cover
Article Per Year (5 Year)
All
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
Jurnal Gaussian
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 15 Documents
 1   2 
Search results for , issue "Vol 10, No 2 (2021): Jurnal Gaussian" : 15 Documents clear
ANALISIS PENGARUH KEPUASAN TERHADAP LOYALITAS KONSUMEN SMARTPHONE SAMSUNG MENGGUNAKAN METODE PARTIAL LEAST SQUARE PADA MAHASISWA UNIVERSITAS DIPONEGORO SEMARANG Jefferio Gusti Putratama; Alan Prahutama; Suparti Suparti
Jurnal Gaussian Vol 10, No 2 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i2.30948

Abstract

Smartphones are one of the electronic devices that are capable of experiencing fairly rapid development. The existence of this Smartphone is considered to be the most important item for used everyday. Samsung is one of the most popular smartphone brand in Indonesia. Based on data from the website of the Statcounter survey institute, it was found that the Samsung market share in Indonesia until August 2020 was in the top position, namely 24.19%. Samsung continues to make various innovations in order to continue to dominate the top of the smartphone sales segment. In addition, to provide consumer's satisfication so that consumer’s loyalty to the Samsung brand will be maintained. The purpose of this study is to make measurement models and structural models, as well as to test the relationship of customer satisfaction to consumer loyalty of Samsung smartphones using the SEM – PLS (Partial Least Square) method. This research was conducted on Diponegoro University students who have purchased and used a Samsung smartphone. This research was conducted on Diponegoro University students who have purchased and used a Samsung smartphone. This research has produced 4 latent variables with 18 measurement models and 2 structural models. Based on the 2 structural models formed, the result shows that the R2 value in the customer satisfaction model is 0.670. This indicates that the variable customer satisfaction can be explained by the variable product quality and price by 67%. Meanwhile, in the consumer loyalty model, the R2 value is 0.478. This indicates that the consumer loyalty variable can be explained by the consumer satisfaction variable of 47.8%. Keywords:    Samsung Smartphone, Consumer’s Satisfaction, Consumer’s Loyalty, Partial Least Square.
PENERAPAN SEASONAL GENERALIZED SPACE TIME AUTOREGRESSIVE SEEMINGLY UNRELATED REGRESSION (SGSTAR SUR) PADA PERAMALAN HASIL PRODUKSI PADI Leni Pamularsih; Mustafid Mustafid; Abdul Hoyyi
Jurnal Gaussian Vol 10, No 2 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i2.29435

Abstract

Ordinary Least Square (OLS) is general method to estimate Generalized Space Time Autoregressive (GSTAR) parameters. Parameter estimation by using OLS for GSTAR model with correlated residuals between equations will produce inefficient estimators. The method that appropriate to estimate the parameter model with correlated residuals between equations is Generalized Least Square (GLS), which is usually used in Seemingly Unrelated Regression (SUR). This research aims to build the seasonal GSTAR SUR model as model of rice yield forecasting in three locations by using the best weighting. Weights used are binary weights, inverse distance and normalization of cross correlation. Data which used in this research are the data of rice yield per quarter in three districts in Central Java, namely Banyumas, Cilacap and Kebumen. The data from the period of January 1981 to December 2014 as training data and the period of January 2015 to December 2018 as validation data. The resulting is a model that has a seasonal effect with the autoregressive order and the spasial order limited to 1 so the model formed is SGSTAR (41)-I(1)(1)3. The best model produced is the SGSTAR SUR (41)-I(1)(1)3 model with inverse distance weighting because it fulfills both assumptions, residuals white noise and residuals normally multivariate distribution. Additionally, it has the smallest MAPE value when compared the other weighting, that is 20%. This MAPE value indicates  that the accuracy rate of forecast is accurate.Keywords: Rice yield, Seasonal, GSTAR, SUR.
ANALISIS INTEGRASI SPASIAL PASAR CABAI MERAH KERITING DI JAWA TENGAH DENGAN METODE VECTOR ERROR CORRECTION MODEL Samantha, Kenia; Tarno, Tarno; Rahmawati, Rita
Jurnal Gaussian Vol 10, No 2 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i2.29007

