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Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Mulawarman Jl. Barong Tongkok, Kampus Gunung Kelua Kota Samarinda, Provinsi Kalimantan Timur 75123
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INDONESIA
Eksponensial
Published by Universitas Mulawarman
ISSN : 20857829     EISSN : 27983455     DOI : https://doi.org/10.30872/
Core Subject : Economy, Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other
Jurnal Eksponensial is a scientific journal that publishes articles of statistics and its application. This journal This journal is intended for researchers and readers who are interested of statistics and its applications.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 205 Documents
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Pengklasifikasian Item Persediaan Menggunakan Metode Always Better Control-Fuzzy Retno Octaviyani; Desi Yuniarti; Yuki Novia Nasution
EKSPONENSIAL Vol 9 No 2 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (583.105 KB)

Abstract

ABC Classification is a method of controlling inventory to control a small quantity of goods but has a high usage value. Inventories are categorized into three classes, namely A, B, and C. Fuzzy classification is a classification used to classify training data sets (data sets used to generate membership functions) and to predict data testing. The purpose of this study was to control inventory using the ABC classification method, Fuzzy Classification, and ABC-fuzzy classification. The results of ABC classification showed that from 182 items of drug, class A is consisted of 15 items of drug with a value of 69,276% usage, class B is consisted of 34 items of drug with a value of use of 20.723%, and class C is consisted of 133 items of drug with value use of 10.010%. The results of the fuzzy classification showed that of the 182 drug items, fuzzy 3 consisted of 9 medicinal items which meant that, there were 9 very important drugs, fuzzy 2 consisted of 171 drug items which meant that there were 171 important medicines, and fuzzy 1 consisted of 2 items of medicine which means that, there are 2 less important drugs. The results of the ABC-fuzzy classification showed that of 182 drug items, there were 17 items of drugs in the first priority which means that the 17 items of this drug are most preferred, then there are 41 items of drug on the 2nd priority which means the stock of 41 items of this drug is preferred, 124 items of drug on priority 3 which means that 124 items of this drug is not preferred.
Penerapan Metode Projected Unit Credit dan Entry Age Normal pada Asuransi Dana Pensiun Bayu Nanda Permana; Yuki Purnamasari; Ika Purnamasari
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Actuarial calculation method in pension funding is divided into two major categories, namely Accrued Benefit Cost Method and Projected Benefit Cost Method. One example method which is included in Accrued Benefit Cost Method is the Projected Unit Credit Method, and one of the method which is included in Projected Benefit Cost Method is the Entry Age Normal Method. Both methods are used to determine the amount of normal cost and actuarial liabilitiy which are the basis in determining pension benefits. The purpose of this study was to compare the value of normal cost and actuarial liabilities of the two methods. The data used in this research is the employee data from PT. INHUTANI I Berau Branch. The result showed that normal cost using Projected Unit Credit method continued increases with the salary received, meanwhile if using the Entry Age Normal method the amount of normal cost is same for each year to an employee. On the other hand, actuarial liability using Projected Unit Credit Method is smaller than using Entry Age Normal for each employee in each year.
Model Geographically Weighted Univariat Weibull Regression pada Data Indikator Pencemaran Air Dissolve Oxygen di Daerah Aliran Sungai Mahakam Kalimantan Timur Tahun 2018 Sugiarto, Sugiarto; Suyitno, Suyitno; Rizki, Nanda Arista
EKSPONENSIAL Vol. 12 No. 2 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1241.218 KB) | DOI: 10.30872/eksponensial.v12i2.813

Abstract

Geographically Weighted Univariat Weibull Regression (GWUWR) model is a regression model applied to spatial data. Parameter estimation of GWUWR model is performed at every observation location using spatial weighting. The purpose of this study is to determine the GWUWR model at the water pollution indicator data namely dissolved oxygen (DO) at Mahakam river in East Kalimantan and to find out the factors that influence DO in Mahakam river. The research data are secondary from the environmental services East Borneo. The research response variable was DO, meanwhile the predictor variables were pH, Total Dissolve Solid, Total Suspended Solid, Nitrate and Amonia. Parameter estimation method is Maximum Likelihood Estimation (MLE). Spatial weighting was determined using the Adaptive Gaussian weighting function and optimum bandwidth determination criteria used Generalized Cross-Validation (GCV). Based on the result of the parameter testing of GWUWR model it was concluded the factors influencing DO locally were pH, Total Dissolve Solid and ammonia concentrations, while the factors influencing globally were Total Dissolve Solid and ammonia concentration
Penerapan Statistika Nonparametrik dengan Metode Brown-Mood pada Regresi Linier Berganda Ni Wayan Rica A; Darnah Andi Nohe; Rito Goejantoro
EKSPONENSIAL Vol 7 No 1 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (301.194 KB)

Abstract

Brown-Mood is a method first developed by GW brown 1950 and AM mood in 1951 with the purpose of the parameters of the multiple linear regression model of the linear regression model of the equation of the median small sample size. This study discusse the application of the method of brown-mood on multiple linear regression with the open unemployment rate (X1), and growth rate of gross regional domestic product at constant prices (X2) to the number of poor population (Y) Province of east Kalimantan. If the method ordinary least square in a multiple linear regression is a statistical parametric aims to minimize the average (mean) error, the brown-mood methods as a nonparametric statistical method chose a multiple linear regression model by minimising the median and average weighted. The results of this research to get a linear regression model using the method of brown-mood is Ŷ=-31.11+1.74 X1 + 1.44 X2 from the multiple linear regression model obtained are percentage distribution of gross regional domestic product at current prices [without oil, gas and its products] and growth rate of gross regional domestic product at constant prices affect to the number of poor population.
Estimasi Parameter Model Regresi Linier dengan Pendekatan Bayes: Studi Kasus: Kemiskinan di Provinsi Kalimantan Timur pada Tahun 2017 Katianda, Kristin Rulin; Goejantoro, Rito; Satriya, Andi M Ade
EKSPONENSIAL Vol. 11 No. 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (740.26 KB) | DOI: 10.30872/eksponensial.v11i2.653

