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Jurnal Gaussian
Published by Universitas Diponegoro
ISSN : 23392541     EISSN : -     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 477 Documents
PEMODELAN DAN PERAMALAN VOLATILITAS PADA RETURN SAHAM BANK BUKOPIN MENGGUNAKAN MODEL ASYMMETRIC POWER AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY (APARCH) Rohmaningsih, Nur Musrifah; Sudarno, Sudarno; Safitri, Diah
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

Stock is a sign of ownership of an individual or entity within a corporation or limited liability company. While the stock price index is a reflection of the movement of the stock price. Stock investments can not avoid the risk, so we need a model that can predict stock returns and volatility. Models are often used is ARCH/GARCH models. On the stock market also shows asymmetric effect(leverage), which is a negative relationship between the change in the value of returns with volatility movement. So, the model can be used is Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) model. APARCH model chosen to modeling and forecasting the volatility of Bukopin return stock is APARCH (1,2) model Keywords: Stock, volatility, asymmetric, return, APARCH
PEMODELAN REGRESI NONPARAMETRIK DATA LONGITUDINAL MENGGUNAKAN POLINOMIAL LOKAL (STUDI KASUS: HARGA PENUTUPAN SAHAM PADA KELOMPOK HARGA SAHAM PERIODE JANUARI 2012 – APRIL 2015) Khalid, Izzudin; Suparti, Suparti; Prahutama, Alan
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Stocks are securities that can be bought or sold by individuals or institutions as a sign of participating or possessing a company in the amount of its proportions. From the lens of market capitalization values, stocks are divided into 3 groups: large capitalization (Big-Cap), medium capitalization (Mid-Cap) and small capitalization (Small-Cap). Longitudinal data is observation which is conducted as n subjects that are independent to each subject observed repeatedly in different periods dependently. Smoothing technique used to estimate the nonparametric regression model in longitudinal data is local polynomial estimator. Local polynomial estimator can be obtained by WLS (Weighted Least Square) methods. Local polynomial estimator is very dependent on optimal bandwidth. Determination of the optimal bandwidth can be obtained by using GCV (Generalized Cross Validation) method. Among the Gaussian kernel, Triangle kernel, Epanechnikov kernel and Biweight kernel, it is obtained the best model using Gaussian kernel. Based on the application of the model simultaneously, it is obtained coefficient of determination of 97,80174% and MSE values of 0,03053464. Using Gaussian kernel, MAPE out sample of data is obtained as 11,74493%. Keywords: Longitudinal Data, Local Polynomial, Stocks
PEMODELAN VOLATILITAS RETURN PORTOFOLIO SAHAM MENGGUNAKAN FEED FORWARD NERURAL NETWORK (STUDI KASUS :PT BUMI SERPONG DAMAI TBK. DAN PT H.M SAMPOERNA TBK.) Widyantomo, Rizki Pradipto; Hoyyi, Abdul; Widiharih, Tatik
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Time series analysis is an analysis used to predict a time-observed data, one of which is the ARIMA model. ARIMA model assumes a constant residual variance (homogeneous). While financial data usually produce ARIMA model with variance error that is not constant. If the assumption of homogeneity of the residual variance is not met, then the method that can be used is ARCH or GARCH model. Another method that can be used on the data assuming the homogeneity of the variance error is not met is the Neural Network model. In this model we use Neural Network model with variance and residual as the input variables that obtained from ARCH / GARCH model. The data used are BSDE and HMSP asset portfolio returns from November 14, 2016 to January 18, 2018. In this study the selected input variables are from ARIMA (1.0.1) GARCH (1,1) model. The best Neural Network model obtained is Neural Network model with 10 hidden layers with MSE value 6.58 x10-10 with model train evaluation which is MAPE value 1.14441%.Keywords: Time series Analysis, ARCH / GARCH, Neural Network, Return.
ANALISIS PREFERENSI SISWA SMA DI KOTA SEMARANG TERHADAP PROGRAM STUDI DI PERGURUAN TINGGI DENGAN METODE CHOICE-BASED CONJOINT Anggreani, Dini; Mukid, Moch. Abdul; Rusgiyono, Agus
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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This research aims to determine the design of study program that has the biggest opportunity to be chosen by the students. One method can be used to determine the preferences of high school students on existing study program in college is choice-based conjoint method. Variables used in this research are a minimum value of accreditation of selected study program that consist of three categories (A, B, and C), field of science study program that consist of two categories (exact sciences and not exact sciences), type of study program that consist of two categories (educational and not educational), and education level that consist of three categories (S1, D4, and D3). Data analysis techniques used in the choice-based conjoint method is conditional logit model. Variables order starting from the biggest contribution in influencing students preferences is accreditation of study program, level of education, type of study program, and field of science. The design of study program most likely to be chosen by the students is a study program with accreditation A, not exact sciences field, not educational type, and S1 level.
