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Meiliyani Siringoringo
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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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Penerapan Metode Complete Linkage dan Metode Hierarchical Clustering Multiscale Bootstrap Lisda Ramadhani; Ika Purnamasari; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 9 No 1 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (566.958 KB)

Abstract

Cluster analysis is an analysis that has a purpose to grouping the data (object). The multiscale bootstrap method in cluster analysis is used as a manner for looking at the validity from the result of cluster analysis. The working process of multiscale bootstrap in cluster analysis is taking a sample that has been bootstrapped and then take the one of bootstrap resampling result that has been reputed to represent the distribution in East Kalimantan 2016. The purpose of this research is looking at the result of data agglomeration in poverty indicator in East Kalimantan 2016 in using a multiscale bootstrap method that produces four cluster types. The first cluster consists of two regencies/cities who has the low percentage of poverty indicator 49,32%. Additionally, the second cluster contains of five regencies/cities with the high percentage of poverty indicator 53,39%. In addition, the third cluster involves of two regencies/cities with the percentage of poverty indicator in high sufficient 51,46%. Finally, the fourth cluster consists of a regency/city which has a percentage of poverty indicator low adequate 51,02%.
Peramalan Harga Minyak Mentah Dunia (Crude Oil) Menggunakan Metode Radial Basis Function Neural Network (RBFNN) Ayu Wulandari; Sri Wahyuningsih; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 8 No 2 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Forecasting is a technique to estimate a value in the future with past data and current data. One of the forecasting method that includes neural network is Radial Basis Function Neural Network (RBFNN). In this research, RBFNN method is used to get the best model and to forecast world crude oil price (US$) data. World crude oil prices forecasting is very important for many stakeholder, both from the government sector, business entities and investors so that all activities can go according to plan. In the RBFNN method, the network input and the number of hidden layers is very influential to get the best model from RBFNN and also the forecasting. To get the best model by using network input determination by identifying the Partial Autocorrelation Function (PACF) lag, and to determine the number of hidden layers by the K-Means cluster method. Results of the research showed that from the training data, the best model of RBFNN is using 2 network inputs Xt−1 and Xt−2 and 3 hidden layers with Mean Absolute Percentage Error (MAPE) accuracy level is 6,8150%. With the model, for the next period from June 2017 to December 2017 the world crude oil price (US $) shows a downward trend.
Peramalan dengan Menggunakan Metode Holt-Winters Exponential Smoothing: Studi Kasus: Jumlah Wisatawan Mancanegara yang Berkunjung Ke Indonesia Aryati, Ayu; Purnamasari, Ika; Nasution, Yuki Novia
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 (591.792 KB) | DOI: 10.30872/eksponensial.v11i1.650

Abstract

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
Penerapan Metode Fuzzy Subtractive Clustering Nur Azizah; Desi Yuniarti; Rito Goejantoro
EKSPONENSIAL Vol 9 No 2 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Cluster analysis is a statistical analysis to classify the objects to be some clusters based on checked variables and similarity of character between the objects. Quality of human living or society has been influenced by many things. In reality, population density is very influential to the quality of human living because high population density will cause many problems that impact on deterioration of quality of human living. Fuzzy Subtractive Cluster (FSC) methods using the data as a candidate of cluster center, so that duty of computation is hanging on the number of data and is not hang at dimension of data. This study aims is to determine the results of FSC at clustering the district in East Borneo based on wide of the district and total of population in 2015. The result shows there is 8 until 24 districts which have high population density. From validity of cluster, it isfounded that the best result for clustering the district in East Borneo based on wide of the district and sum of citizen in 2015 is 2 clusters, there are narrow district with many citizen and wide district with few citizen.
Penggunaan Metode Seven New Quality Tools dan Metode DMAIC Six Sigma Pada Penerapan Pengendalian Kualitas Produk Yurin Febria Suci; Yuki Novia Nasution; Nanda Arista Rizki
EKSPONENSIAL Vol 8 No 1 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Product quality control is a technique and activities or planned actions undertaken to achieve, maintain, and improve the quality of products and services to meet with customers standards and satisfaction. This study aim to address the product quality at a company using statistical methods of products control. The methods are Seven New Quality Tools and DMAIC Six Sigma which are used on a product with a brand of Roti Durian Panglima, produced by PT. Panglima Roqiiqu Group in June 2016. Based on the result by using Seven New Quality Tools method, there are five factors that caused defect on Roti Durian Panglima product, which are : human factor, materials, environmental, machine, and work method, which makes the priority of the product improvement lays on human factor. Meanwhile, the use of DMAIC Six Sigma method has obtained performance baseline values at 4,48 Sigma with four kinds of defects on Roti Durian Panglima products, and based on improvement phase using PFMEA method, the priority on product improvement also lays on human factor.
Penerapan Generalized Poisson Regression I Untuk Mengatasi Overdispersi Pada Regresi Poisson Iim Masfian Nur; Desi Yuniarti; Memi Nor Hayati
EKSPONENSIAL Vol 7 No 1 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Poisson Regression model is commonly used to analyze count data is assumed to have Poisson distribution where the mean and variance values are equal or also called equdispersion. In fact, this assumption is often violated, because the value of variance is greater than the mean value, this condition is called overdispersion. Poisson regression which is applied to the data that contains overdispersion will imply the value of standard error becomes underestimates, so the conclusion is not valid. One of the models that can be used for overdispersion data is Generalized Poisson Regression I (GPR I). This research discuss the handling of overdispersion on Poisson regression using GPR I, with case study modeling the number of cervical cancer cases in East Kalimantan in 2013. In this research GPR I models meet the criteria for suitability of regression compared Poisson regression models because it has a smaller AIC value.
Perbandingan Metode Klasifikasi Naive Bayes dan K-Nearest Neighbor pada Data Status Pembayaran Pajak Pertambahan Nilai di Kantor Pelayanan Pajak Pratama Samarinda Ulu Rahmaulidyah, Fatihah Noor; Hayati, Memi Nor; Goejantoro, Rito
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 (641.425 KB) | DOI: 10.30872/eksponensial.v12i2.809

