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Jurnal Gaussian
Published by Universitas Diponegoro
ISSN : -     EISSN : 23392541     DOI : -
Core Subject : Education,
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.
Arjuna Subject : -
Articles 733 Documents
PERBANDINGAN METODE PEMULUSAN EKSPONENSIAL TUNGGAL DAN FUZZY TIME SERIES UNTUK MEMPREDIKSI INDEKS HARGA SAHAM GABUNGAN Taufan Fahmi; Sudarno Sudarno; Yuciana Wilandari
Jurnal Gaussian Vol 2, No 2 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (891.862 KB) | DOI: 10.14710/j.gauss.v2i2.2779

Abstract

The development of methods of forecasting with time series data quite rapidly result there are many options that the method can be used to predict the data according to the needs and the need to compare one method to the other methods that get results of prediction with high accuracy. In this thesis, comparison of forecasting will be done using measure forecasting accuracy in the form of MAPE, MAE, and MSE of a forecast in calculating the value of The composite stock price index (CSPI) using Single Exponential Smoothing method that will be compared to modern forecasting methods, namely Fuzzy Time Series . Fuzzy Time Series methods used in this study is the method of Fuzzy Time Series proposed by Chen and Cheng. Between the three forecasting methods obtained the best  method is of Cheng’s Fuzzy Time Series.
PENERAPAN DIAGRAM KONTROL MEWMA DAN MEWMV PADA PENGENDALIAN KARAKTERISTIK KUALITAS AIR (Studi Kasus: Kualitas Pengolahan Air II PDAM Tirta Moedal Kota Semarang) Adestya Ayu Maharani; Mustafid Mustafid; Sudarno Sudarno
Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (654.498 KB) | DOI: 10.14710/j.gauss.v7i1.26632

Abstract

Water is one of the most important elements for human life, water treatment is done for human consumption and must fulfill the health requirements with the levels of certain parameters. Quality of Water Treatment II is the second water purification installation owned by PDAM Tirta Moedal Semarang City with production capacity of 60 l/s. Variables used in the water treatment process are correlated with each other, so used multivariate control chart. The Multivariate Exponentially Weighted Moving Average control chart is used for monitoring process mean, and the Multivariate Exponentially Weighted Moving Variance control chart is used for monitoring process variability. The variables used are colour, turbidity, organic substance, manganese and the total dissolved solid. MEWMA control chart with λ = 0.5, showed that the process mean is controlled statistically. MEWMV control chart showed that variability is controlled statistically in λ = 0.4, ω = 0.2 and L = 3.3213. MEWMA and MEWMV control chart showed that the process is not capable because it obtained the value of process capability index less than 1. Keywords: Water, Multivariate Exponentially Weighted Moving Average, Multivariate Exponentially Weighted Moving Variance, process capability.
PEMETAAN CABANG PERUSAHAAN ASURANSI X BERDASARKAN LAPORAN BEBAN KLAIM DAN PENERIMAAN PREMI MENGGUNAKAN BIPLOT Maharani Febriana Putri; Yuciana Wilandari; Rita Rahmawati
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (561.797 KB) | DOI: 10.14710/j.gauss.v4i2.8580

Abstract

The number of cars currently make everyone aware of the benefits of insurance to protect against financial loss. Insurance products that demand a lot of people are motor vehicle insurance product that car. As an entrepreneur it is necessary to determine whether or not a company healthy in order to determine the condition of the company and what things need to be considered to improve the financial condition of the company. To see healthy or not an insurance company then needs to be analyzed on the income and expenditures of the company. The company has a good insurance premium income is greater than the burden of claims. This makes the company should strive to find that a lot of customers and minimize the burden of the claims that the company is in good financial condition. This study was conducted to find out how the condition of the company by using biplot analysis. This analysis can be applied to determine the company branch mapping, information and determine which branch company has the top achievers. The results obtained from these studies is the premium income report greater than the burden of claims and the top achievers is Surabaya Tunjungan. In addition, mapping that can be explained by a biplot analysis reached 100% which means it can explain the total data properly.Keywords : company branch mapping, biplot analysis, premium income and            burden of claims
PERHITUNGAN PEMBIAYAAN DANA PENSIUN DENGAN METODE ATTAINED AGE NORMAL DAN PROJECTED UNIT CREDIT (STUDI KASUS : PT. TASPEN (PERSERO) KANTOR CABANG UTAMA SEMARANG) Musandingmi Elok Nurul Islam; Yuciana Wilandari; Suparti Suparti
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (788.909 KB) | DOI: 10.14710/j.gauss.v5i3.14707