PEMODELAN PENDAPATAN ASLI DAERAH (PAD) DI KABUPATEN DAN KOTA DI JAWA TENGAH MENGGUNAKAN GEOGRAPHICALLY WEIGHTED RIDGE REGRESSION Veronica, Depy; Yasin, Hasbi; Widiharih, Tatik
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

Linear Regression Analysis is a statistical method for modeling the relation between response variable and predictor variable. Geographically Weighted Regression (GWR) is an expansion of linear regression model if spatial heterogeneity occurred. Local multicollinearity test is required to know the presence of linear correlation between independent variables for each observation location. Geographically Weighted Ridge Regression (GWRR) is a extension of GWR model to solve local multicollinearity problem. Parameter estimation for GWR and GWRR model is done using Weighted Least Square (WLS) method by applying optimum bandwith with Cross Validation (CV) criteria. GWRR model is applied on locally generated recurring revenues (PAD) at district and city of Central Java and its result shows the ability of GWRR model to erase multicollinearity problem. Based on Mean Squared Error (MSE) and Akaike Information Criterion (AIC) value for GWR and GWRR model, it is know that the best model to analyze locally generated recurring revenues (PAD) at district and city of Central Java is GWRR model with the smallest MSE and AIC value. Keywords : Akaike Information Crietion, Spasial Heterogeneity, Geographically Weighted Ridge Regression, Mean Square Error, Local Multicoliniearity
ANALISIS FAKTOR – FAKTOR YANG MEMPENGARUHI JUMLAH KEJAHATAN PENCURIAN KENDARAAN BERMOTOR (CURANMOR) MENGGUNAKAN MODEL GEOGRAPHICALLY WEIGHTED POISSON REGRESSION (GWPR) Haris, Muhammad; Yasin, Hasbi; Hoyyi, Abdul
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Theft is an act taking someone else?s property, partially or entierely, with intention to have it illegally. Motor vehicle theft is one of the most highlighted crime type and disturbing the communities. Regression analysis is a statistical analysis for modeling the relationships between response variable and predictor variable. If the response variable follows a Poisson distribution or categorized as a count data, so the regression model used is Poisson regression. Geographically Weighted Poisson Regression (GWPR) is a local form of Poisson regression where data sampling location is prioritized. GWPR model is used for identifying the factors that influence the numbers of motor vehicles theft, either using a weighted gauss kernel function or bisquare kernel function. Based on the value of Akaike Information Criterion (AIC) of Poisson regression and GWPR model, it is analyzed that GWPR model using a weighted fixed bisquare kernel function is the best model for analyzing the number of motor vehicles theft at every Sub-Districts in the Semarang city in 2012, because it has the smallest AIC value. This model has a precision of 88,81%.Keywords: Motor Vehicle Theft, Geographically Weighted Poisson Regression, Kernel Gauss Function, Kernel Bisquare Function, Akaike Information Criterion
PENERAPAN PENGENDALIAN KUALITAS DENGAN MEWMA DAN FUNGSI DENSITAS KERNEL MULTIVARIAT (STUDI KASUS: PT SUKOREJO INDAH TEXTILE KAB. BATANG) Sany, Mifta Fara; Santoso, Rukun; Hakim, Arief Rachman
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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In an era of industrial revolution 4.0, technology is increasingly sophisticated, requiring companies to be more creative. Product quality control is an effort to minimize the defective products produced by the company. The production of weaving sarongs at PT SUKORINTEX pays attention to the accuracy of the length and width of the sarong to conform to the standards set by the company. To find out the quality of woven sarong products at PT SUKORINTEX, analysis was performed using Multivariate Exponentially Weighted Moving Average (MEWMA) control charts and multivariate kernel control charts. The research variable was the characteristics of the X sarongs which is reflected in 2 variates, namely the average length and average width. Based on the results and discussion that has been done, the MEWMA control chart used a weighting ? which is determined using trial and error. MEWMA control charts can be said to be stable and controlled by ? = 0.1, Upper Control Limit (UCL) of 14.62943, and Lower Control Limit (LCL) of 0. Multivariate kernel control chart were declared uncontrolled with ? = 0.1 and level = 0.06130611 because there were data that was outside the contour. Chart improvement was done by trial and error and obtained a controlled chart results at ? = 0.01 and a level value of 0.03125701. Based on this case study, the quality control of the average length and width of WADIMOR woven sarong types 30 STR with MEWMA is better than the multivariate kernel density, because MEWMA is controlled and stable in controlling product quality. The results of the MEWMA control chart show a capable process because more than 1 process capability index value is obtained. Keywords: Multivariate Exponentially Weighted Moving Average (MEWMA) control chart, multivariate kernel control chart, process capability.
