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Sri Wahyuningsih
Laboratorium Statistika Terapan FMIPA Universitas Mulawarman
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

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Pemodelan Generalized Space Time Autoregressive (GSTAR) Pada Data Inflasi di Kota Samarinda dan Kota Balikpapan Riska Handayani; Sri Wahyuningsih; Desi Yuniarti
EKSPONENSIAL Vol 9 No 2 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

One of the macroeconomic indicators used in the preparation of government’s economicpolicy is inflation. Inflation is a data time series monthly that also is influenced by location effects. Generalized Space Time Autoregressive (GSTAR) is a time series methode that combines time and location effects. The case study is applied of GSTAR for forecasting inflation in two cities in East Kalimantan namely Samarinda and Balikpapan. This research aims to implement GSTAR model to gain forecasting model for inflation data in Samarinda city and Balikpapan city by using method of cross-correlation normalization. The resulting model is GSTAR model GSTAR (2,1) and GSTAR (3,1). The model obtained is not feasible to be used for forecasting, because it does not meet the white noise assumption.
Penerapan Diagram Kontrol Multivariate Exponentially Weighted Moving Variance (MEWMV) Agustina Feni Baransano; Sri Wahyuningsih; Yuki Novia Nasution
EKSPONENSIAL Vol 9 No 2 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Statistical Process Control based on the quality characteristics can be divided into two kinds, namely univariate control chart and multivariate control charts.This study usedMultivariate Exponentially Weighted MovingVariance Control Chart (MEWMV).PDAM Tirta Mahakam in the districts of Kutai Kartanegara is one of the regional companies engaged in the production of drinking water, which is located in Tenggarong, East Kalimantan.In the production process, PDAM Tirta Mahakam always refers to the standard which is set by the government in producing drinking water.The purpose of this study was to determine whetherwater quality characteristics of PDAM Tirta Mahakam in a controlledstate or not by using control charts MEWMV,and to know the the water process capability. From the result of research it can be concluded that by using MEWMV control chart with weight , , and , show that the condition has been statistically in control. Process capability index MCpin multivariateexplains that the process has not been capable in precision with a value of 0,896 or not meet the specifications of the company.
Optimasi Klasifikasi Batubara Berdasarkan Jenis Kalori dengan menggunakan Genetic Modified K-Nearest Neighbor (GMK-NN) Nanang Wahyudi; Sri Wahyuningsih; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 10 No 2 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

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%.
Analisis Model Threshold Generalized Autoregressive Conditional Heteroskedasticity (TGARCH) dan Model Exponential Generalized AutoregressiveConditional Heteroskedasticity (EGARCH) Julia Julia; Sri Wahyuningsih; Memi Nor Hayati
EKSPONENSIAL Vol 9 No 2 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

In the field of finance, Autoregressive Integrated Moving Average (ARIMA) is one of the models that can be used. Financial data usually have a non constant variance error. Thus, Autoregressive Conditional Heterokedasticity (ARCH )model can be used to solve the problem. In addition, it also can be used the development of ARCH model that is Generalized Autoregressive Conditional Heterkadasticity (GARCH) model. The symmetry of residual data can be determined by using the model of Threshold Generalized Autoregressive Conditional Heterkadasticity (TGARCH) and the model of Exponential Generalized Autoregressive Conditional Heterkadasticity (EGARCH). The purpose of this research is to know the best model among the model of TGARCH and the model of EGARCH in predicting Indonesia Composite Index (ICI) and the results of ICI forecasting by using the best model for the period of July 2017 until December 2017. The best model in the ICI case study from January 2011 to June 2017 is the model of ARIMA (1,1,1) -GARCH (1,2) -EGARCH (1). The results of ICI forecasting by using the model of ARIMA (1,1,1) -GARCH (1,2) -EGARCH (1) obtained an upward trend in the period of July 2017 to December 2017.
Peramalan Dengan Metode Fuzzy Time Series Markov Chain Yenni Safitri; Sri Wahyuningsih; Rito Goejantoro
EKSPONENSIAL Vol 9 No 1 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Forecasting is an activity to predict what will happen in the future with certain methods. Fuzzy time series is a method known as artificial intelligence used to predict the problem which the actual data is formed in linguistic values using fuzzy principles as its basis. This study discusses the method of fuzzy time series developed by Ruey Chyn Tsaur to predict the closing price of the shares of PT. Radiant Utama Interinso Tbk April 2017. Markov Chain fuzzy time series method is used to analyze a time series data which is a combination of fuzzy time series model with Markov Chain. Forecasting of closing stock price based on data from January 2011 to March 2017 for April 2017 is Rp 224,29,00. Markov Chain's fuzzy time series method to forecast the closing stock prices data from January 2011 to March 2017 has a 3,48% of MAPE value or has a 96,52% of precision forecast. The results show that the Markov Chain fuzzy time series has an excellent level of accuracy for forecasting the closing stock prices.
Analisis Model Intervensi Fungsi Step Ganda untuk Peramalan Inflasi Indonesia Masrawanti Masrawanti; Sri Wahyuningsih; Memi Nor Hayati
EKSPONENSIAL Vol 10 No 2 (2019): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

