Articles
12 Documents
Search results for
, issue
"Vol. 13 No. 2 (2022)"
:
12 Documents
clear
<![CDATA[Peramalan Jumlah Kunjungan Wisatawan Mancanegara Ke Indonesia Menggunakan Autoregressive Integrated Moving Average (ARIMA) ]]>
<![CDATA[Ramadhani, Adelia]]>;
<![CDATA[Wahyuningsih, Sri]]>;
<![CDATA[Siringoringo, Meiliyani]]>
<![CDATA[ EKSPONENSIAL Vol. 13 No. 2 (2022) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (1130.747 KB)
|
DOI: 10.30872/eksponensial.v13i2.1049
<![CDATA[Autoregressive Moving Average (ARIMA) is a general model that is often used in time series modeling. One application of ARIMA can be used on the data foreign tourist visits to Indonesia. The tourism sector is one of the priority sectors in Indonesia's economic development. One of the determining factors in the tourism sector is the number of foreign tourist visits. Therefore, forecasting the number of foreign tourist visits is very necessary. The purpose of this study was to obtain a model and forecast results for the number of foreign tourist visits from March 2020 to October 2021 using the ARIMA model. The results of the analysis showed that the ARIMA model (0,1,1) was the best model with a MAPE of 6.23%. The forecasting results with the best model showed that the highest number of foreign tourist visits is in Agustus 2021 and the lowest is in December 2020.]]>
<![CDATA[Pengelompokan Kabupaten/Kota Di Pulau Kalimantan Dengan Fuzzy C-Means Berdasarkan Indikator Kemiskinan ]]>
<![CDATA[Ningtyas, Retno Ayu]]>;
<![CDATA[Nasution, Yuki Novia]]>;
<![CDATA[Syaripuddin, Syaripuddin]]>
<![CDATA[ EKSPONENSIAL Vol. 13 No. 2 (2022) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (869.216 KB)
|
DOI: 10.30872/eksponensial.v13i2.1054
<![CDATA[Cluster analysis is a branch of statistical science that is used to grouping data that have similar characteristics between each other. The grouping method used in this research is Fuzzy C-Means. Fuzzy C-Means method is one of the grouping methods developed from the C-Means method by applying the properties of fuzzy sets. With the existence of each data is determined by the degree of membership. This method is applied to data from 56 districts/cities on Borneo based on poverty indicators with variables namely the percentage of average length of schooling, life expectancy, percentage of the poor, percentage of open unemployment rate, percentage of households with proper sanitation, and percentage of households with proper drinking water. This study aims to obtain the results of grouping districts/cities on Borneo based on poverty indicators and to obtain optimal cluster results based on three validity indices, namely Connectivity, Dunn, and Silhoutte values. Based on the results of the study, it was found that there were 2 optimal clusters, namely the first cluster consisted of 36 regencies/cities while the second cluster consisted of 20 regencies/cities.]]>
<![CDATA[Peramalan Kredit Modal Kerja di Indonesia Menggunakan Brown's Double Exponential Smoothing dengan Optimasi Pencarian Dikotomis ]]>
<![CDATA[Yustiani, Iis]]>;
<![CDATA[Wahyuningsih, Sri]]>;
<![CDATA[Siringoringo, Meiliyani]]>
<![CDATA[ EKSPONENSIAL Vol. 13 No. 2 (2022) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (829.587 KB)
|
DOI: 10.30872/eksponensial.v13i2.948
<![CDATA[Brown's Double Exponential Smoothing (DES) method is a forecasting method with the smoothing process carried out twice. DES Brown has one parameter to define, and it is usually done in a trial and error manner. Another way to determine value parameters more quickly and precisely is to use optimization methods. In this study, forecasting of working capital credit in Indonesia using DES Brown for the period May to July 2022 was carried out with dichotomous search optimization. The results showed that the results of forecasting for working capital loans showed a decrease in May then increased in June and July with a very good forecasting accuracy, namely the MAPE value of 1.480768%.]]>
<![CDATA[Model Geographically Weighted Weibull Regression Pada Indikator Pencemaran Air COD di Daerah Aliran Sungai Mahakam Kalimantan Timur ]]>
<![CDATA[Primadigna, Ullimaz Sam]]>;
<![CDATA[Suyitno, Suyitno]]>;
<![CDATA[Siringoringo, Meiliyani]]>
<![CDATA[ EKSPONENSIAL Vol. 13 No. 2 (2022) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (860.595 KB)
|
DOI: 10.30872/eksponensial.v13i2.1050
