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<![CDATA[Optimasi Pendistribusian Barang Dengan Menggunakan Vogel’s Approximation Method dan Stepping Stone Method ]]>
<![CDATA[Yuli Ratnasari]]>;
<![CDATA[Desi Yuniarti]]>;
<![CDATA[Ika Purnamasari]]>
<![CDATA[ EKSPONENSIAL Vol 10 No 2 (2019) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[The development of era and technology are getting shopisticated which impacts the increasing of company in service area. Distribution and transportation are important aspects that can affect the success of the company’s performance.Vogel’s Approximation Method the first solution to solve the transportation problem and also Stepping Stone Method for the optimum solution to get the minimum operational cost. The aim of this research is to see the difference distribution operational cost of LPG gas 3 Kg in PT. Tri Pribumi Sejati before and after applying Vogel’s Approximation Method (VAM) and Stepping Stone Method. The result shows that Vogel’s Approximation Method (VAM) spent transportation cost Rp 24.353.568,- so it saved the transportation cost for 45,9% and made difference Rp 20.646.432,-. Next, applying Stepping Stone Method optimum solution spent transportation cost Rp 24.031.104,- so it also saved the transportation cost for 46,6% and made difference Rp 20.968.896,- of total cost of PT. Tri Pribumi Sejati Rp 45.000.000,-. To sum up that using Vogel’s Approximation Method the first solution and Stepping Stone Method optimum solution are exact method to minimize the distribution operational cost of 3kg gas tube in PT. Tri Pribumi Sejati.]]>
<![CDATA[Klasifikasi Data Nasabah Asuransi Dengan Menggunakan Metode Naive Bayes ]]>
<![CDATA[Dyah Arumatica Novilla]]>;
<![CDATA[Rito Goejantoro]]>;
<![CDATA[Fidia Deny Tisna Amijaya]]>
<![CDATA[ EKSPONENSIAL Vol 10 No 2 (2019) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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DOI: 10.30872/eksponensial.v10i2.565
<![CDATA[Classification is the logical grouping of objects according to the characteristics of their similarities. Naive Bayes is a method for predicting future opportunities based on past experiences. This study discusses the classification of insurance customer data of PT. Prudential Life Branch of Samarinda in 2017. With the aim to know whether the method of Naive Bayes can classify data of insurance customers of PT. Prudential Life in 2017 using the R program and to determine the accuracy of the results of data testing I and data testing II. As a result, Naive Bayes method can classify data of insurance customers of PT. Prudential Life with 80% accuracy for 25 data testing I and 74.67% for 75 data testing II.]]>
<![CDATA[Perbandingan Klasifikasi Metode Naive Bayes dan Metode Decision Tree Algoritma (J48) pada Pasien Penderita Penyakit Stroke di RSUD Abdul Wahab Sjahranie Samarinda ]]>
<![CDATA[Irene Lishania]]>;
<![CDATA[Rito Goejantoro]]>;
<![CDATA[Yuki Novia Nasution]]>
<![CDATA[ EKSPONENSIAL Vol 10 No 2 (2019) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[Classification is a technique to form a model of the data that has not been classified, then the model can be used to classify new data. Naive Bayes is a classification using probability method based on the Bayes theorem with a strong assumption of independence. The decision tree algorithm (J48) is an implementation of the algorithm (C4.5) that produces decision trees. In this research, will be compared the results of classification accuracy with the naive Bayes method and the decision tree algorithm (J48) in stroke patients. That is, a person who has stroke will be classified by using the data of patients in Abdul Wahab Sjahranie Samarinda Hospital with 7 factors, namely age, gender, blood pressure, diabetes mellitus, dyslipidemia, uric acid levels and heart disease. The results showed that the decision tree algorithm (J48) method has the higher level of accuracy than the method naive Bayes for stroke classification.]]>
<![CDATA[Peramalan Menggunakan Time Invariant Fuzzy Time Series ]]>
<![CDATA[Siti Rahmah Binaiya]]>;
<![CDATA[Memi Nor Hayati]]>;
<![CDATA[Ika Purnamasari]]>
<![CDATA[ EKSPONENSIAL Vol 10 No 2 (2019) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[Forecasting is a technique for estimating a value in the future by looking at past and current data. Fuzzy Time Series is a forecasting method that uses fuzzy principles as the basis, where the forecasting process uses the concept of fuzzy set. This study discusses the Time Invariant Fuzzy Time Series method developed by Sah and Degtiarev to forecast the East Kalimantan Province Consumer Price Index (CPI) in May 2018. In the Time Invariant Fuzzy Time Series method using a frequency distribution to determine the length of the interval, 13 fuzzy sets are used in the forecasting process. Based on this study, using CPI data of East Kalimantan Province from September 2016 to April 2018, the forecasting results for May 2018 were obtained 135.977 and obtained the results of forecasting error values using Mean Absolute Percentage Error (MAPE) is under 10% of 0.0949%.]]>
<![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[Perbandingan Pengelompokan K-Means dan K-Medoids Pada Data Potensi Kebakaran Hutan/Lahan Berdasarkan Persebaran Titik Panas ]]>
<![CDATA[Athifaturrofifah Athifaturrofifah]]>;
<![CDATA[Rito Goejantoro]]>;
<![CDATA[Desi Yuniarti]]>
<![CDATA[ EKSPONENSIAL Vol 10 No 2 (2019) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[The cases of forest/land fires in Indonesia seem endless, almost every year in the dry season similar problems always occur. Some areas in Indonesia often occur in forest fires and result in losses of up to trillions of rupiah. Various ways have been made to help the government in minimizing the potential for forest or land fires, one of them is by monitoring hot spots. In this study using data hot spots with parameters of latitude, longitude, brightness, fire radiation power and confidence by using the method of grouping K-Means and K-Medoids. The difference between these two methods is that the K-means method uses the mean as the center of the cluster, while K-Medoids uses representative objects (medoids) as the center of the cluster. This study aims to compare the results of the grouping of K-Means method with K-Medoids by using 42 data. The results of this study indicate that the K-Means method produces Silhouette Coefficient scores greater than K-Medoids. So that, K-Means can provide more accurate grouping results with a greater Silhouette Coefficient value.]]>