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%.
Diagram Kontrol Short-Run untuk Memantau Variabilitas Proses Budi Nugroho; Desi Yuniarti; Sri Wahyuningsih
EKSPONENSIAL Vol 10 No 1 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Short run control chart is designed for production small scale and a limited amount data for monitoring different characteristic on same diagram control. The purpose of research is to know the implementation of short run control chart for monitoring mean and variability process based on characteristic data 1 inch Polyvinyl Chloride (PVC) in PT. Maspion. The objective of characteristics 1 inch pipe in this research are socket external diameter, socket internal diameter, barrel external diameter and barrel internal diameter. The Results showed that controlling process of by applying influence function, effective is to be applied to detect small movement in observation 11, 14, 14 and 9 for every control chart characteristic limited control 3σ.
Prediksi Data Curah Hujan Dengan Menggunakan Statistika Non Parametrik Gracia Indah Fajarini; Ika Purnamasari; Sri Wahyuningsih
EKSPONENSIAL Vol 9 No 2 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Rainfall data analysis is the first stage of a water resource planning. One of rainfall data analysis method is using rain frequency analysis. In this research, rainfall frequency analysis is used to prediction the probability of occurrence from hydrological event. The maximum monthly rainfall frequency distribution is affects to rainfall during high repeat periods. Rainfall is the amount of water that falls on a flat surface during certain repetitive periods. Secondary data is got from Temindung Station of Samarinda City on 2007 to 2016. The type of distribution are used Normal, Gumbel, Log Pearson Type III, and Log Normal. Compatibility test of Non Parametric Statistics using Chi Square method. The results showed if the estimated rainfall at the highest repeating period of 2, 5, 10, 25, 50, and 100 years is Log Normal distribution. The distribution that requirement of qualify criteria is Log Normal and Gumbel distribution. The distribution that fit from Chi Square test is Gumbel distribution is 3,5177 and Log Normal distribution is 6,8945. From Kolmogorov Smirnov test the value of Gumbel distribution is 0, and Log Normal distribution is 0,0805. Rainfall patterns for Normal distribution, Gumbel distribution, Pearson Log distribution Type III and Log Normal distribution are horizontal patterns.
Peramalan Harga Minyak Mentah Menggunakan Model Autoregressive Integrated Moving Average Neural Network (ARIMA-NN) Laila Nur Qamara; Sri Wahyuningsih; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 10 No 2 (2019): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Crude oil prices can affect the production and consumption of a country. Crude oil prices are set every month and semester by Indonesian Crude Oil Price(ICP). Bontang Return Condensate (BRC) is one of crude oil type in Indonesia. Forecasting is an expectation of a request or thing that will come based on several forecasting variables.In this study, data was used in July 2010-December 2017. The purpose of this study was to determine the best model of Indonesian crude oil price data for the BRC type and the forecasting results. The model used in this study is the ARIMA-NN model which is a combination of ARIMA model and Neural Network (NN) model. The best ARIMA-NN model has ARIMA (2,1,0) and NN components with 2 inputs and 2 neurons in the hidden layer. The NN model is a Feed Forward Neural Network (FFNN) model with backpropagation algorithm. The results of the forecasting of the BRC Indonesia crude oil price for January-December 2018 are around the value of 60 USD / Barrel.
Analisis Survival pada Data Kejadian Bersama Menggunakan Metode Exact Partial Likelihood Rahmawati Isnaeni; Yuki Novia Nasution; Sri Wahyuningsih
EKSPONENSIAL Vol 9 No 2 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Survival data is the data of survival time until the appearance of certain events. In the survival analysis, ties are sometimes found, that is the situation where there are two or more individuals who experience the same event at the same time. There are several methods in estimating the parameters in the case of a ties, one of which is by applying the exact partial likelihood method. The exact method is the most accurate method, which can be applied in estimating Cox regression parameters from traffic accident data of Samarinda City in 2016. Traffic accidents are one of the most deadly events. The four main factors that cause traffic accidents are human factors, vehicles, roads, and weather or environmental factors. The variables used in this study are age, gender, role of victim, driver’s license, vehicle type, time of incident, line of the road, and weather. The results of analysis with the help of Rstudio software showed that the factors whose affect the fatality rate of traffic accident victims of Samarinda City are age and gender. For the age variables concluded that each addition of one year of age of the accident victim, the risk of dying from a traffic accident will also increase 1,0258 times. As for the gender variables concluded that the victim of male sex has a risk of 0.4180 times greater to die due to traffic accidents compared with female victims.
Co-Authors Agustina Feni Baransano Budi Nugroho Desi Yuniarti Fidia Deny Tisna Amijaya Gracia Indah Fajarini Ika Purnamasari Julia Julia Laila Nur Qamara Masrawanti Masrawanti Memi Nor Hayati Nanang Wahyudi Rahmawati Isnaeni Riska Handayani Rito Goejantoro, Rito Yenni Safitri Yuki Novia Nasution