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Jurnal Gaussian

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Published by Universitas Diponegoro

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Jurnal Gaussian terbit 4 (empat) kali dalam setahun setiap kali periode wisuda. Jurnal ini memuat tulisan ilmiah tentang hasil-hasil penelitian, kajian ilmiah, analisis dan pemecahan permasalahan yang berkaitan dengan Statistika yang berasal dari skripsi mahasiswa S1 Departemen Statistika FSM UNDIP.

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Search results for , issue "Vol 4, No 4 (2015): Jurnal Gaussian" : 40 Documents clear

PERAMALAN BEBAN PEMAKAIAN LISTRIK JAWA TENGAH DAN DAERAH ISTIMEWA YOGYAKARTA DENGAN MENGGUNAKAN HYBRID AUTOREGRESIVE INTEGRATED MOVING AVERAGE – NEURAL NETWORK Berta Elvionita Fitriani; Dwi Ispriyanti; Alan Prahutama
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Excessive use of electronic devices in household and industry has made the demand of nation’s electrical power increase significantly these days. As a corporation that aim to provide national electrical power, Perusahaan Listrik Negara (PLN) that distributes electrical power to Central Java and Yogyakarta has to be able to provide an economical and reliable system of electrical power provider. This study aimed to forecast data of electrical power usage in Central Java and Yogyakarta for the next 30 days. There were three forecasting methods used in this study; Neural Networks and Hybrid ARIMA-NN. The data used in this study was electrical power usage data in January 2014 - November 2014 in Central Java and Yogyakarta. The accuracy of the study was measured based on MSE criteria where the best model chosen was the model that has lowest MSE value. According to the result of the analysis, using Neural Networks model to forecast electrical power usage for the next 30 days has better forecasting result than Hybrid ARIMA-NN model.Key Word : electrical power usage, forecasting of electrical power usage, ARIMA, NN, hybrid ARIMA-NN

KLASIFIKASI KELOMPOK RUMAH TANGGA DI KABUPATEN BLORA MENGGUNAKAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) DAN FUZZY K-NEAREST NEIGHBOR (FK-NN) Yani Puspita Kristiani; Diah Safitri; Dwi Ispriyanti
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Good classification method will result on less classification error. Classification method developed rapidly. Two of the existing classification methods are Multivariate Adaptive Regression Spline (MARS) and Fuzzy K-Nearest Neighbor (FK-NN). This research aims to compare the classification of poor household and prosperous household based on per capita income which has been converted according to the poverty line between MARS and FK-NN method. This research used secondary data in the form of result of National Economy and Social Survey (SUSENAS) in Blora subdistrict in 2014. The result of the classification was evaluated using APER. The best classification result using MARS method is by using the combination of BF= 76, MI= 3, MO= 1 because it will result on the smallest Generalized Cross Validation (GCV) and the APER is 10,119 %. The best classification result using FK-NN method is by using K=9 because it will result on the smallest error and the APER is 9,523 %. The APER calculation shows that the classification of household in Blora subdistrict using FK-NN method is better than using MARS method. Keywords: Classification, MARS, FK-NN, APER, SUSENAS, Blora

PERBANDINGAN METODE REGRESI LOGISTIK BINER DAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) PADA PEMINATAN JURUSAN SMA (Studi Kasus SMA Negeri 2 Semarang) Ratih Binadari; Yuciana Wilandari; Suparti Suparti
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Major specialization at High School is aimed to gives opened opportunity for students to choose subject that are interest and develop their potential in accordance with the abilities, interests, talents, and personality. Major specialization at High school is influenced by some factors. To detect those factors, used biner logistic regression method and Multivariate Adaptive Regression Spline (MARS). Biner Logistic Regression is method that describes relationship between dependent variable and some independent variable, with independent variable has been coded 1 as representing the presence of the characteristic, and 0 as representing the absence of the characteristic. MARS is multivariate nonparametric regression method that development of Recursive Partitioning Regression (RPR) method and Spline method for high dimensional data that produces accurate prediction and continuous models on knots. Both of the methods are compared to know the best method used in research. From the result of analysis using biner logistic regression method and MARS, concluded that major specialization has been influenced by mathematic score, science score and relationship between students and friends. From proportion test, concluded classification that formed by regression logistic is as good as by MARS. Keywords : Major specialization at High School, Biner Logistic Regression, Mutlivariate Adaptive Regression Spline (MARS), Clasification