<![CDATA[Penaksiran Parameter Model Mixed Geographically Weighted Regression (MGWR) Data Indeks Pembangunan Manusia di Kalimantan Tahun 2016 ]]>
<![CDATA[Mita Asti Wulandari]]>;
<![CDATA[Suyitno Suyitno]]>;
<![CDATA[Wasono Wasono]]>
<![CDATA[ EKSPONENSIAL Vol 10 No 2 (2019) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[Mixed Geographical Regression (MGWR) model is a combination of global linear regression model and GWR model. Some MGWR parameters are global (the same value) and the other parameters are local (different values) at each observation location. The purpose of this study is to obtain MGWR model for every District’s HDI and to obtain the factors that significantly influence District HDI in East Kalimantan, Central Kalimantan and South Kalimantan Provinces. Estimating parameters for global parameters use Ordinary Least Square (OLS) method. Estimating parameters for local parameters use Weighted Least Square (WLS) method, where weighting spatial is determined by using gaussian adaptive function. Based on the result of MGWR parameters testing, it was concluded that the school enrollment rates (SMP) affected the HDI of all districs in East Kalimantan, Central Kalimantan and South Kalimantan provinces. The population density and the percentage of poor people influence locally to HDI.]]>
<![CDATA[Penerapan Model Seasonal Autoregressive Fractionally Integrated Moving Average Pada Data Inflasi di Indonesia ]]>
<![CDATA[Edy Fahrin]]>;
<![CDATA[Memi Nor Hayati]]>;
<![CDATA[Meiliyani Siringoringo]]>
<![CDATA[ EKSPONENSIAL Vol 10 No 2 (2019) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[Current inflation data is influenced by previous inflation data. Inflation data from time to time is indicated to have a long memory and seasonal pattern. The Seasonal Autoregressive Fractional Integrated Moving Average (SARFIMA) model is one of the models used to predict data that has a long memory and seasonal pattern. The purpose of this research was to find out the the best SARFIMA model and forecast inflation in 2018 using the best SARFIMA model. The sample in this research was Indonesian monthly inflation data for the period January 2008 to December 2017. There are four stages of SARFIMA modeling, namely model identification, parameter estimation, diagnostic checking, and application of models for forecasting. Based on the results of the analysis, the best SARFIMA model selected based on the AIC and MSE criteria is the SARFIMA model with d = 0.687. The results of inflation forecasting from January to December 2018 show a fluctuating value every month with the inflation rate at 3.30% - 3.65%.]]>
<![CDATA[Analisis Model Intervensi Fungsi Step Ganda untuk Peramalan Inflasi Indonesia ]]>
<![CDATA[Masrawanti Masrawanti]]>;
<![CDATA[Sri Wahyuningsih]]>;
<![CDATA[Memi Nor Hayati]]>
<![CDATA[ EKSPONENSIAL Vol 10 No 2 (2019): Jurnal Eksponensial ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[The intervention model is one time series model that can be used to explain the impact of an intervention caused by external or internal factors that occur in a time series data. This model can also be generally used to explain structural changes in a time series data. The purposes of this study are to determine the intervention model of double step function on the increase of the price of fuel oil to the Indonesia’s inflation (yoy), and forecasting Indonesia's inflation (yoy) period 2018. The government's policy to increase of the price of fuel oil in June 2013 and November 2014 is a step intervention because impact of the intervention is permanent. The procedure of forming an intervention model is a double step function that is determining the intervention function that occurs during the research period, dividing the data based on the time of the intervention, modelling, estimating parameters, testing diagnostics, and selecting the best model. Next stage is forming the first and second intervention models. The best model for predicting Indonesia's inflation (yoy) isSARIMA (0,1,1) (1,0,0)12 as the model before the intervention with the order of the first intervention responseand the second intervention response order . The results of forecasting Indonesia's inflation (yoy) in the period 2018 will placed around the average inflation amount 3%.]]>
<![CDATA[Deteksi Pencilan Spasial pada Data Kandungan Klorida di Sungai Mahakam Wilayah Samarinda Kalimantan Timur ]]>
<![CDATA[Muhammad Jainudin]]>;
<![CDATA[Memi Nor Hayati]]>;
<![CDATA[Ika Purnamasari]]>
<![CDATA[ EKSPONENSIAL Vol 10 No 2 (2019): Jurnal Eksponensial ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
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<![CDATA[Spatial data is data presented in the geographical position of an object, relating to the location in the space of the earth. In spatial data often have conditions that are not reasonable because the existence of outlier. Outlier referred to are spatial outlier that are defined as local instability or spatial objects that describe relatively extreme non-spatial attributes or differ significantly from other objects. The existence of outlier can have an impact on the results of model parameter estimates for example, which causes the estimation results to be biased. One method of outlier detection is spatial statistic Z test. This research aims to detect outlier chloride level data in seven locations on the Mahakam River of Samarinda area using spatial statistic Z test method. Based on the calculations with a significance level of 5% from the seven locations, there is one location which is outlier at the location IPA Tirta Kencana value equal to Zhit is 1.997.]]>
