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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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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 733 Documents
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PERHITUNGAN PREMI MURNI PADA SISTEM BONUS MALUS UNTUK FREKUENSI KLAIM BERDISTRIBUSI BINOMIAL NEGATIF DAN BESAR KLAIM BERDISTRIBUSI WEIBULL PADA DATA ASURANSI KENDARAAN BERMOTOR DI INDONESIA Rillifa Iris Adisti; Aceng Komarudin Mutaqin
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.v10i2.30084

Abstract

System bonus malus is one of the systems offered by an insurance company where the risk premium calculation is based on the claim history of each policyholder. In study will be discussed premium calculation in system, bonus malus  where the frequency of claims has a negative binomial distribution and the size of claims is Weibull distribution on motor vehicle insurance data in Indonesia. This method will producesystem an bonus malus optimal by finding the posterior distribution using Bayes analysis. As the application material used secondary data from the recording results obtained from the general insurance company PT. XYZ in 2014, data contains data on the frequency of claims and the amount ofclaims partial loss of policyholders forinsurance products for comprehensivemotor vehicle insurance category 8 regions 3.The results of the implementation show that the premiums with the system are bonus malus optimalconsidered fair enough because the premiums paid by policyholders insurance that extends the policy in the following year is proportional to the risk it faces, where the premium to be paid by each policyholder is based on past claims history. Keywords: system bonus malus, negative binomial distribution, Weibull distribution, comprehensive,  partial loss.
PEMODELAN TRANSFORMASI FAST-FOURIER PADA VALUASI OBLIGASI KORPORASI (Studi Kasus: PT. Bank Danamon Tbk, PT. Bank CIMB Niaga Tbk, dan PT. Bank UOB Indonesia Tbk) Ubudia Hiliaily Chairunnnisa; Abdul Hoyyi; Hasbi Yasin
Jurnal Gaussian Vol 10, No 1 (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.v10i1.30937

Abstract

The basic assumption that is often used in bond valuations is the assumption on the Black-Scholes model. The practical assumption of the Black-Scholes model is the return of assets with normal distribution, but in reality there are many conditions where the return of assets of a company is not normally distributed and causing improperly developed bond valuation modeling. The Fast-Fourier Transform model (FFT) was developed as a solution to this problem. The Fast-Fourier Transformation Model is a Fourier transformation technique with high accuracy and is more effective because it uses characteristic functions. In this research, a modeling will be carried out to calculate bond valuations designed to take advantage of the computational power of the FFT. The characteristic function used is the Variance Gamma, which has the advantage of being able to capture data return behavior that is not normally distributed. The data used in this study are Sustainable Bonds I of Bank Danamon Phase I Year  2019 Series B, Sustainable Bonds II of Bank CIMB Niaga II Phase IV Year 2018 Series C, Sustainable Subordinated Bonds II of Bank UOB Indonesia Phase II 2019. The results obtained are FFT model using the Variance Gamma characteristic function gives more precise results for the return of assets with not normal distribution.  Keywords: Bonds, Bond Valuation, Black-Scholes, Fast-Fourier Transform, Variance Gamma
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.
PENENTUAN MODEL ANTREAN NON-POISSON DAN PENGUKURAN KINERJA PELAYANAN BUS RAPID TRANSIT TRANS SEMARANG (STUDI KASUS: SHELTER PEMBERANGKATAN BRT KORIDOR V) Purwati Ayuningtyas; Sugito Sugito; Di Asih I Maruddani
Jurnal Gaussian Vol 10, No 1 (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.v10i1.30932

Abstract

One of the queue systems that is often found  in daily life is the transportation service system, for example a queue system at the shelters departure of corridor V Bus Rapid Transit (BRT) Trans Semarang. Corridor V has three departure shelters, they are Shelter Victoria Residence, Shelter Marina, and Shelter Bandara Ahmad Yani. Corridor V was choosen, because of its high load factor on January to June 2019. Based on the observation, the service time at the departure shelter is usually longer than the normal shelter. This causes the rise of queue at the departure shelters. The queue at the departure shelters can hamper the arrival of BRT at the other shelters, so the application of the queue theory is needed to find out the extent of operational effectiveness at the departure shelters. The resulting queue model is the Non-Poisson queue model, the queue model for Victoria Residence Shelter: (DAGUM/GEV/1):(GD/∞/∞), Marina Shelter: (DAGUM/G/1):(GD/∞/∞), and Bandara Ahmad Yani Shelter: (GEV/GEV/1):(GD/∞/∞). Based on the value from measurement of the queue system performance, it can be conclude that the three departure shelters of corridor V BRT Trans Semarang have some optimal condition. Keywords: Shelter Departure of Corridor V, Non-Poisson Queueing Model, Dagum, Generalized Extreme Value, System Perfomance Measure  
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.
MODERATING STRUCTURAL EQUATION MODELING DENGAN PARTIAL LEAST SQUARE PADA PEMODELAN PENERIMAAN DAN PENGGUNAAN DOMPET DIGITAL DI KOTA SEMARANG Nisa, Mukrimatun; Sudarno, Sudarno; Sugito, Sugito
Jurnal Gaussian Vol 10, No 1 (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.v10i1.30044

