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Rito Goejantoro
Dosen Program Studi Statistika FMIPA Universitas Mulawarman
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

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Estimasi Parameter Model ARIMA untuk Peramalan Debit Air Sungai Menggunakan Least Square dan Goal Programming Dewi Wulan Sari; Rito Goejantoro; Sri Wahyuningsih
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Forecasting is a technique to make a desicion in the future considered by data from the past and present. This forecasting is in hydrology sector which is river flow forecasting. River flow forecasting is one way to anticipate the instability of the river flow. The aim of this research was to determine the best ARIMA model based on analysis of the river flow of Karang Mumus, Samarinda. This research will explain the procedure of ARIMA model building using the Least Square and Goal Programming to predict the river flow of Karang Mumus, Samarinda. The data used montly from January until December. The model of ARIMA (2,1,2)to predict the river flow of Karang Mumus using Goal Programming is : Zt=μ-0,0492Zt-1-0,0523Zt-2-0,9969Zt-3+0,9247at-1+0,9339at-2+at ARIMA (2,1,2) for river flow forecasting using Goal Programming is : Zt=1,17Zt-1-0,17Zt-2+at+0,31at-1 The best ARIMA model for river flow forecasting of Karang Mumus is ARIMA (2,1,2) using Least Square method. Result for river flow forecasting of Karang Mumus river in Samarinda from January until Desember 2015 are 1.733 m3, 1.729 m3, 1.730 m3, 1.730 m3, 1.729 m3, 1.730 m3, 1.732 m3, 1.729 m3, 1.730 m3, 1.732 m3, 1.729 m3, dan 1.730 m3.
Pemodelan Geographically Weighted Regression (GWR) Dengan Fungsi Pembobot Tricube Terhadap Angka Kematian Ibu (AKI) Di Kabupaten Kutai Kartanegara Tahun 2015 Muhammad Rahmad Fadli; Rito Goejantoro; Wasono Wasono
EKSPONENSIAL Vol 9 No 1 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Maternal Mortality in Kutai Kartanegara is a geographical problem that suspected affected by geographical factor which the global regression cannot model the relation well between the main problem and its independent variable. Therefore, Geographically Weighted Regression (GWR) is used to solve it. Spatial statistics is a method for analyzing data that has spatial correlation. GWR Model is the locally of global regression which considering the geographical or location as the weighted function for estimating the parameters of models. The tricube weighted function is used for the weighting. From this study, the models are different from location to others with also has the independent variables. For Samboja, Muara Jawa, Sanga-Sanga, Anggana, Muara Badak, Marang Kayu, and Tabang which are not affected by the indicators. Loa Janan, Loa Kulu, Muara Muntai, Kota Bangun, Tenggarong, Sebulu, Tenggarong Seberang, Muara Kaman, and Kenohan have the Maternal Mortality affected by Hospital Ratio per 1.000 Pregnant Mothers (x1). Muara Wis, Kenohan, dan Kembang Janggut have the Maternal Mortality affected by Childbirth with Medical Help (x2). Muara Muntai, Muara Wis, Kota Bangun, Sebulu, Tenggarong, Muara Kaman, Kenohan, and Kembang Janggut have the Maternal Mortality affected by Health Care of Childbed (x4).
Peramalan Regarima Pada Data Time Series Yudha Muhammad Faishol; Ika Purnamasari; Rito Goejantoro
EKSPONENSIAL Vol 8 No 1 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

RegArima method is a modelling technique that combines the ARIMA model with a regression model which uses a dummy variable called regressors or variable regression. The purposes of this study was to determine the calendar variation models and application of the model to predict plane ticket sales in January 2016 - December 2017. Based on the data analysis show that ticket sales have seasonal pattern, ie an increase in ticket sales when Idul Fitri. First determine the regressors which is only affected by one feast day is Eid. Then do the regression model, where the dependent variable (Y) is the volume of plane ticket sales and the independent variable (X) is regressors, so the regression model is Ŷt=1.029+1.335 X. The results of analysis show that all parameters had significant regression model and then do a fit test the model, the obtained residual normal distribution and ineligible white noise, which means that it still contained residual autocorrelation. ARIMA modeling is then performed on the data regression residuals. Results of analysis performed subsequent residual own stationary ARIMA model estimation and obtained ARIMA (0,0,1) with all parameters of the model was already significant and conformance test models had also found and that the residual qualified white noise and residual normal distribution. So the calendar variation model was obtained by the method RegARIMA: Yt = 1.029,5 + 1.337,3 Dt + 0,28712 at-1 + at. Based on the model of those variations could be predicted on plane ticket sales for January 2016-December 2017.
Peramalan Dengan Menggunakan Metode Double Exponential Smoothing Dari Brown Etri Pujiati; Desi Yuniarti; Rito Goejantoro
EKSPONENSIAL Vol 7 No 1 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Consumer Price Index (CPI) is one of the economic indicator that givethe information about the price of goods andservices which paid by consumer. CPI in Samarinda City increases so long which the pattern of the data is indicating a trend pattern. Time series forecasting designed to handle the trend of data which used a double exponential smoothing method. The purpose of this study is to determine the using of the parameters α and the forecasting amount of CPI in Samarinda City for three months that use double exponential smoothing method. The best parameter α which use to forecast CPI in Samarinda City is (0,61). To forecast CPI in Samarinda City is using double exponential smoothing method obtained F72+m=119,83+1,62 m. The forecasting result of CPI in Samarinda City from January to March 2015 are 121,44, 123,06, and 124,68.
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