<![CDATA[The Geographically Weighted Weibull Regression (GWWR) model is a Weibull regression model applied to spatial data. Parameter estimation is carried out at each observation location using spatial weighting. This study aimed to determine the GWWR model on the Chemical Oxygen Demand (COD) water pollution indicator data and to obtain the factors that influence COD in the Mahakam watershed. The parameter estimation method was Maximum Likelihood Estimation (MLE). Spatial weighting in parameter estimation has been determined using the adaptive tricube weighting function and the criteria for determining the optimum bandwidth was Generalized Cross-Validation (GCV). The research sample was 20 location points of the Mahakam river determined by the Environmental Department of East Kalimantan Province. The results showed that the factors that influence COD locally was temperature, while the factors that influence globally were temperature, Total Suspended Solids (TSS), and Fecal Coli.]]>
<![CDATA[Optimasi Algoritma Naïve Bayes Menggunakan Algoritma Genetika Untuk Memprediksi Kelulusan: Studi Kasus: Mahasiswa Jurusan Matematika FMIPA Universitas Mulawarman ]]>
<![CDATA[Feronica, Elisa]]>;
<![CDATA[Nasution, Yuki Novia]]>;
<![CDATA[Purnamasari, Ika]]>
<![CDATA[ EKSPONENSIAL Vol. 13 No. 2 (2022) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (1106.672 KB)
|
DOI: 10.30872/eksponensial.v13i2.1057
<![CDATA[The Naïve Bayes algorithm is classification method that uses the principle of probability to create predictive models. Naïve Bayes is based on the assumption that all its attributes are independent which can be optimized by genetic algorithms. Genetic algorithm is an optimization technique which works by imitating the process of evaluating and changing the genetic structure of living creatures. In this study, the Naive Bayes algorithm was optimized using by genetic algorithm to predict student graduation with attributes, namely gender, regional origin, admission path and employment status. The data used is the students of the Mathematics Department, Faculty of Mathematics and Natural Sciences, Mulawarman University who graduated in March 2018 to December 2020. The results of this study indicate the accuracy value generated by Naïve Bayes of 50% increased by 16,67% after the attributes were optimized by using the genetic algorithm to 66,67% with 3 selected attributes, namely regional origin, admission path and employment status]]>
<![CDATA[Perbandingan Algoritma C4.5 Dan Naïve Bayes Untuk Prediksi Ketepatan Waktu Studi Mahasiswa: Studi Kasus: Program Studi Statistika Universitas Mulawarman ]]>
<![CDATA[Permana, Jordan Nata]]>;
<![CDATA[Goejantoro, Rito]]>;
<![CDATA[Prangga, Surya]]>
<![CDATA[ EKSPONENSIAL Vol. 13 No. 2 (2022) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (1043.881 KB)
|
DOI: 10.30872/eksponensial.v13i2.947
<![CDATA[Classification is a statistical technique that aims to classify data into classes that already have labels by building a model based on training data. There are many methods that can be used in the classification including Naïve Bayes and C4.5. The C4.5 algorithm is an algorithm used to form a decision tree while Naïve Bayes is a classification based on probability. This study aims to determine the results of the classification of C4.5 and Naïve Bayes and to determine the classification accuracy of the two methods. The variables used in this study were graduation status , entrance , gender , regional origin , GPA , and UKT group . After the analysis, the results showed that the average accuracy level of the C4.5 algorithm was 61.99% and the Naïve Bayes accuracy level was 69.97%. So it can be said that the Naïve Bayes method is a better method in classifying student status compared to the C4.5 . method.]]>
<![CDATA[Model Regresi Weibull Pada Data Kontinu yang Diklasifikasikan: Studi Kasus: Indikator Pencemaran Air BOD di DAS Mahakam Tahun 2016 ]]>
<![CDATA[Panduwinata, Hesty Dwiyugo]]>;
<![CDATA[Suyitno, Suyitno]]>;
<![CDATA[Huda, Moh. Nurul]]>
<![CDATA[ EKSPONENSIAL Vol. 13 No. 2 (2022) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (765.081 KB)
|
DOI: 10.30872/eksponensial.v13i2.1051