Abstract

Curly red chili (Capsicum annuum L.) is one of commodity which has a big influence to the national economy. To maintain the price stability of curly red chili, an integrated market is needed. Spatial market integration is the level of closeness of relations between regional markets and other regional markets. Spatial market integration will be modeled by the Vector Error Correction Model (VECM) method to see the closeness of both short and long term relationships. The object of this study is the price of curly red chili for several regions in Central Java, such as Kota Semarang, Kab. Demak, Kab. Pati, and Kab. Pekalongan in the period January 2016 to December 2019 where the data has met the stationarity test at first level of difference. In Johansen's cointegration test, it was obtained 3 cointegrations, which means that in each short-term period all variables tend to adjust to each other to achieve long-term balance. Granger causality test shows that there is a two-way relationship and the relationship affects one variable to another for all variables. The VECM model obtained has the MAPE accuracy value for HCMK Semarang 15.93%, Kab. Demak 17.61%, Kab. Pati 15.88%, and Kab. Pekalongan 14.49% which can be interpreted that the performance of the model is good. Keywords: Curly Red Chili, Spatial Market Integration, VECM, Johansen's Cointegration, Granger Causality
PEMODELAN AUTOREGRESSIVE FRACTIONALLY INTEGRATED MOVING AVERAGE DENGAN EFEK EXPONENTIAL GARCH (ARFIMA-EGARCH) UNTUK PREDIKSI HARGA BERAS DI KOTA SEMARANG Rezky Dwi Hanifa; Mustafid Mustafid; Arief Rachman Hakim
Jurnal Gaussian Vol 10, No 2 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i2.29933

Abstract

Time series data is a type of data that is often used to estimate future values. Long memory phenomenon often occurs in time series data. Long memory is a condition that shows a strong correlation between observations even though they are quite far away. This phenomenon can be overcome by modeling time series data using the Autoregressive Fractional Integrated Moving Average (ARFIMA) model. This model is characterized by a fractional difference value. ARFIMA (Autoregressive Fractional Integrated Moving Average) model assumes that the residuals are normally distributed, mutually independent, and homogeneous. However, usually in financial data, the residual variants are not constant. This can be overcome by modeling variants. Standard equipment that can be used to model variants is the ARCH / GARCH (Auto Regressive Conditional Heteroscedasticity / Generalized Auto Regressive Conditional Heteroscedasticity) model. Another phenomenon that often occurs in GARCH models is the leverage effect on the residuals of the model. EGARCH (Exponential General Auto Regessive Conditional Heteroscedasticity) is a development of the GARCH model that is appropriate for data that has an leverage effect. The implementation of this model is by modeling financial data, so this study takes 136 monthly data on rice prices in Semarang City from January 2009 to April 2020. The purpose of this study is to create a long memory data forecasting model using the Exponential method. Generalized Autoregressive Conditional Heteroscedasticity (EGARCH). The best model obtained is ARFIMA (1, d, 1) EGARCH (1,1) which is capable of forecasting with a MAPE value of 3.37%.Keyword : Rice price, forecasting , long memory, leverage effect, GARCH, EGARCH
ANALISIS METODE BAYESIAN PADA SISTEM ANTREAN PELAYANAN LOKET TIKET STASIUN TAWANG SEMARANG Aurum Anisa Salsabela; Sugito Sugito; Budi Warsito
Jurnal Gaussian Vol 10, No 2 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i2.29410

Abstract

Jamming is one of the serious problem in Indonesia caused by the increase of vehicle. The government has made solution for this situation for example was public transportation. Train is one of the suitable public transportation because of the ticket price was cheap. Tawang Railway Stasion Semarang was the biggest railway station in Semarang. In the specific day such long holiday or celebrating day, many people have chosen train to bring them. This make a queuing situation on the counter of station. Queue theory models provide the random of arrival and service time. The Bayesian theory suits to handle the problem of queuing that has been working for several times. Based on the analysis of the queue models for customer service, self-print tickets, cancellation and ordering are (G/G/c):(GD/∞/∞) from the posterior distribution with combination from prior distribution and likelihood sample. The combination of prior distribution and likelihood sample used in this research is Poisson distribution for all ticket counter except the arrival for cancellation counter which Normal distribution. The likelihood sample used Poissonn distribution for all ticket counter, except for self-print tickets which Diskrit Uniform Distribution.  Queue models can be used to count the size of the system performance. Based on the calculations and analysis, it can be concluded that the queueing system to the customer service, self-print tickets, cancellation and ordering have been good because its steady state and busy probability is higher than jobless probability. Keywords: Tawang Railway Station, Queue, Bayesian, size of the system performance

Page 2 of 2 | Total Record : 15

 1   2 

Filter by Year

2021 2021


Reset
Filter By Issues
All Issue Vol 12, No 3 (2023): Jurnal Gaussian (Forthcomming Issue) 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