minimum of objective function, the computation time and ratio value Sw and Sb. Based on the similarity matrix eigen value and the index value Xie and Beni (XB) concluded that the most optimal number of groups is 5 (five) groups.]]>
<![CDATA[Peramalan Dengan Menggunakan Metode Double Exponential Smoothing Dari Brown ]]>
<![CDATA[Etri Pujiati]]>;
<![CDATA[Desi Yuniarti]]>;
<![CDATA[Rito Goejantoro]]>
<![CDATA[ EKSPONENSIAL Vol 7 No 1 (2016) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[Consumer Price Index (CPI) is one of the economic indicator that givethe information about the price of goods andservices which paid by consumer. CPI in Samarinda City increases so long which the pattern of the data is indicating a trend pattern. Time series forecasting designed to handle the trend of data which used a double exponential smoothing method. The purpose of this study is to determine the using of the parameters α and the forecasting amount of CPI in Samarinda City for three months that use double exponential smoothing method. The best parameter α which use to forecast CPI in Samarinda City is (0,61). To forecast CPI in Samarinda City is using double exponential smoothing method obtained F72+m=119,83+1,62 m. The forecasting result of CPI in Samarinda City from January to March 2015 are 121,44, 123,06, and 124,68.]]>
<![CDATA[Penerapan Statistika Nonparametrik dengan Metode Brown-Mood pada Regresi Linier Berganda ]]>
<![CDATA[Ni Wayan Rica A]]>;
<![CDATA[Darnah Andi Nohe]]>;
<![CDATA[Rito Goejantoro]]>
<![CDATA[ EKSPONENSIAL Vol 7 No 1 (2016) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[Brown-Mood is a method first developed by GW brown 1950 and AM mood in 1951 with the purpose of the parameters of the multiple linear regression model of the linear regression model of the equation of the median small sample size. This study discusse the application of the method of brown-mood on multiple linear regression with the open unemployment rate (X1), and growth rate of gross regional domestic product at constant prices (X2) to the number of poor population (Y) Province of east Kalimantan. If the method ordinary least square in a multiple linear regression is a statistical parametric aims to minimize the average (mean) error, the brown-mood methods as a nonparametric statistical method chose a multiple linear regression model by minimising the median and average weighted. The results of this research to get a linear regression model using the method of brown-mood is Ŷ=-31.11+1.74 X1 + 1.44 X2 from the multiple linear regression model obtained are percentage distribution of gross regional domestic product at current prices [without oil, gas and its products] and growth rate of gross regional domestic product at constant prices affect to the number of poor population.]]>
<![CDATA[Penerapan Metode ARIMA Ensembel pada Peramalan ]]>
<![CDATA[Hasniah Hasniah]]>;
<![CDATA[Sri Wahyuningsih]]>;
<![CDATA[Desi Yuniarti]]>
<![CDATA[ EKSPONENSIAL Vol 7 No 1 (2016) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[ARIMA ensemble is a method of combination forecast results from multiple ARIMA models. ARIMA method as individuals and ARIMA ensemble as a combination model to forecasting of national inflation in Indonesia. Ensemble method used to combine the forecast result in this study were averaging and stacking. The data used in this study is the nasional monthly inflation of Indonesian from January 2010 to December 2014. The results showed that for forecasting the next twelve months ensemble averaging method produces the smalles RMSE values and obtained models equation where zt(1) is ARIMA models (2,0,2) and zt2 is ARIMA models (2,0,3). Based on ARIMA ensemble averaging model the monthly inflation forecasting national Indonesia next twelve months forwards experience of fluctuation where highest inflation in January 2015, that is 1,13% and smallest in March 2015, that is equal to -0,13%.]]>
<![CDATA[Analisis Survival Lama Masa Pengobatan Dan Tingkat Kesembuhan Pasien Narkoba Di Lembaga Terapi Dan Rehabilitasi Pondok Pesantren Ibadurrahman Tenggarong Seberang ]]>
<![CDATA[Fathur Rachman]]>;
<![CDATA[Sri Wahyuningsih]]>;
<![CDATA[Yuki Novia Nasution]]>
<![CDATA[ EKSPONENSIAL Vol 7 No 1 (2016) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[Survival analysis is used to analyze of the long life data, in general this method used to estimate and the time curve survival which is Life Table Method, Model of Cox Proportional Hazard or the Cox model and Product Limit Method (Kaplan Meier). This script well knowing about the model of Cox Proportional Hazard for the influencing factors in the recovery term of the Narcotics Patients in the Institution of Therapy and Rehabilitation Pondok Pesantren Ibadurrahman Tenggarong Seberang and knowing of the influencing factors in the recovery term of the Narcotics Patients in the Institution of Therapy and Rehabilitation Pondok Pesantren Ibadurrahman Tenggarong Seberang. The research data is done for 114 of Narcotics Patients. The Procedural in making Cox Proportional Hazard model including to several parts, they are deciding of variables which used to, assumption exam of Cox Proportional Hazard model, choosing the best model with backward exam, deciding variable which influenced of the cure rates duration. The usage data are forming by 5 variables, such as Gender, Education, The use of Smoking, Ages, and Parenting, based on the research was found the model of Cox Proportional Hazard for the influence factors in hi curing is: hi(t,x)=exp(-0.694 x4) h0(t). The influence factors in curing of the Narcotics Patients are the age of the patient since the therapy.]]>
<![CDATA[Perbandingan Hasil Analisis Cluster dengan Menggunakan Metode Single Linkage dan Metode C-Means ]]>
<![CDATA[Maria Goreti]]>;
<![CDATA[Yuki Novia Nasution]]>;
<![CDATA[Sri Wahyuningsih]]>
<![CDATA[ EKSPONENSIAL Vol 7 No 1 (2016) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[Cluster analysis is one of the multivariate analysis which is used to classify objects into groups based on similarity of observed variables, in order to obtain the similarity of objects in the same group compared between objects of different groups. Cluster analysis is divided into two methods, they are is hierarchy method that start grouping with two or more objects that have the closest similarity and non-hierarchical method that begin with the process of determining the number of clusters in advance. This study aims is to determine whether there are differences in the results of the cluster grouping formed by using the hierarchy method, that is single linkage method, and non-hierarchical method, that is C-means method. Data, which is taken from the Environment Agency West Kutai, is data Ambient Air Quality Levels in Plantation Company in West Kutai in 2014. The results showed that based on the type of pollutants from all aleven the eleventh plantation companies have different results clusters formed from both methods which were used. With the characteristics of each cluster or groups: single linkage method for the first cluster has good air quality and its members as much as 7 companies, second Cluster both have poor air quality and its members as much as two companies and for the third Cluster have fairly good air quality and its members as much as 2 companies. As for the method of C-means for the first cluster has good air quality and its members as many as four companies, second Cluster both have poor air quality and its members as many as four companies and third Cluster have fairly good air quality and its members as much as 3 companies. For the average value of the ratio of standard deviation in the group (Sw) and between groups (Sb) by using the method of single linkage has a smaller value that is equal to 0.022 while C-means method is equal to 0.063. Thus, in the case of the classification of the ambient air quality in plantation companies in West Kutai 2014, single linkage method better at classifying than C-means method.]]>
