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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[Sari, Devi Nur Endah]]>;
<![CDATA[Hayati, Memi Nor]]>;
<![CDATA[Wahyuningsih, Sri]]>
<![CDATA[ EKSPONENSIAL Vol. 11 No. 1 (2020) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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DOI: 10.30872/eksponensial.v11i1.645
<![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[Peramalan dengan Menggunakan Metode Holt-Winters Exponential Smoothing: Studi Kasus: Jumlah Wisatawan Mancanegara yang Berkunjung Ke Indonesia ]]>
<![CDATA[Aryati, Ayu]]>;
<![CDATA[Purnamasari, Ika]]>;
<![CDATA[Nasution, Yuki Novia]]>
<![CDATA[ EKSPONENSIAL Vol. 11 No. 1 (2020) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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DOI: 10.30872/eksponensial.v11i1.650
<![CDATA[Forecasting is a technique for estimating a value in the future by looking at past and current data. Foreign tourists are everyone who visits a country outside their place of residence, driven by one or several needs without intending to earn income in the place visited and the duration of the visit is no more than twelve months. The method used in this study is the Holt-Winters smoothing smoothing method. In this study used data of foreign tourists visiting Indonesia in January 2014 - September 2018. The purpose of this study was to determine the pattern of data forecasting the number of foreign tourists, the value of the accuracy of forecasting, and the results of forecasting. Based on the Holt-Winters smoothing method, the data pattern for the number of foreign tourists is the multiplicative Holt-Winters data pattern. The value of the smoothing parameter combination with the smallest MAPE of 0,938% is α = 0,9; β = 0,1; and γ = 0,9. The results of forecasting the number of foreign tourists visiting Indonesia in October 2018 and November 2018 were 1.410.157 and 1.362.473 people respectively]]>
<![CDATA[Model-Model Regresi Weibull Univariat pada Indikator Pencemaran Air Dissolved Oxygen di Daerah Aliran Sungai Lingkungan Hutan Hujan Tropis Kalimantan Timur ]]>
<![CDATA[Chairina, Puspa]]>;
<![CDATA[Suyitno, Suyitno]]>;
<![CDATA[Siringoringo, Meiliyani]]>
<![CDATA[ EKSPONENSIAL Vol. 11 No. 1 (2020) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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DOI: 10.30872/eksponensial.v11i1.641
<![CDATA[A Univariate Weibull Regression is a model of regression developed from univariate Weibull distribution with the parameter scale is stated in parameter regression. There are some of univariate Weibull regression model, namely Weibull survival regression, Weibull hazard regression and mean model. Univariate Weibull regression model in this research is applied to the water pollution indicator dissolved oxygen (DO data at Mahakam river in East Kalimantan. The purpose of this study is to find out the model of univariate Weibull regression based on the parameter estimation by using maximum likelihood estimation method (MLE) and to find out the factors which affect to univariate Weibull regression in Mahakam river. The result shows that the closed form of the maximum likelihood estimator can not be found analytically, and it can be approximed by using the Newton-Raphson iterative method. Based on the result of partial hypothesis test for all the parameter regression, it was found that detergent concentration and nitrate concentration had significant influence to the DO in the water of Mahakam river.]]>
<![CDATA[Analisis Distribusi Frekuensi dan Periode Ulang Hujan: Studi Kasus: Curah Hujan Kecamatan Long Iram Kabupaten Kutai Barat Tahun 2013-2017 ]]>
<![CDATA[Widyawati, Widyawati]]>;
<![CDATA[Yuniarti, Desi]]>;
<![CDATA[Goejantoro, Rito]]>
<![CDATA[ EKSPONENSIAL Vol. 11 No. 1 (2020) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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DOI: 10.30872/eksponensial.v11i1.646