Abstract

Digital wallets (e-wallets) are a technology that provides a new perspective for the public on non-cash payments that are far more practical and secure in transactions. The purpose of this study is to determine the factors that influence the success or failure of implementing digital wallets (e-wallets) using the variant-based Structural Equation Modeling method (Partial Least Square). In this decade, an approach has been developed that allows the relationship between an independent variable to the dependent variable which is influenced by other latent variables called Moderating Structural Equation Modeling (MSEM), so this study uses MSEM by measuring it using the ping method. The results of the analysis show that the factors that influence acceptance defined as interest in using the technology are social influences, faciliating conditions and consumer habits. Meanwhile, the factors that influence the use of digital wallets which are defined as usage behavior are interest in use and conditions that facilitate. The use of digital wallets (e-wallets) is also indirectly influenced by social influences, conditions that facilitate and consumer habits. the factor of supporting facilities from the issuer of digital wallets (e-wallets) is a factor that affects directly and indirectly the use of digital wallets (e-wallets). Analysis of Moderating Structural Equation Modeling (MSEM) using the ping method results that experience does not affect the acceptance and use of digital wallets (e-wallets) as moderation Keywords: Acceptance and Use Model, Digital Wallet (E-wallet), Partial Least Square, Moderating Structural Equation Modeling.
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
GRAFIK PENGENDALI MIXED EXPONENTIALLY WEIGHTED MOVING AVERAGE – CUMULATIVE SUM (MEC) DALAM ANALISIS PENGAWASAN PROSES PRODUKSI (Studi Kasus : Wingko Babat Cap “Moel”) Aulia Resti; Tatik Widiharih; Rukun Santoso
Jurnal Gaussian Vol 10, No 1 (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.v10i1.30938

Abstract

Quality control is an important role in industry for maintain quality stability.  Statistical process control can quickly investigate the occurrence of unforeseen causes or process shifts using control charts. Mixed Exponentially Weighted Moving Average - Cumulative Sum (MEC) control chart is a tool used to monitor and evaluate whether the production process is in control or not. The MEC control chart method is a combination of the Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) charts. Combining the two charts aims to increase the sensitivity of the control chart in detecting out of control. To compare the sensitivity level of the EWMA, CUSUM, and MEC methods, the Average Run Length (ARL) was used. From the comparison of ARL values, the MEC chart is the most sensitive control chart in detecting out of control compared to EWMA and CUSUM charts for small shifts. Keywords: Grafik Pengendali, Exponentially Weighted Moving Average, Cumulative Sum, Mixed EWMA-CUSUM, Average Run Lenght, EWMA, CUSUM, MEC, ARL
PENGARUH TRANSFORMASI DATA PADA METODE LEARNING VECTOR QUANTIZATION TERHADAP AKURASI KLASIFIKASI DIAGNOSIS PENYAKIT JANTUNG Arafa Rahman Aziz; Budi Warsito; Alan Prahutama
Jurnal Gaussian Vol 10, No 1 (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.v10i1.30933

Abstract

Learning Vector Quantization (LVQ) is a type of Artificial Neural Network with a supervised learning process based on competitive learning. Despite the absence of assumptions in LVQ is an advantage, it can be a problem when the predictor variables have big different ranges.This problems can be overcome by equalizing the range of all variables by data transformation so that all variables have relatively same effect. Heart Disease UCI dataset which used in this study is transformed by several transformation methods, such as minmax, decimal scaling, z-score, mean-MAD, sigmoid, and softmax. The result show that the six transformed data can provide better LVQ classification accuracy than the raw data which has 75.99% for training performance accuracy. LVQ classification accuracy with data transformation of minmax, decimal scaling, z-score, mean-MAD, sigmoid, and softmax are 89.16%, 88.22%, 89.7%, 90.1%, 88.17% and 92.18%. Based on the One-way ANOVA test and DMRT post hoc test  known that there are significant differences between the results of the classification with data transformations and raw data in 0,05 significant level of α. It is also known that the best data transformation methods are softmax for training and sigmoid for testing. Keywords: heart disease, neural network, learning vector quantization, classification, data transformation
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

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