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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.
Penerapan Metode If-Then dari Rough Set Theory dalam Menangani Kecelakaan Lalu Lintas di Kota Samarinda Tahun 2016 Martua Tri Januar Sinaga; Rito Goejantoro; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 8 No 2 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Traffic accidents have caused many victims and lost materials, so itbecomes one of casesneed special attention every year. Therefore, it required a serious treatment to avoid the incidence of traffic accidents, so it can reduce the number of victimsbe inflicted. The aim ofthis study to determine the greatest factor/conditioncausing the fatality rate of traffic accidents and to determine the rules of decision rules from data that has been collected. The data used was secondary data taken from the report of traffic accidents recapitulation at Laka Lantas Unit, Satlantas Samarinda City. The analytical methods used to analyze the data are descriptive statistics analysis and Rough Set Theory. Based on the result, it can be seen the largest frequency of the victim who died is in traffic accidents that occur in sunny conditions. Moreover it is obtained 53 decision rules from the fatalities of victims by the traffic accidents in Samarinda City. The most powerful rule is "if a male student involved in a traffic accide nt at residential area and the road condition feasible passed by vehicles then the victim is likely to get serious injuries" with weight of 0.80.
Perbandingan Peta Pengendali Rata-rata Bergerak Dengan Peta Pengendali Rata-rata Bergerak Geometrik Nurdayanti Nurdayanti; Darnah Andi Nohe; Rito Goejantoro
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

The Moving Average Control Chart is a control chart of data observation for small average shift process. The Geometric Moving Average Control Chart is a control chart of specific weight, making it more effective in detecting the smallest change in process. The purpose of this study is to determine whether the wood width which produced by Suryadi Moulding are controlled by Moving Average and Geometric Moving Average Control Chart, and between the two control chart the research want to know which is the best chart.Based on the results of research in the wood width data obtained that the Moving Average and Geometric Moving Average Control Chart there are no points on the outside of the control limits so that it can be concluded that the wood width which produced by Suryadi Moulding Samarinda on the under controlled conditions. If viewed from the width limit controller chart because of the wide limit on the Geometric Moving Average Control Chart is better than the Moving Average Control Chart because the wide limit on the Geometric Moving Average Control Chart is narrower so the result of this control chart is more accurate.
Pemodelan Geographically Weighted Regression (Gwr) Dengan Fungsi Pembobot Adaptive Kernel Bisquare Untuk Angka Kesakitan Demam Berdarah di Kalimantan Timur Tahun 2015 Aditiya Risky Tizona; Rito Goejantoro; Wasono Wasono
EKSPONENSIAL Vol 8 No 1 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Dengue Fever in East Borneo is thought to be a spatial problem that affected by geographic factor and linear regression analysis that is often can not describe with Good Relations pattern. The solution for this problem can be solved using Geographic Weighted Regression Method (GWR) to review and Troubleshooting geographic factor. This research Model proposed to consider GWR model with geography factor or location as the weight to estimate the model parameters, the weight type that used for this research is Adaptive Bisquare. Based on the analysis, this research revealed different model to every observations and different indicators. The eight locations are Paser, Kutai Kartanegara, West Kutai, East Kutai, Berau, Balikpapan, Samarinda dan Bontang. Those locations have variable that affected the morbidity number of dengue fever equally specifically house, elementary school facilities and public place that do not meet the requirements of health, and also waste transported while for the observation location of Penajam Paser Utara has the affected variable of dengue fever morbidity number equally which are house, waste transported, elementary school facilities and public place that do not meet the requirements of health, and also the citizen that do not have the healthy and hygienic lifestyle pattern.
Co-Authors Aditiya Risky Tizona Darnah Andi Nohe Desi Yuniarti Dewi Wulan Sari Etri Pujiati Fidia Deny Tisna Amijaya Ika Purnamasari Martua Tri Januar Sinaga Muhammad Rahmad Fadli Ni Wayan Rica A Nurdayanti Nurdayanti Sri Wahyuningsih Wasono Wasono Yudha Muhammad Faishol