Garuda - Garba Rujukan Digital

Article Per Year (5 Year)

All

Jurnal Gaussian

gaussian
Website

Published by Universitas Diponegoro

ISSN : - EISSN : 23392541 DOI : -

Core Subject : Education,

Other

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

40 41 42 43 44

PERBANDINGAN MODEL REGRESI COX PROPORTIONAL HAZARD MENGGUNAKAN METODE BRESLOW DAN EFRON (Studi Kasus: Penderita Stroke di RSUD Tugurejo Kota Semarang) Setiani, Eri; Sudarno, Sudarno; Santoso, Rukun
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Cox proportional hazard regression is a regression model that is often used in survival analysis. Survival analysis is phrase used to describe analysis of data in the form of times from a well-defined time origin until occurrence of some particular even or end-point. In analysis survival sometimes ties are found, namely there are two or more individual that have together event. This study aims to apply Cox model on ties event using two methods, Breslow and Efron and determine factors that affect survival of stroke patients in Tugurejo Hospital Semarang. Dependent variable in this study is length of stay, then independent variables are gender, age, type of stroke, history of hypertension, systolic blood pressure, diastolic blood pressure, blood sugar levels, and BMI. The two methods give different result, Breslow has four significant variables there are type of stroke, history of hypertension, systolic blood pressure, and diastolic blood pressure, while Efron contains five significant variables such as type of stroke, history of hypertension, systolic blood pressure, diastolic blood pressure and blood sugar levels. From the smallest AIC criteria obtained the best Cox proportional hazard regression model is Efron method. Keywords: Stroke, Cox Proportional Hazard Regression model, Breslow method, Efron method.

FAKTOR-FAKTOR YANG MEMPENGARUHI KRIMINALITAS DI KABUPATEN BATANG TAHUN 2013 DENGAN ANALISIS JALUR Dermawanti Dermawanti; Abdul Hoyyi; Agus Rusgiyono
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Crime or criminality in Indonesia is rampant both in print or television can be seen almost every day news about crime. Basically, each individual will be influenced by several factors, both internal and external causes a person to commit a criminal act, including population, education, morality, poverty, and unemployment. In this case will be studied in a statistical analysis that can detect the magnitude of these factors, either directly or indirectly to the level of criminality. One of the statistical analysis that can be used to analyze the causal relationship of the variables is the path analysis (path analysis) which is a direct development of multiple regression form with the aim to provide estimates of the level of interest (magnitude) and significance (significance) in a hypothetical causal link set variable. In this study showed that the factor that has the greatest positive effect on crime is unemployment factor of 0.395 with immediate effect. A factor which has the second largest positive effect of education is a factor of 0.222 to the direct effects and the indirect effect of 0.0818. Meanwhile, a factor that has a positive influence smallest is the moral factor to the effect of 0.180.Keywords : Criminality, Path Analysis

PEMBENTUKAN MODEL LOG LINIER EMPAT DIMENSI (Studi Kasus : Rata-rata Pengguna Jenis Bahan Bakar Minyak berdasarkan Jenis Kendaraan, Rasio Kompresi dan Kapasitas Mesin) Sari, Juli Sekar; Wilandari, Yuciana; Hoyyi, Abdul
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Based on the data from the Central Bureau of statistics, Indonesia's population is 237 million, an increase of 15.2% of the total population in 2000. With the increasing of the population from year to year, automatically the growth of vehicles will also experience increased. The impact of the increase in the number of motor vehicles is surely in the form of fuel consumption. Moreover, many factors will consider by the people to choose the type of fuel for their vehicle. Those factors included in the internal and external factors of the vehicle itself. At first, the internal factors in question are the type of vehicle, the compression ratio of the engine, and engine capacity. This research was conducted to find out the relationship between the internal factors with the log-linear Models. Log-linear Model was used to analyze the relationship between the variable responsesthat arewhich formed the contingency table. In this case, the researcher used log-linear Model of four dimensions with the step of analysis, as follows: outlining the possible model with diagram’s association, looking for the grade of frequency estimation of hope of any possible model, examining the Goodness of Fit of each model to find out the significant one, and determining the best model, in this case by looking at the smallest value of AIC. From the log-linear Model four dimensions is obtained the best model is the Model (WX, XY, XZ, YZ YZ) which means in case of this research there is a relationship between the type of fuel (W)*type of vehicle (X), the type of vehicle (X)*the compression ratio of the engine (Y), the type of vehicle (X)*engine capacity (Z), and the compression ratio of the engine (Y)*Engine Capacity(Z), with the value of AIC = -184. Keywords:, Log linear models four dimention, AIC

SEGMENTASI PASAR PADA PUSAT PERBELANJAAN MENGGUNAKAN FUZZY C-MEANS (STUDI KASUS: RITA PASARAYA CILACAP) Nurhikmah Megawati; Moch. Abdul Mukid; Rita Rahmawati
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