Abstract

Welfare in the future is something that all people dreamed, including goverment employees. As a government's concern for welfare in the future for the goverment employees so the government give pension program. The pension program will give pension benefit to the goverment employees on their retirement age. Pension funding requires actuarial computation which normal cost and actuarial liability. Actuarial computation is divided into two major parts, Accrued Benefit Cost Method and Projected Benefit Cost Method. One of Accrued Benefit Cost Method example is Projected Unit Credit and for the Projected Benefit cost one of the method is Attained Age Normal. This research uses secondary data from PT. TASPEN (Persero) KCU Semarang. Computation result shows on the both second normal cost the method tends to increase each year. Projected Unit Credit Method exhibits substansial increment, meanwhile on Attained Age Nornal Method the increment is relatively slow. The amount of both second actuarial liability method will always increase each year, by using Attained Age Normal Cost produces bigger actuarial liability than Projected Unit Credit Method. Projected Unit Credit Method give final normal cost less than Attained Age Nornal Method. Keywords: Pension, Normal Cost, Actuarial liability, Attained Age Normal, Projected Unit Credit. 
APLIKASI MODEL REGRESI POISSON TERGENERALISASI PADA KASUS ANGKA KEMATIAN BAYI DI JAWA TENGAH TAHUN 2007 Nurwihda Safrida Umami; Dwi Ispriyanti; Tatik Widiharih
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (591.87 KB) | DOI: 10.14710/j.gauss.v2i4.3810

Abstract

Infant Mortality is one of the issues that can affect the number and age composition of the population. The Government pays special attention to reduce the amount of Infant Mortality Rate in Central Java, so the role of data and information becomes very important. Poisson regression is a nonlinear regression which is often used to model the relationship between the response variable in the form of discrete data with predictor variables in the form of continuous or discrete data. Poisson regression models have equidispersi assumption, a condition in which the mean and variance of the response variable have equal value. In practice, the assumption is sometimes violated in the analysis of discrete data in the form of overdispersi (value of variance greater than the mean value) so that Poisson regression model is not appropriate to be used. Overdispersi is a condition in which the data of response variable shows. One model that can be used to solve the overdispersi problem is generalized Poisson regression model. The regression model is an extension of the Poisson regression and part of the Generalized Linear Model (GLM) which does not require constancy of variance to test the hypothesis. From the data of Infant Mortality Rate in Central Java on 2007 known that there overdispersi. And the factors affecting Infant Mortality Rate is the number of health facilities, the number of medical personnel, and the percentage of households with clean water each county / city.
OPTIMASI PARAMETER MODel AUTOREGRESSIVE MENGGUNAKAN ALGORITMA PARTICLE SWARM OPTIMIZATION Setyoko Prismanu Ramadhan; Hasbi Yasin; Suparti Suparti
Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (799.504 KB) | DOI: 10.14710/j.gauss.v8i2.26666

Abstract

Box-Jenkins ARIMA method is a linear model in time series analysis which is widely used in various fields. One estimation method for Box-Jenkins ARIMA model is OLS method which aims to minimize the number of squared errors. This method is not effective when applied to time series data that is random, nonlinear and non-stationary. In this study discussed the alternative method of the PSO algorithm as an parameter optimization of the ARIMA model. PSO algorithm is an optimization method based on the behavior of a flock of birds or fish. The main advantage of the PSO algorithm is having a simple, easy to implement and efficient concept in calculations. This method is applied to data from PT Perusahaan Gas Negara shares. The results of both methods will be compared. In the AR model (1) the value of MSE is 0.532 and MAPE is 0.993. Meanwhile, the PSO algorithm obtained MSE 0.531 and MAPE 0.988. It was found that the PSO algorithm resulted in smaller MSE and MAPE values and could provide better results.Keywords : Time Series Analysis, Autoregressive, PSO
PEMODELAN FAKTOR-FAKTOR YANG MEMPENGARUHI INDEKS PEMBANGUNAN MANUSIA KABUPATEN/ KOTA DI JAWA TIMUR MENGGUNAKAN GEOGRAPHICALLY WEIGHTED ORDINAL LOGISTIC REGRESSION Rahma Nurfiani Pradita; Hasbi Yasin; Diah Safitri
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (472.263 KB) | DOI: 10.14710/j.gauss.v4i3.9488