Abstract

Two types of viewpoints in statistics are Frequentist and Bayesian Method. In Bayesian method sees a parameter as a random variable, so the value is not single. Frequentist method that are often used in linear regression are Ordinary Least Square (OLS) and Maximum Likelihood Estimation (MLE). But along with developments, several studies show the results of modeling that are better at using Bayesian method than the Frequentist method. The data used is Poverty data in 2017 from BPS East Kalimantan. The purpose of this study is to estimate the parameters of the regression model with the Bayesian method on data on the number of poor people and regional domestic products in East Kalimantan Province in 2017. To estimate the parameters of the Bayesian linear regression model it is used by the prior conjugate distribution. Then the markov chain is designed from the posterior distribution with Gibbs Sampler as many as 50.000 iterations and the estimated parameters that are the average of the Gibbs Sampler value are = 0.9149, = 5.462, and = 0.2827. From the Gibbs Sampler values ​​that have been obtained, a density function for each parameter is generated so that the Bayesian confidence interval (credible interval) for estimation is (0.85; 0.9836), (4.484; 6.439) and (0.2694 ; 0,296) for parameters .
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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Abstract

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%.
Klasifikasi Penyakit Tuberkulosis Menggunakan Metode Naive Bayes (Studi Kasus: Data Pasien Di Puskesmas Petung Kabupaten Penajam Paser Utara) Abidin, Ahmad Aliful; Goejantoro, Rito; Fathurahman, M.
EKSPONENSIAL Vol. 14 No. 1 (2023)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (663.984 KB) | DOI: 10.30872/eksponensial.v14i1.1031

Abstract

The Naive Bayes method is one of the data mining methods used in classifying data and predicting future opportunities based on experience or previous data. This method was proposed by British scientist Thomas Bayes using a branch of mathematics known as probability theory. One of the diseases that can be detected using the classification using the Naive Bayes method is Tuberculous (TB). Tuberculous is an infectious respiratory disease caused by the bacterium Mycobacterium Tuberculosis. The purpose of this study was to determine the results and accuracy of the classification of Tuberculous disease using the Naive Bayes method in one of the health service units, namely Puskesmas Petung, Penajam Paser Utara. The results showed that data mining classification using the Naive Bayes method was appropriate in classifying Tuberculous. For training and testing data, divided into 90:10, the accuracy rate is 87.5%, categorized as Excellent Classification. As for the training and testing data divided into 70:30, the accuracy rate is 90.9%, classified as Excellent Classification.
Penerapan Metode Klasifikasi Multinomial Naive Bayes: (Studi Kasus: PT Prudential Life Samarinda Tahun 2019) Rinaldi, Rival; Goejantoro, Rito; Syaripuddin, Syaripuddin
EKSPONENSIAL Vol. 12 No. 2 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (560.473 KB) | DOI: 10.30872/eksponensial.v12i2.803

Abstract

Life insurance is a risk management service provide payment to policyholders in the event of a disaster that has been stipulated in the agreement. A classification system needs to be done to facilitate the company in making decisions to provide policies to customers. One system that can be used is multinomial Naive Bayes. Multinomial Naive Bayes is a simple probabilistic classification that has more than two groups or categories. An algorithm using Bayes theorem assumes all independent variables. The aim of this study is to obtain an accuracy level of 5 different proportions with the Naive Bayes multinomial method used in insurance customer payment status data. The data used is the customer data of PT. Prudential Life Samarinda in 2019 with the status of current premium payment, substandard and non-current and using 5 independent variables, namely income, age, amount of premium payment, sex and employment. The results of the measurement of classification accuracy using APER status premium payment on insurance customer data of PT. Prudential Life 2019 Naive Bayes multinomial method showed 22,96% misclassification at 50:50 proportion, at the proportion of 60:40 there were 21,43% misclassification, at the proportion of 70:30 there were 19,05% misclassified, at proportions 80:20 had a misclassification of 14,29%, and a proportion of 90:10 has a misclassification of 7,14%.
Penerapan Latent Class Regression Analysis dalam Segmentasi Pasar Musmirani, Musmirani; Purnamasari, Ika; Suyitno, Suyitno
EKSPONENSIAL Vol. 11 No. 1 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (865.292 KB) | DOI: 10.30872/eksponensial.v11i1.644

Abstract

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.
Pemodelan Mixed Geographically Weighted Regression (MGWR) Nur Fajar Apriyani; Desi Yuniarti; Memi Nor Hayati
EKSPONENSIAL Vol 9 No 1 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Diarrhea disease is one of the conditions which a person has soft or liquid defecate consistency, even can be water and frequency more often in one day. The province of East Kalimantan includes areas where the percentage of diarrhea tends to increase annually. Therefore, as one of the efforts to handle cases of diarrhea in East Kalimantan Province, so that the research using Mixed Geographically Weighted Regression (MGWR) model which is a modeling method that combines global regression model and Geographically Weighted Regression (GWR) model. Modeling MGWR aim to find out the factors that affect the number of diarrhea sufferers, where factors are differentiated into factors that affect locally in each District/City and factors that affect globally throughout the District/City. The result of the research using the MGWR method, the variable of the number of households that live clean and healthy and the number of food management places do not meet the criteria affect globally. The number of communal latrine facilities affect locally.

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