<![CDATA[Weibull Regression is a model of regression developed from Weibull distribution in which scale parameter is expressed in the regression parameters. The Weibull regression models discussed in this study are the Weibull survival regression, Weibull hazard regression and regression model for the mean. The Weibull survival regression model is a model of the probability that the Mahakam River water is polluted. The Weibull hazard regression model is a model of velocity of the polluted Mahakam River water, and the Weibull regression for the mean is the model used to predict the average value of BOD (Biochemical Oxygen Demand). The purpose of this study was to obtain the Weibull regression model on BOD water pollution indicator data in the Mahakam River basin, to determine the factors that influence the Weibull regression model. The parameter method is maximum likelihood estimation (MLE). Based on the parameter estimation results, the maximum likelihood estimator is obtained by using the method of Newton-Raphson iteration. The results of hypothesis testing, it is concluded that the factors that influence the Weibull regression model are pH, Total Dissolved Solid (TDS) and water discharge.]]>
<![CDATA[Peramalan Curah Hujan di Kota Samarinda Menggunakan Autoregressive Integrated Moving Average (ARIMA) ]]>
<![CDATA[Syawal, Al Fitri]]>;
<![CDATA[Wahyuningsih, Sri]]>;
<![CDATA[Siringoringo, Meiliyani]]>
<![CDATA[ EKSPONENSIAL Vol. 13 No. 2 (2022) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (1085.686 KB)
|
DOI: 10.30872/eksponensial.v13i2.1058
<![CDATA[Autoregressive Integrated Moving Average (ARIMA) is a forecasting model for time series data analysis. In this study, the modeling and forecasting of monthly rainfall in Samarinda City was carried out using the ARIMA model. The results showed that the ARIMA (6, 1, 1) model was the best model . The results of forecasting rainfall for the period January to December 2022 in Samarinda City using the ARIMA (6, 1, 1) model show that rainfall tends to be constant every month. The lowest level of rainfall occurred in January 2022, which was 210.3869 mm. The highest level of rainfall occurred in April 2022, which was 271.5705 mm.]]>
<![CDATA[Penerapan Metode Fuzzy C-Means Pada Pengelompokan Kabupaten/Kota di Pulau Kalimantan Berdasarkan Indikator Kesejahteraan Rakyat Tahun 2020 ]]>
<![CDATA[Nurmin, Deviyana]]>;
<![CDATA[Hayati, Memi Nor]]>;
<![CDATA[Goejantoro, Rito]]>
<![CDATA[ EKSPONENSIAL Vol. 13 No. 2 (2022) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (944.672 KB)
|
DOI: 10.30872/eksponensial.v13i2.1068
<![CDATA[Clustering is a method of grouping data into several clusters or groups so that data in one cluster has a high level of similarity and data between clusters has a low level of similarity. The clustering method used in this research is Fuzzy C-Means (FCM). FCM is a data grouping technique in which the existence of each data point in a cluster is determined by the degree of membership. To optimize the grouping results, it is necessary to validate the number of clusters using Partition Coefficient (PC). The purpose of this study is to obtain optimal grouping results from the FCM method using the PC validity indices from the people's welfare indicator data in 56 regencies/cities on the island of Kalimantan in 2020. Based on the results of the analysis, the conclusion is that the optimal number of clusters is three clusters. The first cluster consists of 24 regencies/cities on the island of Kalimantan, the second cluster consists of 17 regencies/cities on the island of Kalimantan, and the third cluster consists of 15 regencies/cities on the island of Kalimantan.]]>
<![CDATA[Penerapan Metode Adams-Bashforth-Moulton pada Persamaan Logistik Dalam Memprediksi Pertumbuhan Penduduk di Provinsi Kalimantan Timur ]]>
<![CDATA[Apriani, Dewi]]>;
<![CDATA[Wasono, Wasono]]>;
<![CDATA[Huda, Moh. Nurul]]>
<![CDATA[ EKSPONENSIAL Vol. 13 No. 2 (2022) ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA Universitas Mulawarman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (716.25 KB)
|
DOI: 10.30872/eksponensial.v13i2.1046
<![CDATA[Logistic equation is a nonlinear ordinary differential equation that describes the population. Nonlinear ordinary differential equations can be solved by one of the numerical methods, namely the Adams-Bashforth-Moulton method. Adams-Bashforth-Moulton method is a multistep method which consists of Adams-Bashforth method as predictor and Adams-Moulton method as corrector. The logistic equation is solved first by using the Runge-Kutta method to obtain the four initial solutions, then followed by the Adams-bashforth-Moulton method. This study aims to predict population growth in the province of East Kalimantan using the Adams-Bashforth-Moulton method. Based on the calculation results obtained a numerical solution of the logistic equation for population growth at , with a step size of , the capacity of the province of East Kalimantan is and the growth rate of is 3,856,564 inhabitants.]]>