PROYEKSI DATA PRODUK DOMESTIK BRUTO (PDB) DAN FOREIGN DIRECT INVESTMENT (FDI) MENGGUNAKAN VECTOR AUTOREGRESSIVE (VAR) Indra Satria; Hasbi Yasin; Suparti Suparti
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Gross Domestic Product (GDP) and Foreign Direct Investment (FDI) is an economic instrument that has an attachment and often used for economic development of a country. To predict these two variables there are several methods that can be used, one of which is a method of Vector Autoregressive (VAR). VAR method has some assumptions that the data to be foreseen must have an attachment, stationary in the mean and variance and the resulting error must meet the test of independence and normal distribution. In the early stages of identification done by considering the value of AIC as a determinant of the optimal lag value, which in this case lag 4 who came out as the optimal lag. Granger causality test as an attachment test between variable and Augmented Dickey Fuller test (ADF) as a stationary test. In the parameter estimation phase used Ordinary Least Square method (OLS) to determine the values of the parameters to be used as a model. After getting the model it is necessary to do verification on condition that the residuals must comply with the independence test and multivariate normal test. With a second fulfillment verification test is carried out projections for the next 5 years with a value of R-Square 64% to GDP and 48% for the variable FDI Keywords: FDI, GDP, VAR, causality, independency, multivariate normal, R-Square

ESIMASI PARAMETER REGRESI RIDGE MENGGUNAKAN ITERASI HOERL, KENNARD, DAN BALDWIN (HKB) UNTUK PENANGANAN MULTIKOLINIERITAS Nur Aeniatus Solekakh; Dwi Ispriyansti; Sudarno Sudarno
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Regression analysis is statistical method used to analyze the dependence of respond variables to predictor variable. In multiple linear regression analysis, there are assumptions that must be met, they are normality, homoscedasticity, absence of multicollinearity, and absence of autocorrelation. One of assumption frequently found is multicollinearity. If multicollineraity is exist between predictor variables, then regression analysis with ordinary least square is no longer used. Ridge regression is regression method to handle multicollinearity. The ridge estimator involves adding biasing constant (k) to each diagonal element of X’X. Biasing constant (k) is determined by Hoerl, Kennard, and Baldwin (HKB) iteration method. This regression can be applied to inflation rate in Indonesia data and the factors that influence, they are BI rate, money supply, and exchange rate of rupiah. Ridge regression analysis, the VIF (Variance Inflation Factor) values for each predictor variables BI rate, money supply, and exchange rate of rupiah are 1.6637, 3.2712, and 4.3309. SinceVIF values are not exceed to 10, then there is no multicollinearity in ridge regression model.Keywords: Inflation, Multikollinearity, Ridge Regression, HKB Iteration, VIF

PERAMALAN INDEKS HARGA KONSUMEN MENGGUNAKAN MODEL INTERVENSI FUNGSI STEP Dita Ruliana; Sugito Sugito; Dwi Ispriyanti
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Intervention model is a model for time series data in which practically there is an extreme fluctuation, whether it’s anupward or downward fluctuation. Consumer price index is one of economic data which plot has a fluctuation, the data that will being used for analyze is consumer price index of Indonesia in January 2009 until March 2015, on data detectable downward fluctuation significantly on January 2014 (T=61). Intervention in data was occurred in long time period (T=61 until T=75), so the model of intervention’s assumption is step function. Based on the result and analysis, the obtaining best model of intervention is ARIMA (2,1,3) with intervention order b=0 s=15 and r=0 which later on being used for predicting Indonesian consumer price index in six periods ahead. Keywords : consumer price index, stationery, ARIMA, step function intervention analysis, forecasting

PERBANDINGAN MODEL ARIMA DAN FUNGSI TRANSFER PADA PERAMALAN CURAH HUJAN KABUPATEN WONOSOBO Siti Lis Ina Atul Hidayah; Agus Rusgiyono; Yuciana Wilandari
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Rainfall is one of the things that affect agricultural production. The highest amount of rainfall will cause perturbation in the pollination of flowers and caused zalacca palm to produce fruits no season of the year. Zalacca palm is growing well in heavy rainfall area.. There are some factors which influence rainfall; those are: humidity, solar energy, wind direction and velocity as well as air temperature. The application of ARIMA (Autoregressive Integrated Moving Average) and multi input transfer function was intended to model the rainfall which would be forecasted based on the best model chosen. There were two kinds of variables used in this study. Those were rainfall as the output series while humidity and air temperature as the input series during January 2009 to October 2014. The result showed that ARIMA ([3], 1, [12]) had a fewer Schwart’z Bayesian Criterion (SBC) value 293.199 than multi input transfer function model (0,0,0) (0,1,0) with the result 906.9632.Keywords: Rainfall, ARIMA, Transfer Function

