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Jurnal Gaussian

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Published by Universitas Diponegoro

ISSN : - EISSN : 23392541 DOI : -

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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 3, No 2 (2014): Jurnal Gaussian" : 11 Documents clear

KAJIAN RELIABILITAS DAN AVAILABILITAS PADA SISTEM KOMPONEN PARALEL Pradewi, Riana Ayu Andam; Sudarno, Sudarno; Suparti, Suparti
Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Reliability and availability are a measure of item or system performance. System reliability and system availability obtained from the calculation of reliability and availability of the components in the system. Reliability of components in the system are affected by the time to failure (TTF). While the availability of components in the system are affected by the mean time to failure (MTTF) and mean time to repair (MTTR). Given observed time data of lifting machines consists of trolley drive and hoist in parallel, is measured its system availability. Parameter values determined using simple linear regression and maximum likelihood estimation. Furthermore observation time test data distributions in the Kolmogorov-Smirnov test. Trolley drive has exponential distribution for failure time data with while repair time data is normal distribution with and . Hoist has weibull failure time data with and while lognormal repair time data has and . The higer value of ti,system reliability value will be close to 0 and the engine can survive until the specified time. Due to MTTF is 4000 hours and MTTR is 45,70 hours, trolley drive’s availability is 98,87%. Availability of hoist is 98,84% from MTTF is 5821,61 hours and MTTR is 67,80 hours. The parallel system availability is 99,986% means the probability of system is in the state of functioning at given time is 99,986%.

ANALISIS INFLASI KOTA SEMARANG MENGGUNAKAN METODE REGRESI NON PARAMETRIK B-SPLINE Alvita Rachma Devi; Moch. Abdul Mukid; Hasbi Yasin
Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Inflation is an important consideration for investors to invest in an area. An accurate prediction of inflation is required for investors in conducting a careful planning. One of the method to find the predicted value of inflation is by using B-Spline regression, a nonparametric regression which is not depend on certain assumptions, thus providing greater flexibility. The optimal B-Spline models rely on the optimal knots that has a minimum Generalized Cross Validation (GCV). By using Semarang year-on-year inflation data from January 2008 - August 2013, the optimal B-spline models in this study are on the order of 2 ( linear ) with 2 knots, that is 5,99 and 6,09. Prediction of Semarang inflation in 2014 fluctuated around the number five and six and inflation in the end of 2014 is 6,286394%.

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 .

ORDINARY KRIGING DALAM ESTIMASI CURAH HUJAN DI KOTA SEMARANG Ahmat Dhani Riau Bahtiyar; Abdul Hoyyi; Hasbi Yasin
Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In a measurement of rainfall data, not all points are gauges because of a limitation. Given these limitations, a method is needed to estimate a value for points that are not measurable. Kriging as geostatistical analysis used in the estimation of a value in a point which is not sampled based sample points in the surrounding areas by taking into account the spatial correlation using a spatial weighting, where the correlation is shown by the variogram. Ordinary Kriging is the most widely used. By using the experimental variogram were compared with some theoretical variogram (Exponential, Gaussian, Spherical) selected one of the best semivariogram models to estimate the value that want to find. In this study, conducted rainfall estimates in Semarang in February where the result obtained is the value of rainfall each district and village

OPTIMALISASI JUMLAH BATU BATA YANG PECAH MENGGUNAKAN DESAIN EKSPERIMEN TAGUCHI (Studi Kasus: Usaha Batu Bata Bapak Kholil Ds. Bulak Karangawen) Cakra Kurniawan; Hasbi Yasin; Sugito Sugito
Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Brick is a substansial element in building construction. The strength of building may depend on bricks, a solid construction uses the best quality brick’s, which is not crumbling and broken into two parts. There are two popular types of bricks in Semarang, Penggaron bricks and Welahan bricks, Penggaron bricks is the most desirable type in market, but the quality of Penggaron bricks is worse than Welahan bricks, because the Penggaron bricks broken pieces are much more than Welahan’s. So that Penggaron bricks were taken to do research in purpose of optimizing the number of brick’s broken pieces that occurred during the production process. The method being used was the "Taguchi Design of Experiments" using Smaller is Better as quality character. The outcome of pre-experimental study was 3 factors and 2 levels so that L4 Orthogonal Array was used. After analyzing and conducting confirmation experiment, the result was obtained as follow, at the initial conditions, there are 4.6% of broken bricks, the broken bricks became 1.8%, after the experiment. The 1.8% of broken bricks were still within the range of the predicted value 1% to 2%.

PEMODELAN MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS) PADA FAKTOR-FAKTOR RESIKO ANGKA KESAKITAN DIARE (Studi Kasus : Angka Kesakitan Diare Di Jawa Tengah, Jawa Timur Dan Daerah Istimewa Yogyakarta Tahun 2011) Wasis Wicaksono; Yuciana Wilandari; Suparti Suparti
Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Diarrhea morbidity is a number of diarrhea suffers in specific region in period of one year per 1000 populations. Diarrhea morbidity is the impact from some factors such as environment, education, socioeconomic, nutrition and foods. Environmental factors that can affect the morbidity of diarrhea include the percentage of families who have a healthy latrine and percentage of households using clean water. For educational factors include the average length of school and literacy rates. On socio-economic factors include the percentage of poor and average people per household. While the food and nutritional factors are the percentage TUPM (Public Places and Food Management) healthy.Diarrhea morbidity can be pressed by analyzing those factors so that the prevention can be devised. Regression curve is used to draw the relationship of response variable and predictor variable and mostly approached by parametric regression, where the curve design is known (such as linear, quadratic and cubic). If curve design is unknown, then regression curve can be derived by approaching using non parametric regression. Multivariate Adaptive Regression Spline (MARS) is one of nonparametric regression method that can be used on high dimension data. the best MARS model is derived by combination of Minimal Observation (MO), Maximum Basic Function (BF), and Maximal Interaction (MI) through trial and error. MARS model to predict diarrhea morbidity in Central Java, East Java and Yogyakarta is MARS (MO=2;BF=28;MI=3) and equation is = -0.526742 + 0.264444 * BF2 + 12.2382 * BF5 - 7.76719 * BF15 + 4.96445 * BF17