RITA Pasaraya Cilacap is the first supermarket in Cilacap. Previously, RITA Pasaraya become a shopping center for all people in Cilacap. Now, more and more supermarket is standing. To find this target, RITA Pasaraya needs grouping or market segmentation. Grouping method used fuzzy cluster means (FCM). For an optimal cluster number using the accuracy of the measurement criteria is Xie Beni Index. Research data obtained by questionnaire on RITA Pasaraya Cilacap with 10 psikografik variables. Results of the research, consumer segmentation more accurate if grouped into 2 clusters. The final result is respondents in cluster 1 more attention to low price levels, complete goods, big discounts, satisfactory service, strategic location, roomy parking, comfortable for shopping, adequate public facilities, complete payment facilities, and cleaner room than to respondents in cluster 2. Basically, similar profiles cluster in cluster 1 and cluster 2. Mainly RITA Cilacap Supermarkets are women, with the range age of 16-29 years, with a frequency of shopping 2-4 times per month. Only last education and income are different. In cluster 1, dominated b senior high school with income of 2-5 million every month, and in cluster 2 dominated by bachelor with income <2 million every month.

PENERAPAN METODE EXPONENTIALLY WEIGHTED MOVING AVERAGE (EWMA) DALAM PENGUKURAN RISIKO INEVSTASI SAHAM PORTOFOLIO UNTUK VOLATILITAS HETEROGEN Wulandari, Heni Dwi; Mustafid, Mustafid; Yasin, Hasbi
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Risk measurement is important in making an investment. One tool used in the measurement of investment risk is Value at Risk (VaR). VaR represents the greatest possible loss of investment with a given period and level of confidence. In the calculation of Value at Risk requires the assumption of normality and homogeneity. However, financial data rarely satisfies that assumption. Exponentially Weighted Moving Average is one method that can be used to overcome the existence of a heterogeneous variant. Daily volatility is calculated using the EWMA method by taking a decay factor of 0.94. VaR portfolio of ASII, BBNI and PTBA stocks is calculated using historical simulation method from the revised portfolio return with Hull and White volatility updating procedure. VaR values obtained are valid at a 99% confidence level based on the validity test of Kupiec PF and Basel rules. Keywords: Value at Risk (VaR), Portfolio, EWMA, Historical Simulation, Volatility Updating

ANALISIS PENGARUH INFLASI, KURS, DAN SUKU BUNGA SERTIFIKAT BANK INDONESIA TERHADAP INDEKS HARGA SAHAM GABUNGAN MENGGUNAKAN REGRESI LINIER BERGANDA BAYES Marta Widyastuti; Moch. Abdul Mukid; Yuciana Wilandari
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Jakarta Composite Index (JCI) is one of the stock price index emitted by Indonesia Stock Exchange (IDX). JCI is influenced by macro factors (external factors of a company) and micro factors (factors that come within the company). Some of the macro factors include inflation, exchange rate, and interest rate of Bank Indonesia Certificate. To obtain model of inflation, exchange rate, and interest rate of Bank Indonesia Certificate on JCI, Bayesian multiple linier regression can be used so that researcher is able to take into account prior information and apply it together with current data to obtain posterior estimation. From the data processing, it is known that interest rate of Bank Indonesia Certificate is not significantly influencing the model. Meanwhile, inflation and exchange rate are significantly influencing the model and both of them result 72,72% of R-Squared. Furthermore, the final model of Bayesian multiple linier regression proven to be very accurate because it has 4,951% of MAPE. Keywords: JCI, inflation, exchange rate, interest rate of Bank Indonesia Certificate, Bayesian multiple linier regression, prior, posterior, MAPE

ANALISIS KONJOIN FULL PROFILE DALAM PEMILIHAN BEDAK UNTUK MAHASISWI DEPARTEMEN STATISTIKA UNIVERSITAS DIPONEGORO Julianisa, Rose Debora; Safitri, Diah; Yasin, Hasbi
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Powder is the one of cosmetic product that serves to cover the shortfall on the face. Powder consumption continues to increase from year to year to follow trend of cosmetic and lifestyle that happened to people. It makes producer to be more creative and innovative to produce or developing their product to keep consumers interested. To help producer to know and understand the consumer preference on combinations of attributes in the powder, it can be used conjoint analysis. Beside that, conjoint analysis is used to get the concept of products that comply with the consumers want and can be developed as a combination of new products. In this thesis conjoint analysis is used by using presentation method of full-profile. There are four attributes used in this analysis, they are powder types, form of packaging, aroma, and glass facility. From the results of the analysis that obtained by the respondents, the most importance attribute in selecting a face powder is the package attribute (34,338 %), the second is a kind of powder (33,667 %), the third is glass facility in the powder (16,397 %), and the last is the scent of powder (15,598 %). The combination of desired respondents in choosing or use a powder is a powder that have the type of compact powder, circular packaging forms, has no aroma, and there is no glass. Keywords : powder, consumer’s preference, conjoint analysis, full-profile