VALUASI COMPOUND OPTION PUT ON PUT TIPE EROPA Sutarno, Yulia Agnis; Maruddani, Di Asih I; Sugito, Sugito
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Options are one of the form of investment which a contract that gives the right (not obligation) to the option holder to buy (call options) or sell (put options) the underlying asset by a certain date for a certain price. Option price is a reflection of the intrinsic value of the option and any additional amount over intrinsic value. One type of options that are traded is compound options. Compound option model is introduced by Robert Geske in 1979. Compound options are options on options. Compound option put on a put is put option where the underlying assets are another put option. The compound option put on put will be exercised on the first exercise date only if the value of the put option on that date is less than the first stike price. An empirical study using compound option put on a put stocks of Apple Inc which is strike price compound option US$ 560, strike price put option US$ 585, with the first exercise date on March 28, 2014 and the second exercise date on May 17, 2014. The theoritical price of compound option put on put on stocks of Apple Inc is US$ 501.4566.
IMPLEMENTASI METODE SIX SIGMA MENGGUNAKAN GRAFIK PENGENDALI EWMA SEBAGAI UPAYA MEMINIMALISASI CACAT PRODUK KAIN GREI Wahyuningtyas, Ayudya Tri; Mustafid, Mustafid; Prahutama, Alan
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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The quality being a very important aspect for consumer to choose products beside price that competes. In production process grey fabric there are several kinds of defects, the defects can cause to decrease of grade fabric produced. Six sigma method is a method that can be used to analyze defect rate to approach zero defect products. A procedure used for quality improvement toward the target that the concept of six sigma DMAIC. This study aims to implement six sigma method and EWMA control chart in quality control of product quality cloth of grey. The results obtained in this study is one the whole production process produces DPMO value of 24790.97 with sigma quality level of 3.464 means that the product of one million cloth of grey there are 24790.97 meters of product that does not fit in production. In the calculation process capability, process capability ratio value obtained more than 1 means that the process is going well and meets the specifications that have been established, but it is still possible to be improved so that the products resulting better. Keywords: Quality, Quality Control, Six Sigma, EWMA
ANALISIS KETAHANAN HIDUP PENDERITA DENGUE HEMORRHAGIC FEVER (DEMAM BERDARAH) DENGAN REGRESI COX KEGAGALAN PROPORSIONAL SENSOR TIPE III STUDI KASUS DI RUMAH SAKIT UMUM DAERAH (RSUD) TEMANGGUNG Afifi, Irfan; Maruddani, Di Asih I; Hoyyi, Abdul
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Dengue Fever is a disease caused by the dengue virus, transmitted from person to person through the bite of Aedes Aegypti and Aedes Albopictus mosquitoes. Dengue Fever mainly found in the tropical countries, such as Indonesia. According to World Health Organization (WHO) data, Indonesia reported as the 2nd country with the largest dengue cases among 30 endemic countries between 2004 until 2010.  Therefore, it is important to identify the factors influencing the recovery speed of dengue patients. This study utilize statitistical approach through regression analysis. One of the analysis methode choosen is survival analysis. This analysis is utilized to figure out the time series data analysis, of origin undefined time until the occurrence of certain events. In Survival Analysis, one of the regression method which is commonly used is  Cox regression. This study uses statistical methods approach through Cox regression proportional hazard to take into consideration the time of failure as the dependent variable. as well as the response variable function tends to a constant failure. object of research in this study are patients with dengue fever and the time the patient entered in a separate viewing the selected sensor type III This study used medical records of dengue fever patients of regional public hospital in Temanggung City, Central Java, from period of January to November 2016. Results obtained shows that the factors affecting the recovery speed of patients is Hematocrit state of the patient. Patients with normal Hematocrit state have faster recovery that patients with upnormal circumtances.  Keywords: Dengue, Survival Analysis, Regression Cox Proportional Hazard

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