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI LAJU PERTUMBUHAN PENDUDUK KOTA SEMARANG TAHUN 2011 MENGGUNAKAN GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION Catra Aditya Wisnu Aji; Moch. Abdul Mukid; Hasbi Yasin
Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Geographically Weighted Logistic Regression (GWLR) is a local form of logistic regression where geographical factors considered and it is assumed that the Bernoulli distribution of data used to analyze spatial data from non-stationary processes. This research will determine the factors that affect the Population Growth Rate (PGR) in the Semarang city using logistic regression and GWLR with a weighting function of bisquare kernel and gaussian kernel. The result showed that GWLR model with a weighting function of bisquare kernel better than logistic regression model and GWLR model with a weighting function of gaussian kernel because it has the smallest AIC value and classification accuracy is 87,5%. Factor that have significant effect is the number of couples of childbearing age in the Semarang city.

KAJIAN MODEL INFLASI TAHUNAN KOTA SIBOLGA DENGAN ARIMA DAN PENDEKATAN REGRESI POLINOMIAL PADA ANALISIS MULTIRESOLUSI WAVELET Ebeit Devita Simatupang; Suparti Suparti; Rita Rahmawati
Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Inflation rate is one of the fundamental economic indicators of a country. Therefore, prediction of inflation rate become important thing in taking monetary to maintain economy stability. In studying inflation model, commonly used method of parametric ARIMA Box-Jenkins which requires data is stationer and residual is white noise. However, data inflation which is fluctuates often does not meet parametric assumptions. In this study, it is proposed to use wavelet Multiresolution Analysis (MRA) as alternative method. The transformation from wavelet capable in representing time and frequencies simultaneously so that it can be used to analyze nonstasioner data. One of wavelet transformation form is discrete wavelet transformation (DWT) which expresses sized data N as for positive integer j. DWT analyses supported by MRA that divides data X become detail component ( ) and smoothing component ( ) to gain of estimating result. The best of MRA estimation will be approached by polynomial regression. The model of regression is formed by summing influence each variable predictor which raised increasingly to k-degress. By using yoy inflation data of Sibolga City in July 2008-October 2013 period, obtain the best parametric model ARIMA (0,1,[12]) with MSE=1,15411 and the best model of polynomial regression approached 13-degress at MRA that use la18 filter in resolution level which has MSE=1,238816. Both models are used to forecast yoy inflation of Sibolga City in 2014.

MODEL REGRESI COX PROPORTIONAL HAZARDS PADA DATA LAMA STUDI MAHASISWA (Studi Kasus Di Fakultas Sains dan Matematika Universitas Diponegoro Semarang Mahasiswa Angkatan 2009) Landong Panahatan Hutahaean; Moch. Abdul Mukid; Triastuti Wuryandari
Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

High education has important role to increase the intellectual life of the nation and the development of natural sciences and technology by producing the quality graduates. The quality graduates just need 48 month to finish the study. There are many factors that will affect time of study students as Grade Point Average(GPA), Bustle student level, etc. Hence, need to know what factors affecting time of study students. One method that can be used is Survival analysis. Survival Analysis is analysis of survival data from the beginning of time research until certain events occurred. One of the methods of survival analysis is Cox Proportional Hazards Regression. Cox Proportional Hazards Regression is a regression which used data of intervals of time an event. The case which is discussed in this research is factors that affect time of study students of Faculty of Science and Mathematics started 2009 Diponegoro of University with the second type of censoring. From the research give conclusion that factors affecting time of study students is Department, GPA, and Organization

PEMILIHAN MODEL REGRESI POLINOMIAL LOKAL DAN SPLINE UNTUK ANALISIS DATA INFLASI DI JAWA TENGAH Elyas Darmawan; Suparti Suparti; Moch. Abdul Mukid
Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Inflation becomes one of important problems as parameter of economic growth and determiner factor for government in formulating fiscal, monetary and nonmonetary policy. But, these days the policies were arranged can’t give the positive response to inflation pressure in the future. Therefore, the prediction of inflation rates are needed. Inflation rates are predicted by nonparametric regression approach because of the fluctuation of inflation which can’t be solved by classic time series models. In this research, the best nonparametric regression models are selected between local polynomial and spline regression to predict Central Java Inflation movement in 2014. Based on analysis, the best nonparametric regression is spline order 2, knot points are 5,37; 5,44; 5,59 and 9,01 with GCV 0,4367286. By using that model, the prediction of Central Java inflation got down since October 2013 until February 2014 on level 7% and March until December 2014 on level 6%.

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Jawa tengah

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