PEMODELAN REGRESI HURDLE POISSON DALAM MENGATASI EXCESS ZEROS UNTUK KASUS PENYAKIT TETANUS NEONATORUM PADA NEONATAL DI JAWA TIMUR Cylvia Evasari Margaretha; Dwi Ispriyanti; Tatik Widiharih
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Tetanus Neonatorum is one of the infectious diseases that occur in newborns caused by Clostridium Tetani bacteria through cuts or scratches. The number of Tetanus Neonatorum cases in East Java Province in 2017 is discrete data Poisson distribution with a proportion of zero value of 73,7 percent. The amount of zero value data can result in overdispersion where the variance is greater than the mean. To overcome this problem, Hurdle Poisson regression model is a solution. To estimation of regression parameters for Hurdle Poisson regression is using the Maximum Likelihood Estimation (MLE) method and Broyden Fletcher Goldfarb Shanno (BFGS) iteration. Hurdle Poisson regression produces predictor variables that affect the number of Tetanus Neonatorum cases in East Java Province in the logit model are the percentage of pregnant women administered the K4 program, population density per and in the truncated Poisson model are the percentage of labor assisted by health workers the percentage of pregnant women administered the K4 program, population density per with the Akaike Information Criterion (AIC) value of 78,422.Keywords: Tetanus Neonatorum, Excess Zeros, Overdispersion, Hurdle Poisson Regression

PERAMALAN JUMLAH TAMU HOTEL DI KABUPATEN DEMAK MENGGUNAKAN METODE SUPPORT VECTOR REGRESSION Desy Trishardiyanti Adiningtyas; Diah Safitri; Moch. Abdul Mukid
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The purpose of this research is to forecast the number of hotel’s guests in Demak using Support Vector Regression. Support Vector Regression (SVR) is method used for forecasting. Forecasting the number of hotel’s guests in Demak using SVR produce good accuracy for forecasting the training and testing data. Forecasting for the training data generate MAPE value of 10.2806% and forecasting of testing data generate MAPE value of 11.622%.Keywords: Support Vector Regression, hotel, Demak, accuracy, forecasting, training, testing

PEMBANGKITAN SAMPEL RANDOM MENGGUNAKAN ALGORITMA METROPOLIS-HASTINGS Irwanti, Lies Kurnia; Mukid, Moch. Abdul; Rahmawati, Rita
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Generating random samples can be done directly and indirectly using simulation techniques. This final project will discuss the process of generating random samples and estimate the parameters using an indirect simulation. Indirect simulation techniques used if the target distribution has a complicated shape and high dimension of density functions. Markov Chain Monte Carlo (MCMC) simulation is a solution to do it. One of the algorithms that is commonly used is Metropolis-Hastings. This algorithm uses the mechanism of acceptance and rejection to generate a sequence of random samples. In the example to be discussed, Metropolis-Hastings algorithm is applied to generate random samples of Beta distribution and also estimate the parameter value of the Poisson distribution using a proposal distribution random-walk Metropolis.

Page 42 of 74 | Total Record : 733

40 41 42 43 44

Filter by Year

2012 2024

Filter By Issues

All Issue Vol 13, No 1 (2024): Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian Vol 12, No 2 (2023): Jurnal Gaussian Vol 12, No 1 (2023): Jurnal Gaussian Vol 11, No 4 (2022): Jurnal Gaussian Vol 11, No 3 (2022): Jurnal Gaussian Vol 11, No 2 (2022): Jurnal Gaussian Vol 11, No 1 (2022): Jurnal Gaussian Vol 10, No 4 (2021): Jurnal Gaussian Vol 10, No 3 (2021): Jurnal Gaussian Vol 10, No 2 (2021): Jurnal Gaussian Vol 10, No 1 (2021): Jurnal Gaussian Vol 9, No 4 (2020): Jurnal Gaussian Vol 9, No 3 (2020): Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian Vol 9, No 1 (2020): Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian Vol 7, No 4 (2018): Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian Vol 6, No 4 (2017): Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian Vol 5, No 2 (2016): Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian Vol 3, No 1 (2014): Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian Vol 2, No 3 (2013): Jurnal Gaussian Vol 2, No 2 (2013): Jurnal Gaussian Vol 2, No 1 (2013): Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian More Issue

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
-

Contact Email
-

Phone
-

Journal Mail Official
-

Editorial Address
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

Location
Kota semarang,

Jawa tengah

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name -

Contact Email -

Phone -

Journal Mail Official -

Editorial Address 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

Location Kota semarang, Jawa tengah INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
-

Contact Email
-

Phone
-

Journal Mail Official
-

Editorial Address
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

Location
Kota semarang,

Jawa tengah

INDONESIA