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<![CDATA[Model Spatial Autoregressive Moving Average (SARMA) pada Data Jumlah Kejadian Demam Berdarah Dengue (DBD) di Provinsi Kalimantan Timur dan Tengah Tahun 2016 ]]>
<![CDATA[Devi Nur Endah Sari]]>;
<![CDATA[Memi Nor Hayati]]>;
<![CDATA[Sri Wahyuningsih]]>
<![CDATA[ EKSPONENSIAL Vol 11 No 1 (2020) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[Spatial Autoregressive Moving Average (SARMA) is a spatial regression model that uses the regional approach. The weighting matrix used is an adjacency matrix which is based on the intersection between observed locations. This study was conducted to determine the SARMA model and the factors that influence the number of cases of dengue hemorrhagic fever (DHF) in the provinces of East Kalimantan and Central Kalimantan in 2016. Based on the results of the Moran's Index test, there is a spatial autocorrelation on the number of dengue events in East Kalimantan Province and Central Kalimantan in 2016. The Lagrange Multiplier (LM) test has a spatial lag on the dependent variable and the error variable, which is a parameter and that is significant to the significance level . Based on the results of SARMA modeling that the factors that influence the number of dengue events in the provinces of East Kalimantan and Central Kalimantan in 2016 are the percentage of population density, the percentage of healthy houses, and the percentage of puskesmas.]]>
<![CDATA[Pemodelan Generalized Space Time Autoregressive (GSTAR) Pada Data Inflasi di Kota Samarinda dan Kota Balikpapan ]]>
<![CDATA[Riska Handayani]]>;
<![CDATA[Sri Wahyuningsih]]>;
<![CDATA[Desi Yuniarti]]>
<![CDATA[ EKSPONENSIAL Vol 9 No 2 (2018) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[One of the macroeconomic indicators used in the preparation of government’s economicpolicy is inflation. Inflation is a data time series monthly that also is influenced by location effects. Generalized Space Time Autoregressive (GSTAR) is a time series methode that combines time and location effects. The case study is applied of GSTAR for forecasting inflation in two cities in East Kalimantan namely Samarinda and Balikpapan. This research aims to implement GSTAR model to gain forecasting model for inflation data in Samarinda city and Balikpapan city by using method of cross-correlation normalization. The resulting model is GSTAR model GSTAR (2,1) and GSTAR (3,1). The model obtained is not feasible to be used for forecasting, because it does not meet the white noise assumption.]]>
<![CDATA[Penerapan Diagram Kontrol Multivariate Exponentially Weighted Moving Variance (MEWMV) ]]>
<![CDATA[Agustina Feni Baransano]]>;
<![CDATA[Sri Wahyuningsih]]>;
<![CDATA[Yuki Novia Nasution]]>
<![CDATA[ EKSPONENSIAL Vol 9 No 2 (2018) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[Statistical Process Control based on the quality characteristics can be divided into two kinds, namely univariate control chart and multivariate control charts.This study usedMultivariate Exponentially Weighted MovingVariance Control Chart (MEWMV).PDAM Tirta Mahakam in the districts of Kutai Kartanegara is one of the regional companies engaged in the production of drinking water, which is located in Tenggarong, East Kalimantan.In the production process, PDAM Tirta Mahakam always refers to the standard which is set by the government in producing drinking water.The purpose of this study was to determine whetherwater quality characteristics of PDAM Tirta Mahakam in a controlledstate or not by using control charts MEWMV,and to know the the water process capability. From the result of research it can be concluded that by using MEWMV control chart with weight , , and , show that the condition has been statistically in control. Process capability index MCpin multivariateexplains that the process has not been capable in precision with a value of 0,896 or not meet the specifications of the company.]]>
<![CDATA[Optimasi Klasifikasi Batubara Berdasarkan Jenis Kalori dengan menggunakan Genetic Modified K-Nearest Neighbor (GMK-NN) ]]>
<![CDATA[Nanang Wahyudi]]>;
<![CDATA[Sri Wahyuningsih]]>;
<![CDATA[Fidia Deny Tisna Amijaya]]>