METODE LENTH PADA RANCANGAN FAKTORIAL FRAKSIONAL 3^(k-p) DENGAN ESTIMASI EFEK ALGORITMA YATES Mutiara Ardin Rifkiani; Rita Rahmawati; Abdul Hoyyi
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Factorial design often is used in experiments on various fields to identify the influence of main factors and interaction factors to respons were observed. A design which has k factors with three levels for each factor called factorial design. For a large number of factors, fractional factorial design is an effective alternative because it has less combination of treatment than factorial design, but it still has important needed information. In experiments conducted without repetition, determining factors that influence towards response is difficult to be analyzed if using analysis of variance. It was due to the the average of squared error absence, where error variance estimation is based on the variability of the data obtained from repeated observations. To overcome this, we use Lenth Method to identify the factors that affect the response. Lenth method uses the value of the statistic margin of error (ME) test for the main factor, and simultaneous margin of error (SME) for the interaction factor. The calculation of the statistic test ME and SME values are based on the estimated effects of each treatment. Yates algorithm is used to calculate the effect’s estimation for each treatment. To clarify the discussion about this matery is given an example of fractional factorial design application with 27 experiments on combustion boiler. The results indicate that treatment factors are influenced towards the response are , , , dan . Keywords: three-level fractional factorial, factorial without replication, Lenth Methods, Yates Algorithm

PEMODELAN PERTUMBUHAN EKONOMI JAWA TENGAH MENGGUNAKAN PENDEKATAN LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR (LASSO) Feby Kurniawati Heru Prabowo; Yuciana Wilandari; Agus Rusgiyono
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The economic growth recently become more important because of its implementation widely, the economic growth concept is a measure of country or regional economy valuation. The economic growth data in this research that is measured by Gross Regional Domestic Product (GRDP) are susceptible of multicollinearity. Multicollinearity become a problem in regression analysis, especially in Ordinary Least Square (OLS) because it causes the regression coefficient estimates become not efficient. One of method to overcome multicollinearity is using Least Absolute Shrinkage and Selection Operator (LASSO). LASSO is a shrinkage method to estimate regression coefficients by minimazing residual sum of squares subject to a constraint. Because of that constraint, LASSO can shrinks coefficients towards zero or set them to exactly zero so it can do variable selection too. Based on Variance Inflation Factor (VIF), there are high correlations between predictor variables, so there is multicollinearity in growth economic data of Jawa Tengah 2013 if we use OLS. In this research, LASSO shrinks eleven coefficients estimator of predictor variables to exactly zero, so that variables considered to have not a significant influence toward model. Keywords : LASSO, Multicollinearity, Shrinkage, Gross Regional Domestic Product (GRDP)

ANALISIS PROCRUSTES PADA INDIKATOR INDEKS PEMBANGUNAN MANUSIA (IPM) DI KABUPATEN/KOTA PROVINSI JAWA TENGAH (STUDI KASUS IPM TAHUN 2008 DAN 2013) Bunga Maharani; Moch. Abdul Mukid; Suparti Suparti
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Human Development Index (HDI) as a measure of development in the performance of a whole formed through the approach of the four indicators which is real expenditure per capita, the average length of school, literacy rates and life expectancy. To learn about how the in contributing each indicators need to be identified the changes that occurred. Of the changes can use these as an ingredient of analysis in order to cope with or reduce the problems of development to realize the quality sustainably. Hence, this study aims to know of the changes of HDI in Central Java by mapping position of districts there are into a map geometry resulting from metric multidimensional scaling analysis. Where in 2008 as the beginning of leadership and 2013 as the end of leadership of Provincial Governor of Central Java Mr. Bibit Waluyo for five years served. By using an analysis procrustes, obtained the results that in the early period and the end of having the consistency of 90,53 %. In other words, the similarity of the large this indicates that at the beginning and end leadership of relatively no change.Keywords: Indicator, Map geometry, Metric multidimensional scaling, Changes analysis procrustes.

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