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Jurnal Gaussian

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Published by Universitas Diponegoro

ISSN : - EISSN : 23392541 DOI : -

Core Subject : Education,

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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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Articles 733 Documents

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PERBANDINGAN MODEL GWR DENGAN FIXED DAN ADAPTIVE BANDWIDTH UNTUK PERSENTASE PENDUDUK MISKIN DI JAWA TENGAH Pamungkas, Rifki Adi; Yasin, Hasbi; Rahmawati, Rita
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Regression analysis is statistical method for modeling the dependency relationship that might exist among the dependent variable with independent variable. Geographically Weighted Regression (GWR) is an expansion of linier regression model where each of the parameters from every observation sites is counted, so each sites have local regression parameter. Weighted Least Square (WLS) model is applied to estimate the parameter of GWR model. GWR method differentiates bandwidth kernel into two, fixed bandwidth kernel and adaptive bandwidth kernel. Fixed kernel has the same bandwidth in each observation location, meanwhile adaptive kernel has different bandwidth value in each observation location. Cross Validation (CV) is used to choose the most optimum bandwidth. The application of GWR model to show the percentage of poor population at district and city of Central Java shows that GWR model is significantly different in each location towards global regression model, also the estimated model will also give different result between one location and another. Based on Akaike Information Criterion (AIC) value between global regression models with GWR, it is know that GWR model with fixed exponentially weighted kernel is the best model to use to analyze the percentage poor population at district and city of Central Java because of it has the smallest AIC value. Keywords: Akaike Information Criterion, Bandwidth, Cross Validation, Exponential Kernel Function, Geographically Weighted Regression, Weighted Least Square

PENENTUAN KOMPOSISI WAKTU OPTIMAL PRODUKSI DENGAN METODE TAGUCHI (Studi Kasus: Penelitan di Pabrik Kerupuk Rambak Stik Cap Ikan Bawang, Semarang) Angga Saputra Desti; Triastuti Wuryandari; Sudarno Sudarno
Jurnal Gaussian Vol 3, No 1 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Many businesses crackers facing obstacles in meeting the market demand. Business doers must minimize time in the process so that market demand can be fulfilled. This study aims to minimize the time making process as well as getting the right optimal composition without damaging the quality of the product. Settlement problems using the Taguchi method in experimental design . Factor used is steaming (22 and 19 minutes), the first drying (7 and 6 hours), the second drying (10 and 9 hours) and frying (2 minutes 45 seconds and 2 minutes 30 seconds), as well as variables assessed from the experimental results in terms of taste, color and crunchiness with using organoleptic assessment by a not trained panelists. From the experimental results best factor level selected by SNR and the mean value in terms of taste, color and crunchiness. The composition of the optimal cracker manufacture process to produce the most preferred crackers elected steaming (19 minutes), the first drying (7 hours) , the second drying (9 hours) and frying (2 minutes 30 seconds). Optimal composition of the comparison results with the standard factory based T – test independent sampel the response of taste, color and crunchiness produce the same average, with the time difference for once the process is 310 minutes or 5 hours 10 minutes.

PEMODELAN B-SPLINE UNTUK MENGESTIMASI KURVA YIELD OBLIGASI PEMERINTAH KODE FIXED RATE Nurcahyanti, Tri Meida; Widiharih, Tatik; Warsito, Budi
Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Bond is a medium-long term loan agreement that can be handed over, it contains a promise from the issuer to pay rewards in the form of interest on a particular period and paying off the principal debt on the time that has been appointed to the bond buyer. A method to find out the relationship between yield and time to maturity for a type of bond at any given time is illustrated through the yield curve. One of the methods for estimating yield curve is B-spline. The data that used to estimate the yield curve with B-spline model are sourced from Indonesia Stock Exchange, namely Government Bond Trading Report with code FR (Fixed Rate). The data periods used are 9, 16, and 23 November 2018. The best model for estimating the yield curve at any period of the data is linear B-spline model with 6 knots but the knot position is different for every data period. Based on the calculation of MAPE, the ability of the model to predict is very good. Investment with maximum profit based on the estimation of yield curve using B-spline linear model with 6 knot is FR0071.Keywords: bond, yield, yield curve, Government Bond, B-spline