<![CDATA[ EKSPONENSIAL Vol 10 No 2 (2019) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[The K-Nearest Neighbor (K-NN) method is one of the oldest and most popular Nearest Neighbor-based methods. The researchers developed several methods to improve the performance of the K-NN algorithm by using the Genetic Modified K-Nearest Neighbor (GMK-NN) algorithm. This method combines the genetic algorithm and the K-NN algorithm in determining the optimal K value used in the classification prediction. The GMK-NN algorithm will greatly facilitate the examination of coal classification in the laboratory without having to do a lot of chemical and physics testing that takes a long time only with the data already available. In this research, K value optimization is done to predict the classification of coal based on calories owned by PT Jasa Mutu Mineral Indonesia in 2017. Based on the research, using the proportion of training and testing data 90:10, 80:20 and 70:30 obtained the value of K the most optimal is at K = 1. The highest prediction accuracy was obtained by using 90:10 proportion data which is 100%, then with the proportion of 80:20 data obtained prediction accuracy of 91.67% and with the proportion of 70:30 data obtained prediction accuracy of 94.44%.]]>
<![CDATA[Peramalan Produksi Kelapa Sawit Menggunakan Metode Pegel’s Exponential Smoothing ]]>
<![CDATA[Yetty Veronica Lestari Sinaga]]>;
<![CDATA[Sri Wahyuningsih]]>;
<![CDATA[Meiliyani Siringoringo]]>
<![CDATA[ EKSPONENSIAL Vol 12 No 2 (2021) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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DOI: 10.30872/eksponensial.v12i2.810
<![CDATA[Time series data analysis using Pegel's exponential smoothing method are an analysis of time series that is influenced by trend and seasonal data patterns. The data used in this study was oil palm production in East Kalimantan Province from January 2014 until December 2018. This study aims to predict oil palm production for January, February, March in 2019. Forecasting results were verified based on the MAPE value and monitoring signal tracking method. The results showed that in the Pegel method, the exponential smoothing model without a multiplicative seasonal trend with a MAPE value of 7.84% had better forecasting accuracy than the other methods. The forecast results of the Pegel's exponential smoothing method without a multiplicative seasonal trend can be used to predict the next 3 periods, namely January, February and March 2019. The forecast results for the next 3 periods have increased in succession.]]>
<![CDATA[Pengelompokkan Data Runtun Waktu menggunakan Analisis Cluster ]]>
<![CDATA[Andrea Tri Rian Dani]]>;
<![CDATA[Sri Wahyuningsih]]>;
<![CDATA[Nanda Arista Rizki]]>
<![CDATA[ EKSPONENSIAL Vol 11 No 1 (2020) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[The export value of East Kalimantan Province has big data conditions with time series and multivariable data types. Cluster analysis can be applied to time series data, where there are different procedures and grouping algorithms compared to grouping cross section data. Algorithms and procedures in the cluster formation process are done differently, because time series data is a series of observational data that occur based on a time index in sequence with a fixed time interval. The purpose of this research is to obtain the best similarity measurement using the cophenetic correlation coefficient and get the optimal c-value using the silhouete coefficient. In this study, the grouping algorithm used is a single linkage with four measurements of similarity, namely the Pearson correlation distance, euclidean, dynamic time warping and autocorrelation based distance. The sample in this study is the data on the export value of oil and non-oil commodities in East Kalimantan Province from January 2000 to December 2016 consisting of 10 variables. Based on the results of the analysis, the distance of the best similarity measurement in clustering the export value of oil and non-oil commodities in East Kalimantan Province is the dynamic time warping distance with the optimal c-value of 3 clusters.]]>
<![CDATA[Analisis Model Threshold Generalized Autoregressive Conditional Heteroskedasticity (TGARCH) dan Model Exponential Generalized AutoregressiveConditional Heteroskedasticity (EGARCH) ]]>
<![CDATA[Julia Julia]]>;
<![CDATA[Sri Wahyuningsih]]>;
<![CDATA[Memi Nor Hayati]]>
<![CDATA[ EKSPONENSIAL Vol 9 No 2 (2018) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[In the field of finance, Autoregressive Integrated Moving Average (ARIMA) is one of the models that can be used. Financial data usually have a non constant variance error. Thus, Autoregressive Conditional Heterokedasticity (ARCH )model can be used to solve the problem. In addition, it also can be used the development of ARCH model that is Generalized Autoregressive Conditional Heterkadasticity (GARCH) model. The symmetry of residual data can be determined by using the model of Threshold Generalized Autoregressive Conditional Heterkadasticity (TGARCH) and the model of Exponential Generalized Autoregressive Conditional Heterkadasticity (EGARCH). The purpose of this research is to know the best model among the model of TGARCH and the model of EGARCH in predicting Indonesia Composite Index (ICI) and the results of ICI forecasting by using the best model for the period of July 2017 until December 2017. The best model in the ICI case study from January 2011 to June 2017 is the model of ARIMA (1,1,1) -GARCH (1,2) -EGARCH (1). The results of ICI forecasting by using the model of ARIMA (1,1,1) -GARCH (1,2) -EGARCH (1) obtained an upward trend in the period of July 2017 to December 2017.]]>