<![CDATA[Increasing water demand for various needs can be a complex problem so it is necessary to manage water resources. Analysis of hydrological data is very necessary to get information about water resources where the information can be used as a benchmark for planning a water resources builder. One of hydrological analysis is the analysis of rainfall data where this analysis uses frequency distribution analysis and rain return periods. There are four types of distribution used, namely normal distribution, normal log distribution, Gumbel distribution and type III log Pearson distribution. The goodness of fit test uses the Kolmogorov-Smirnov method, Chi-Square and Anderson-Darling. Rainfall return calculation is calculated when it is known the type of distribution of the data studied. This research uses rainfall data of Long Iram Sub-Distric, West Kutai Distric in 2013 to 2017 obtained from the Meteorology, Climatology and Geophysics Agency (BMKG) of Samarinda City. The results from research showed that the Gumbel distribution was the right distribution or distribution that was the best with the results of the return period of rain for the return period of 2 years obtained by rainfall of 519 mm, 5-year return period of 796 mm, 10-year return period of 980 mm, return period 20 years of 1.154 mm, a 50 year return period of 1.348 mm and in a 100 year return period of 1.752 mm.]]>
<![CDATA[Pengelompokkan Data Runtun Waktu menggunakan Analisis Cluster: Studi Kasus: Nilai Ekspor Komoditi Migas dan Nonmigas Provinsi Kalimantan Timur Periode Januari 2000-Desember 2016 ]]>
<![CDATA[Dani, Andrea Tri Rian]]>;
<![CDATA[Wahyuningsih, Sri]]>;
<![CDATA[Rizki, Nanda Arista]]>
<![CDATA[ EKSPONENSIAL Vol. 11 No. 1 (2020) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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DOI: 10.30872/eksponensial.v11i1.642
<![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 Diagram Kontrol Fuzzy U: Studi Kasus: Kecacatan Produk Kayu Lapis (Plywood) di PT. Segara Timber Mangkujenang, Samarinda Provinsi Kalimantan Timur Tahun 2018 ]]>
<![CDATA[Fauzia, Rina]]>;
<![CDATA[Yuniarti, Desi]]>;
<![CDATA[Hayati, Memi Nor]]>
<![CDATA[ EKSPONENSIAL Vol. 11 No. 1 (2020) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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DOI: 10.30872/eksponensial.v11i1.647
<![CDATA[Fuzzy in general means that an element can be classified into two sets simultaneously. Fuzzy control diagrams are very suitable to be used for observations that produce information (data) that is uncertain, unclear and based on one's subjectivity. This study was applied to data on plywood products in PT. Segara Timber, Samarinda, East Kalimantan Province in 2018. The purpose of this study is to get the results of the decision fuzzy u control diagram. Based on the results of the use of the fuzzy control diagram u produce the most found decisions are rather in control that is equal to 26 observations, while the second most is rather out of control that is equal to 22 observations, and out of control that is equal to 14 and in control of 5 out of 67 observation.]]>
<![CDATA[Penerapan Model Geographically Weighted Logistic Regression Pada Data Status Kesejahteraan Masyarakat di Kalimantan Tahun 2017 ]]>
<![CDATA[Pratiwi, Nadya]]>;
<![CDATA[Suyitno, Suyitno]]>;
<![CDATA[Siringoringo, Meiliyani]]>
<![CDATA[ EKSPONENSIAL Vol. 11 No. 1 (2020) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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DOI: 10.30872/eksponensial.v11i1.648
<![CDATA[Geographically Weighted Logistic Regression (GWLR) model is a regression model developed from logistic regression which is applied to spatial data. The aims of research is a GWLR model determination on dichotomous data of community welfare status based on the Human Development Index (HDI) and to find the factors influencing the probability of high welfare status of each Regency/City on Kalimantan Island in 2017. Parameters estimation of the GWLR model was done at each observation location using a weighted Maximum Likelihood Estimation (MLE) method and maximum likelihood estimator was obtained by Newton Raphson iterative method. Spatial weighting on parameter estimation was determined using Adaptive Gaussian weighting function and optimum bandwidth was determined using Generalized Cross-Validation (GCV) criterion. Based on the result of GWLR parameter testing, it was concluded that the factors influencing the probability of high welfare status of Regency/City on Kalimantan Island in 2017 were school enrollment rates (senior high school), the number of health workers, real per capita income and the open unemployment rate.]]>
