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Memi Nor Hayati
Laboratorium Statistika Terapan FMIPA Universitas Mulawarman
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

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Peramalan Menggunakan Time Invariant Fuzzy Time Series Siti Rahmah Binaiya; Memi Nor Hayati; Ika Purnamasari
EKSPONENSIAL Vol 10 No 2 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

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%.
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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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.
Penerapan Model Seasonal Autoregressive Fractionally Integrated Moving Average Pada Data Inflasi di Indonesia Edy Fahrin; Memi Nor Hayati; Meiliyani Siringoringo
EKSPONENSIAL Vol 10 No 2 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

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%.
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%.
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.
Deteksi Pencilan Spasial pada Data Kandungan Klorida di Sungai Mahakam Wilayah Samarinda Kalimantan Timur Muhammad Jainudin; Memi Nor Hayati; Ika Purnamasari
EKSPONENSIAL Vol 10 No 2 (2019): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

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.
Regresi Nonparametrik Spline Birespon Untuk Memodelkan Persentase Penduduk Miskin dan Indeks Kedalaman Kemiskinan di Kalimantan Timur Tahun 2015 Ronald Tediwibawa; Desi Yuniarti; Memi Nor Hayati
EKSPONENSIAL Vol 10 No 1 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

State of Indonesia is a developing country which has a problem, namely poverty. Poverty is a condition that is often associated with the needs, difficulties and shortcomings in the various circumstances of life. Measuring poverty in a region that can be done by looking at two indicators, namely the percentage of the poor population and the poverty depth index. This study uses 5 factors thought to affect the percentage of poor people and the depth of poverty in East Kalimantan which includes the average of the old school, the open unemployment rate, the labor force participation rate, population growth rate and the expectancy of the old school. The Data used in this study is the data year 2015, which is obtained from the Central bureau of Statistics of East Kalimantan Province. The method used is a nonparametric regression spline-response and determine the value of the optimal knots point using the Generalized Cross Validation (GCV). The best Model resulting from this research is the model with the point of optimal knot with the value of GCV of 31.14057 and R-squared of 86.47.
Pemodelan Faktor-Faktor yang Berpengaruh Terhadap Indeks Pembangunan Manusia (IPM) di Kalimantan dengan Geographically Weighted Logistic Regression (GWLR) Lili Widyastuti; Desi Yuniarti; Memi Nor Hayati
EKSPONENSIAL Vol 9 No 1 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Human Development Index (HDI) is an indicator to measure the success in building the quality of human life (community/population) and HDI can be used to see the results of the development. The average of Kalimantan HDI in 2016 has low HDI value however there is also high HDI value. Be observed from the score of those HDI, Kalimantan only has two categories those are medium and high. The statistical method used for determining the IPM model is the Geographically Weighted Logistic Regression (GWLR) method. GWLR is a local form of logistic regression in which geographic factors are considered and it is assumed that the data distributed Bernoulli are used to analyzing spatial data. This research was conducted to know the model of HDI and the factors that influence HDI in Kalimantan with GWLR using Adaptive Bisquare Kernel. The results showed that by using Adaptive Bisquare Kernel there are 56 different models for each district/city with the factors that affect the HDI in Kalimantan in 2016 vary by district/city as follows; the percentage of the poor population, the percentage of open unemployment, the percentage of the population graduated from college.
Co-Authors Desi Yuniarti Edy Fahrin Ika Purnamasari Julia Julia Lili Widyastuti Masrawanti Masrawanti Meiliyani Siringoringo Muhammad Jainudin Nur Fajar Apriyani Ronald Tediwibawa Siti Rahmah Binaiya Sri Wahyuningsih