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Analisis Faktor Konfirmatori untuk Mengetahui Faktor-Faktor yang Mempengaruhi Prestasi Mahasiswa Program Studi Statistika FMIPA Universitas Mulawarman Andini Juita Sari; Desi Yuniarti; Sri Wahyuningsih
EKSPONENSIAL Vol 8 No 1 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Confirmatory factor analysis is one part of the multivariate analysis. In this study conducted a confirmatory factor analysis of statistics student of Mulawarman University in 2013, 2014, and 2015 of 159 with research aims to determine the factors affecting the achievement of students. The analysis showed that, is influenced by four latent variables are latent variables background (ξ1) with three indicator variables of the relation with family (X1), parental (X2), and the motivation of the family (X3). Latent variables learning environment outside the campus (ξ2) with two indicator variables are the concentrations studied (X6) and the completion of tasks (X7). Latent variables campus facilities (ξ3) with indicator variables study room (X8), reading room of statistics (X9), wifi (X10), and computer facilities laboratory (X11). Latent variable students perceptions of lecturers (ξ4) with two indicator variables the learning system of lecturers (X14) and system administration duties of lecturers (X15). Indicator variables give large contribute affect to student achievement is the completion of the task (X7) rated loading factor of 0.89.

Pemantauan Peramalan Akseptor KB Baru Provinsi Kalimantan Timur Menggunakan Simple Moving Average dan Weighted Moving Average dengan Metode Tracking Signal Eric Sapto Raharjo; Memi Nor Hayati; Sri Wahyuningsih
EKSPONENSIAL Vol 7 No 1 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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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.

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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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.

Peramalan Harga Minyak Mentah Dunia (Crude Oil) Menggunakan Metode Radial Basis Function Neural Network (RBFNN) Ayu Wulandari; Sri Wahyuningsih; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 8 No 2 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Forecasting is a technique to estimate a value in the future with past data and current data. One of the forecasting method that includes neural network is Radial Basis Function Neural Network (RBFNN). In this research, RBFNN method is used to get the best model and to forecast world crude oil price (US$) data. World crude oil prices forecasting is very important for many stakeholder, both from the government sector, business entities and investors so that all activities can go according to plan. In the RBFNN method, the network input and the number of hidden layers is very influential to get the best model from RBFNN and also the forecasting. To get the best model by using network input determination by identifying the Partial Autocorrelation Function (PACF) lag, and to determine the number of hidden layers by the K-Means cluster method. Results of the research showed that from the training data, the best model of RBFNN is using 2 network inputs Xt−1 and Xt−2 and 3 hidden layers with Mean Absolute Percentage Error (MAPE) accuracy level is 6,8150%. With the model, for the next period from June 2017 to December 2017 the world crude oil price (US $) shows a downward trend.

Aplikasi Classification and Regression Tree (CART) dan Regresi Logistik Ordinal dalam Bidang Pendididikan David Siahaan; Sri Wahyuningsih; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 7 No 1 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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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%

Analisis Autokorelasi Spasialtitik Panas Di Kalimantan Timur Menggunakan Indeks Moran dan Local Indicator Of Spatial Autocorrelation (LISA) Nurmalia Purwita Yuriantari; Memi Nor Hayati; Sri Wahyuningsih
EKSPONENSIAL Vol 8 No 1 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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In the last few decades has developed statistical methods relating to spatial science, is the spatial statistics. Spatial Statistics aims to analyze spatial data. The case studies in this study was the amount of hotspots in East Kalimantan by Regency/City in years 2014-2016. This study aimed to analyze the existence of spatial autocorrelation in the data the amount of hotspots as well as determine the level of vulnerability to potential areas of forest and land fires in East Kalimantan by Regency/City in 2014-2016. The method used to analyze the global spatial autocorrelation is the Moran Index method and Local Indicators of Spatial Autocorrelation (LISA) for analyze spatialautocorrelation locally. The results of the analysis of global spatial autocorrelation using the Moran index with α = 20% showed there spatial autocorrelation amount of hotspots in East Kalimantan in 2014, 2015, and 2016. Meanwhile, the analysis results locally using LISA showed that there spatial autocorrelation in several Regency/City in East Kalimantan in 2014, 2015 and 2016. The analysis results Regency/City that belong to the vulnerable category of forest and land fires is Bontang City, Kutai Barat Regency, Kutai Kartanegara Regency, Mahakam Ulu Regency, dan Penajam Paser Utara Regency and Samarinda City.

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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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.

Analisis Data Kejadian Berulang Tidak Identik Dengan Cox Gap TimeModel Andi Widya Rhezky Awalul Aziz; Yuki Novia Nasution; Sri Wahyuningsih
EKSPONENSIAL Vol 8 No 2 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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The gap time method is a method that can be used in recurrent event Time-based modelling. Gap analysis is often useful when events are relatively uncommon, when the object of the study is the prediction of time for the next event, or on the phenomenon of circulation.The analysis of model for non-identical recurrent events using survival time in the form of gap time is called Cox Gap Time Model. The purpose of this research is to know Cox Gap Time model for recurrent occurrence in DM type II disease and to know the factors that influence repetitive incident in DM type II disease in RSUD A. W. Sjahranie Samarinda. The variables in this research are age, treatment, status and relapse time (gap time). The study was conducted by using 263 medical records data of DM type II patients admitted to the hospital during observation period in January 2015 until December 2016. The results shows that age factor affects the first gap time and there are age, gap 1 covariate and gap 2 covariate that have significant effect aga inst to the third gap time variable, meanwhile there is no variable affects the second gap time.

Perbandingan Metode C-Means dan Fuzzy C-Means Dalam Pengelompokkan Wilayah Desa/Kelurahan di Kabupaten Kutai Kartanegara Nissa Irabawati; Sri Wahyuningsih; Rudy Ramadani Syoer
EKSPONENSIAL Vol 7 No 1 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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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.

Penerapan Metode ARIMA Ensembel pada Peramalan Hasniah Hasniah; Sri Wahyuningsih; Desi Yuniarti
EKSPONENSIAL Vol 7 No 1 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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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%.

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