<![CDATA[Model Regresi Cox Weibull Dengan Metode Penaksiran Parameter Efron Partial Likelihood: Studi Kasus : Lama Perawatan Pasien Penderita Tuberkulosis Di Puskesmas Loa Ipuh Tenggarong Tahun 2017 ]]>
<![CDATA[Ihsan, Ihsan]]>;
<![CDATA[Suyitno, Suyitno]]>;
<![CDATA[Wahyuningsih, Sri]]>
<![CDATA[ EKSPONENSIAL Vol. 11 No. 1 (2020) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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DOI: 10.30872/eksponensial.v11i1.639
<![CDATA[Survival analysis is an analysis that involves statistical tests to analyze data on the time or length of time until the occurrence of a particular event. One regression model for time duration data modeling is the Cox PH regression model. Cox PH regression applied to time duration data with Weibull distribution is called Cox PH Weibull regression. The purpose of this study is to obtain a Cox PH Weibull regression model and determine the factors that influence the length of treatment for tuberculosis patients. The parameter estimation in this study is Efron partial likelihood method. The Efron partial likelihood method is suitable for estimating Cox PH regression parameters to data containing ties. Based on the results of parameter estimation the best model is obtained by using AIC crtiteria. Based on the partial test, age is factor that influence to the length of treatment. The results of the study show that every increasing age patient one year, then length of treatment until the patient recovered, will increas 1,024 times.]]>
<![CDATA[Penerapan Latent Class Regression Analysis dalam Segmentasi Pasar ]]>
<![CDATA[Musmirani, Musmirani]]>;
<![CDATA[Purnamasari, Ika]]>;
<![CDATA[Suyitno, Suyitno]]>
<![CDATA[ EKSPONENSIAL Vol. 11 No. 1 (2020) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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DOI: 10.30872/eksponensial.v11i1.644
<![CDATA[Cluster analysis is a method of grouping observation objects into several classes. One method of mixed-scale data grouping is Latent Class Regression Analysis (LCRA). The purpose of this research is to classify the opinion of Wardah's product consumers on marketing strategies (product aspects, price aspects, location aspects, and promotion aspects) PT. Paragon Technology and Innovation Regional Samarinda in 2017 with covariate variables arelength of subscription, type of work and age of consumers. Estimation of LCRA using the Expectation Maximization (EM) method, solved by the Newton-Raphson method. The result of LCRA analysis that based on consumer opinion on market segmentation, consumers are grouped into two classes.The first class is 31 consumers that strongly agrees the aspects of product, price, promotion and position are appropriate market segmentation, and the second class is 69 that quite agrees product aspects, prices, promotion and position is the appropriate market segmentation.]]>
<![CDATA[Penentuan Jalur Terpendek dengan Metode Heuristik Menggunakan Algoritma Sarang Semut (Ant Colony): Studi Kasus: Jalan Arteri Sekunder Kota Samarinda ]]>
<![CDATA[Hidayat, Alfian]]>;
<![CDATA[Purnamasari, Ika]]>;
<![CDATA[Siringoringo, Meiliyani]]>
<![CDATA[ EKSPONENSIAL Vol. 11 No. 1 (2020) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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DOI: 10.30872/eksponensial.v11i1.649
<![CDATA[Ant Colony algorithm was adopted from the behavior of ant colonies, known as the system of ants, ant colonies are naturally able to find the shortest route on their way from nest to food source places. Colony of ants can find shortest route between the nest and food sources based on the trajectory of footprints that have been passed. The density of ant footprints on the path is always updating because of the evaporation of the footprints and the determination of ant pathways using probability calculations. This study aims to determine the results of determining the shortest path using the ant colony algorithm as the best route from the Samarinda City secondary arterial road with the route starts from Slamet Riyadi road to DI Panjaitan road. Based on the results of the study using the ant colony algorithm obtained the shortest path of 8.307 kilometers with footprint density of 1.005.]]>