ANALISIS ANTRIAN DALAM OPTIMALISASI SISTEM PELAYANAN KERETA API DI STASIUN PURWOSARI DAN SOLO BALAPAN Siti Anisah; Sugito Sugito; Suparti Suparti
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Train is one of mass transportation’s mode in great demand by the people of Indonesia. Purwosari and Solo Balapan stations are place which often visited by the public to travel long distances by using the train from economy class, business and executive. With so many types of trains that pass through the station, so the queuing analysis needs to be done to find out how the train service system at the station. From the results obtained, the queuing model at the Purwosari station is (M/M/2):(GD/∞/∞) for model lanes of 1 and 4 and lanes of 2 and 3. For the queuing model from lanes of 1 and 5 in the Solo Balapan station obtained models (M/M/2):(GD/∞/∞). Later models of queuing lanes of 2,3, and 4 at the station Solo Balapan is (M/M/3):(GD/∞/∞), while lane of 6 is (M/M/1):(GD/∞/∞). Keywords: Train, Purwosari and Solo Balapan Stations, Queuing models.

ANALISIS KECENDERUNGAN INFORMASI DENGAN MENGGUNAKAN METODE TEXT MINING (Studi Kasus: Akun twitter @detikcom) Syaifudin Karyadi; Hasbi Yasin; Moch. Abdul Mukid
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The internet is an extraordinary phenomenon. Starting from a military experiment in the United States, the internet has evolved into a 'need' for more than tens of millions of people worldwide. The number of internet users is large and growing, has been creating internet culture. One of the fast growing social media twitter. Twitter is a microblogging service that stores text database called tweets. To make it easier to obtain information that is dominant discussed, then sought the topic of twitter tweet using clustering. In this research, grouping 500 tweets from twitter account @detikcom using k-means clustering. The results of this study indicate that the maximum index Dunn, the best grouping K-means clustering to obtain the dominant topic as many as three clusters, namely the government, Jakarta, and politics.Keywords: text mining, clustering,, k-means , dunn index, and twitter.

ANALISIS NILAI RISIKO (VALUE AT RISK) MENGGUNAKAN UJI KEJADIAN BERNOULLI (BERNOULLI COVERAGE TEST) (Studi Kasus pada Indeks Harga Saham Gabungan) Iwan Ali Sofwan; Agus Rusgiyono; Suparti Suparti
Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Risk management is a systematic procedure to decrease the risk of an asset. Risk must be calculated in order to determine the best strategy in investing. Value at Risk (VaR) is a measure of risk that can be used. VaR measures the worst loss that can be happen in the future at a certain confidence level. There are many method to compute VaR. However, the methods are useful if it can predict future risks accurately. Therefore, the methods should be evaluate with a backtesting procedure. This research analyze the two methods of computing VaR, Historical Simulation and Johnson transformation approach, that estimate the risk of Jakarta Composite Index and backtest the methods use Bernoulli Coverage Test. The result, if using the relative VaR to forecast the risk of Jakarta Composite Index, the historical simulation approach can be used if the expected probability of violation is . Whereas the Johnson transformation approach can be used if the expected probability of violation is . If using the absolute VaR to forecast the risk of Jakarta Composite Index, the historical simulation approach can be used if the expected probability of violation is . Whereas the Johnson transformation approach can be used if the expected probability of violation is .

OPTIMASI REGRESI LOGISTIK MENGGUNAKAN ALGORITMA GENETIKA UNTUK PEMODELAN FAKTOR-FAKTOR YANG MEMPENGARUHI PENGGOLONGAN KREDIT BANK (Studi Kasus: Debitur di PT BPR Gunung Lawu Klaten Periode Tahun 2017) Reno Penggalih Surya Wardhani; Sudarno Sudarno; Di Asih I Maruddani
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Credit is the greatest asset managed by banks and also the most dominant contributor to the bank’s income. But in its implementation, the provision of credit to the public is at risk for non-performing loans. For this reason, creditors try to minimize the occurrence of non-performing loans by predicting credit risk appropriately. In this study, modeling the factors that influence credit classification at PT BPR Gunung Lawu is useful for predicting the credit risk of prospective debtors. Modeling are done using logistic regression and genetic algorithms. Factors suspected of influencing credit classification include age, gender, marital status, education, home ownership, employment, net income, tenor, type of business, type of loan, type of loan interest, and loan size. Estimated model parameters obtained from logistic regression were optimized using genetic algorithms. The fitness function used is pseudo or and MSE. The best model is generated by modeling with genetic algorithms based on MSE fitness. The model produces the highest value of 0.1958 and the lowest MSE value of 0.1648 with classification accuracy of 75.33%. Keywords: credit classification, logistic regression, genetic algorithms