Abstract

Classification is a systematic grouping of objects into certain groups based on the same characteristics. The classification method used in this research are naive Bayes and K-Nearest Neighbor which has a relatively high degree of accuracy. This research aims to compare the level of classification accuracy on the status data of value-added tax (VAT) payment. The data used is data on corporate taxpayers at Samarinda Ulu Tax Office in 2018 with the status of VAT payment being compliant or non-compliant and used 3 independent variables are income, type of business entity and tax reported status. Measurement of accuracy using APER in the Naive Bayes method is 17.07% and in K-Nearest Neighbor method is 19,51%. The comparison results of accuracy measurements between the two methods show that the naive Bayes method has a higher level of accuracy than the K-Nearest Neighbor method
Analisis Value At Risk Portofolio Saham Menggunakan Metode Varian-Kovarian Nur Rizki Wahidah; Yuki Novia Nasution; Nanda Arista Rizki
EKSPONENSIAL Vol 9 No 2 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Investing is a human effort to save a certain amount of money in time, in the hope of gaining some profit in the future. Investment decisions are fundamentally related to the management of funds in a given period, in which investors have hope to earn income or profit from the funds invested. Almost all investors do not want losses when investing. Various ways are done to avoid loss, or at least maximize profits with minimal risk. The value of risk that is often used is Value At Risk (VaR). Values ​​At Risk (VaR) is one of the statistical tools used to measure the maximum loss of an asset or investment over a certain period with a certain degree of confidence to reduce the occurrence of the risk. This study aims to determine how the risk of stock portfolio of PT. Astra Agro Lestari Tbk (AALI) and PT.PP London Sumatra Indonesia Tbk (LSIP) use Value at Risk analysis using Varian-Covariance method at closing price of shares incorporated in Jakarta Islamic Index (JII) and Asset Value at Risk PT. Astra Agro Lestari Tbk (AALI) and PT.PP London Sumatra Indonesia Tbk (LSIP) to Value at Risk Portfolio. The results showed that if the initial fund invested to PT. Astra Agro Lestari Tbk. and PT.PP London Sumatra Indah Tbk. Rp. 10.000.000, - with a 95% confidence level obtained Value at Risk (VaR) of Rp. 369.682. this can be interpreted there is a 95% confidence that the losses received by investors will not exceed from Rp. 369.682..The result of PT. Astra Agro Lestari tbk. against portfolio risk at 6% and PT. PP London Sumatra Indonesia Tbk. of portfolio risk is 46%.
Metode Regresi Robust Dengan Estimasi Method of Moment (Estimasi-MM) Pada Regresi Linier Berganda Hisintus Suban Hurint; Ika Purnamasari; Memi Nor Hayati
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (556.811 KB)

Abstract

Method of Ordinary Least Square (OLS) on the regression analysis is a method which is often used to estimate the parameters. In the OLS method, there are several assumptions that must be fulfilled, these assumptions are often not fulfilled when the data contains outlier, so need a method that are robust to the presence of outliers. In this research, studied method of robust regression with MM-estimation. MM-estimation is a combination of estimation methods that have a high breakdown point, namely the Scale estimation(S-estimation) and Least Trimmed Square estimation (LTS estimation) and the method that have higher efficiency point, namely the Maximum Likelihood Type estimation (M-estimation). The first step in the MM-estimation is to find the S-estimator, then set the parameter regression using the M-estimation. The purpose of this study was to determine the effect of price index of foodstuffs ( ), the price index of education ), and the price index of health ) to the CPI for the province of east borneo, where the CPI data contains outliers, namely observation to 13, 31,and 32.
Peramalan Kredit Modal Kerja di Indonesia Menggunakan Brown's Double Exponential Smoothing dengan Optimasi Pencarian Dikotomis Yustiani, Iis; Wahyuningsih, Sri; Siringoringo, Meiliyani
EKSPONENSIAL Vol. 13 No. 2 (2022)
Publisher : 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

Abstract

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%.

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