<![CDATA[Peramalan Jumlah Titik Panas Provinsi Kalimantan Timur Menggunakan Analisis Intervensi Fungsi Pulse ]]>
<![CDATA[Ahmad Ronaldy Saputra]]>;
<![CDATA[Sri Wahyuningsih]]>;
<![CDATA[Meiliyani Siringoringo]]>
<![CDATA[ EKSPONENSIAL Vol 12 No 1 (2021) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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DOI: 10.30872/eksponensial.v12i1.766
<![CDATA[Intervention analysis is a time series analysis that used to explain the influence of intervention caused by external and internal factors. As for the number of hotspot in East Borneo which was increased in 2015. The purpose of this study was to determine the best intervention model for forecasting the number of hotspots in East Borneo. In the initial stage of the intervention analysis is to divide the data into 2 parts, namely data before the intervention and data after the intervention occurred. The results of the analysis obtained the best model for the data before the intervention occurred were SARIMA (0,1,1)(0,1,1)12. The next step was identifying the intervention function by observing the residual graph of the SARIMA model and obtained the order b = 0, s = 0 and r = 0 with the AIC value of the intervention model of -143,16. Furthermore, based on the intervention model obtained forecasting results is increased from July to September 2019. The number of hotspots with the highest number of hotspots occurring on September 2019 with 249 hotspots. Then decreasing on October 2019 to 183 hotspots. On November 2019 it dropped significantly to 13 hotspots.]]>
<![CDATA[Peramalan Dengan Metode Fuzzy Time Series Markov Chain ]]>
<![CDATA[Yenni Safitri]]>;
<![CDATA[Sri Wahyuningsih]]>;
<![CDATA[Rito Goejantoro]]>
<![CDATA[ EKSPONENSIAL Vol 9 No 1 (2018) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[Forecasting is an activity to predict what will happen in the future with certain methods. Fuzzy time series is a method known as artificial intelligence used to predict the problem which the actual data is formed in linguistic values using fuzzy principles as its basis. This study discusses the method of fuzzy time series developed by Ruey Chyn Tsaur to predict the closing price of the shares of PT. Radiant Utama Interinso Tbk April 2017. Markov Chain fuzzy time series method is used to analyze a time series data which is a combination of fuzzy time series model with Markov Chain. Forecasting of closing stock price based on data from January 2011 to March 2017 for April 2017 is Rp 224,29,00. Markov Chain's fuzzy time series method to forecast the closing stock prices data from January 2011 to March 2017 has a 3,48% of MAPE value or has a 96,52% of precision forecast. The results show that the Markov Chain fuzzy time series has an excellent level of accuracy for forecasting the closing stock prices.]]>
<![CDATA[Optimasi Parameter Pemulusan Pada Metode Peramalan Double Exponential Smoothing Holt Menggunakan Golden Section ]]>
<![CDATA[Tika Anggre Ria Yani]]>;
<![CDATA[Sri Wahyuningsih]]>;
<![CDATA[Meiliyani Siringoringo]]>
<![CDATA[ EKSPONENSIAL Vol 13 No 1 (2022) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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DOI: 10.30872/eksponensial.v13i1.880
<![CDATA[Double Exponential Smoothing Holt (DES Holt) is a method that can be used when the data pattern shows a trend pattern. Determination of smoothing parameters usually uses trial and error, but this method still has inefficient results to get the best accuracy. One method that can be used to determine the smoothing parameters value is the golden section method. The application of the DES Holt and golden section methods will be carried out to predict the Exchange Rate of Farmers Subsector Livestock (ERFSL) of East Kalimantan Province. The purpose of this study was to obtain forecasting results and the level of accuracy of the ERFSL of East Kalimantan Province for the period January, February, and March 2020 using the DES Holt methods with the golden section smoothing parameter optimization method. The Forecasting results of DES Holt method have increased in the next three periods with an accuracy rate of 0.8856663%. The level of accuracy of forecasting results using the DES Holt methods has a MAPE value of less than 10%, which means the methods very good for predicting the ERFSL of East Kalimantan Province.]]>