Abstract

Human Development Index (HDI) is a measurement used for measuring human developmental achievement in certain area. Although, it does not measure all dimensions of human development, HDI seems able to measure principal dimension of human development that include longevity and health, knowledge and a good life. Geographically Weighted Ordinal Logistic Regression (GWOLR) Model is used to model a relationship between categorical response variable that have ordinal scale toward predictor variable that depend on geographical location where the data are observed. This research aims to know the factors that influence HDI of Regency/ City in East Java Province 2013 using ordinal logistic regression model and GWOLR with exponential kernel function weighting. Factors that are influencing HDI of Regency/ City in East Java are percentage of population that finish Junior High School (X2), the number of health facility (X4), and population density (X5). Based on HDI of Regency/ City in East Java’s accuracy classification result, between observations and prediction counted based on Apparent Error Rate (APER) value, it is known that GWOLR model with exponential kernel function weighting has better classification’s accuracy (86,84%) than ordinal logistic regression model (81,58%). Keywords:      HDI, Ordinal Logistic Regression Model, GWOLR, Exponential         Kernel Function,                     Classification’s Accuracy, APER
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (538.386 KB) | DOI: 10.14710/j.gauss.v3i2.5909

Abstract

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.
PEMODELAN FUNGSI TRANSFER DAN BACKPROPAGATION NEURAL NETWORK UNTUK PERAMALAN HARGA EMAS (Studi Kasus Harga Emas Bulan Juli 2007 sampai Februari 2019) Silvia Nur Rinjani; Abdul Hoyyi; Suparti Suparti
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (628.079 KB) | DOI: 10.14710/j.gauss.v8i4.26727

Abstract

The prestige of investment is increasingly rising as the people educates in managing finances. Gold is an alternative that most people tend to choose to invest. One of the important knowledge in gold investing is to predict the price in the future with factors that influence the price of gold. Therefore, in this research we made a model of gold prices based on crude oil prices. One method to forecast gold prices based on crude oil prices is the transfer function and backpropagation neural network. The results of transfer function model will be used as input for the backpropagation neural network method. The purpose of this research is to get the right forecasting method through the transfer function and backpropagation neural network model that can be used to predict gold prices. The results showed that the transfer function model with b = 0, r = [2], s = 0 and the ARMA noise model (0, [6]) is the best model to forecast the price of gold with the MAPE value of data out sample as 3,3507%.  Keywords : Gold Price, Crude Oil Prices, Transfer Function,Backpropagation Neural Network, Forecasting
PENGELOMPOKAN PROVINSI DI INDONESIA BERDASARKAN KARAKTERISTIK KESEJAHTERAAN RAKYAT MENGGUNAKAN METODE K-MEANS CLUSTER Fitra Ramdhani; Abdul Hoyyi; Moch. Abdul Mukid
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (409.125 KB) | DOI: 10.14710/j.gauss.v4i4.10222

Abstract

Welfare have a relative explanation, dynamic, and quantitative. Quantitative formulation of welfare is never final because it will continue to evolve along with the development needs of human life. In 2011, the National Team for the Acceleration of Poverty Reduction (NTAPR) made priority sector that can serve as a benchmark the welfare in a region. From the priority sector will be made cluster or group which contains all 33 provinces based on the level of public welfare in the region uses data in 2012 were sourced from the Central Statistics Agency (CSA). The method that can be used to group the 33 provinces is K-Means Cluster method with number cluster as many as two, three, four, and five clusters. K-Means Cluster method is one of cluster analysis method who can partition the data into one or more clusters, so that the data with the same characteristics are grouped into the same cluster and data with different characteristics grouped into other clusters. To know the most optimal of the number of clusters we use Davies-Bouldin Index (DBI). We concluded that the optimal number of cluster is three with details the province in the first clusters have superiority in four sectors like net enrollment rate of primary school, net enrollment rate of junior high school, IMR (Infant Mortality Rate), and access to electricity. The province in the second clusters have superiority in one sector, that is open unemployment rate. The province in the third clusters have superiority in all sectors. Keywords: Welfare, NTAPR Priority Sector, K-Means Cluster Method, Davies-.Bouldin Index (DBI)

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