PEMODELAN REGRESI LINIER MULTIVARIAT DENGAN METODE PEMILIHAN MODEL FORWARD SELECTION DAN ALL POSSIBLE SUBSET SELECTION PADA JUMLAH KEMATIAN BAYI DAN INDEKS PEMBANGUNAN MANUSIA (IPM) ( Studi Kasus di Provinsi Jawa Tengah Tahun 2013 ) Indri Puspitasari; Abdul Hoyyi; Diah Safitri
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Regression analysis is a statistical analysis that aims to measure the effect of the independent variables to the dependent variable. Multivariate Linear Regression is a regression model that consists of more than one dependent variables and the dependent variables are correlated. The Number of Infant Mortality and Human Development Index (HDI) of Central Java Province in 2013 was influenced by several variables, such as: mean years of schooling and the number of health centers. To analyze the effects of mean years of schooling and the number of health centers to The Number of Infant Mortality and Human Development Index (HDI) can use multivariate linear regression analysis becuase the dependent variables are correlated. Model selection is determined by using the Forward Selection and All Possible Subset Selection. Selection the model by using Forward Selection, first variables that is included in the model is based of independent variable that have the greatest correlation with the dependent variables. For All Possible Subset Selection, model selection is done by modeling all the models that may have formed. AIC criteria is used for determining the model for All Possible Subset Selection. The model which is selected by using Forward Selection and All Possible Subset Selection has the same independent variables, the model with independent variables mean years of schooling and the number of health centers. The error of the model fulfill all of the error assumptions. Based on the model, the value of AIC is 247.8142 and Eta Squared Lambda is 92.22%. Keywords : Multivariate Linear Regression, Forward Selection, All Possible Subset Selection, AIC

ANALISIS DATA RUNTUN WAKTU MENGGUNAKAN METODE WAVELET THRESHOLDING Yudi Ari Wibowo; Suparti Suparti; Tarno Tarno
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Latterly, wavelet is used in various application of statistics. Wavelet is a method without parameter which used in signal analysis, data compression, and time series analysis. Wavelet thresholding is a method which reconstructing the largest number of wavelet coefficients. Only the coefficients are greater than a specified value which taken and the rest coefficients are ignored, because considered null. Certain value is called the threshold value. The level of smoothness estimation are determined by some factor such as wavelet functions, the type of thresholding functions, level of resolutions and threshold parameters. But most dominant factor is threshold parameter. Because that was required to select the optimal threshold value. At the simulation study was analyzing of the stasioner, nonstasioner and nonlinier data. Wavelet thresholding method gives the value of Mean Square Error (MSE) which is smaller than the ARIMA. Wavelet thresholding is considered quite so well in the analysis of time series data.

PENERAPAN METODE TAGUCHI UNTUK KASUS MULTIRESPON MENGGUNAKAN PENDEKATAN GREY RELATIONAL ANALYSIS DAN PRINCIPAL COMPONENT ANALYSIS (Studi Kasus Proses Freis Komposit GFRP) Annisa Ayu Wulandari; Triastuti Wuryandari; Dwi Ispriyanti
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Taguchi method is a method for quality control of product by off line. Taguchi method usually used to solve optimization problem with single respon. Multirespon case was done by using Grey Relational Analyisis (GRA) and Principal Component Analysis (PCA). With GRA method is obtained many Grey Relational Grade value. For weight is estimated using PCA. The case study use freis process GFRP composite with characteristic smaller is better. From the research is obtained combination in optimal canditions for factor fiber orientation angle at 150, helix angle at 250, and feed rate at 0,04 mm/rev. While the respon that observed are surface roughness, machine force, and delamination factor. The value of contribution percentage for each factor is 69,596% for fiber orientation angle, 9,768% for helix angle and 11,9841% for feed rate..Keywords : Multirespon Optimization, Taguchi Method, Grey Relational Analysis, Principal Component Analysis, Freis Process GFRP Composite

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Department of Statistic, Faculty of Science and Mathematics , Universitas Diponegoro Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro Gedung F lt.3 Tembalang Semarang 50275

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Jawa tengah

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