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Jurnal Gaussian

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Published by Universitas Diponegoro

ISSN : - EISSN : 23392541 DOI : -

Core Subject : Education,

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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.

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Search results for , issue "Vol 7, No 2 (2018): Jurnal Gaussian" : 10 Documents clear

OPTIMALISASI PORTOFOLIO SAHAM MENGGUNAKAN METODE MEAN ABSOLUTE DEVIATION DAN SINGLE INDEX MODEL PADA SAHAM INDEKS LQ-45 Diah Wulandari; Dwi Ispriyanti; Abdul Hoyyi
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Stock investment is the planting of money in a securities that indicates the ownership of a company in order to provide benefits in the future. In obtaining optimal results from stock investments, investors are expected to create a series of portfolios. The portfolio will help investors in allocating some funds in different types of investments in order to achieve optimal profitability. For selection of optimal stocks representing LQ-45 Index, used 2 methods of Mean Absolute Deviation (MAD) method and Single Index Model (SIM) method. In MAD method, 5 best stocks are BBCA with weight 23%, INDF 8%, KLBF 23%, TLKM 23%, and UNVR 23%. While the SIM method of candidate portfolio obtained is AKRA with weight 15,459%, BBCA 48,193%, BBNI 5,028%,KLBF 0,258% and TLKM 31,062%. Portfolio performance meter is used by sharpe ratio. The value of sharpe ratio is 0,36754 for optimal portfolio using MAD method and 0,40782 for optimal portfolio using SIM method, this means that optimal portfolio using SIM method has better performance than MAD. Keywords: Investment, Portfolio, Index LQ-45, Mean Absolute Deviation, Single Index Model, Sharpe Ratio

PEMBENTUKAN PORTOFOLIO SAHAM DENGAN METODE MARKOWITZ DAN PENGUKURAN VALUE AT RISK BERDASARKAN GENERALIZED EXTREME VALUE (Studi Kasus: Saham Perusahaan The IDX Top Ten Blue 2017) Ria Epelina Situmorang; Di Asih I Maruddani; Rukun Santoso
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In financial investment, investors will try to minimize risk and increase returns for portfolio formation. One method of forming an optimal portfolio is the Markowitz method. This method can reduce the risk and increase returns. The performance portfolio is measured using the Sharpe index. Value at Risk (VaR) is an estimate of the maximum loss that will be experienced in a certain time period and level of trust. The characteristics of financial data are the extreme values that are alleged to have heavy tail and cause financial risk to be very large. The existence of extreme values can be modeled with Generalized Extreme Value (GEV). This study uses company stock data of The IDX Top Ten Blue 2017 which forms an optimal portfolio consisting of two stocks, namely a combination of TLKM and BMRI stocks for the best weight of 20%: 80% with the expected return rate of 0.00111 and standard deviation of 0.01057. Portfolio performance as measured by the Sharpe index is 1,06190 indicating the return obtained from investing in the portfolio above the average risk-free investment return rate of -0,01010. Risk calculation is obtained based on Generalized Extreme Value (GEV) if you invest both of these stocks with a 95% confidence level is 0,0206 or 2,06% of the current assets. Keywords: Portfolio, Risk, Heavy Tail, Value at Risk (VaR), Markowitz, Sharpe Index, Generalized Extreme Value (GEV).

PEMODELAN PENGELUARAN PER KAPITA DAN PERSENTASE PENDUDUK MISKIN DI JAWA TENGAH MENGGUNAKAN REGRESI BIRESPON SPLINE TRUNCATED Merinda Pangestikasari; Rita Rahmawati; Dwi Ispriyanti
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The Central Bureau of Statistics states that the average per capita spending (Y1) of Central Java Community in 2016 is around 27.808 rupiah per day. This value is still considered low, because it covers all the needs of an individual's life. The low expenditure per capita indicates the low level of welfare. Another indicator that can be used to measure community welfare is the percentage of poverty (Y2). Through this variable can be known how proportion of people who still difficult to meet their needs. Many factors are suspected to affect welfare, one of which is the average variable of school length (X). This study aims to get the best model and know the goodness of the model. Approach is done by nonparametric regression that is regres biresponse spline truncated. Nonparametric approach is done when data function does not show certain pattern. The best spline truncated biresponse model is highly dependent on determining the order and location of the optimal knot point that has a minimum Mean Square Error (MSE) value. In this study, the best model is obtained when order of Y1 is 2 and order of Y2 is 2 with five knots. The location of the knot point obtained is 7,05; 7,17; 7,32; 9,82 and 10,29 with MSE value of 662634,2. The goodness of the model is measured based on R-Square and MAPE, R-Square=43,21%, means the variance of response variables that can be explained by the predictor variable are 43,21% while the rest is influenced by other variables and MAPE=14,25%. Based on the value of MAPE can be said that the model had a good performance. Keywords: Welfare, Expenditure, Percentage of Povery, Birespon Spline, Truncated, MSE

PENERAPAN METODE WEIGHTED PRODUCT (WP) DAN ELIMINATION ET CHOIX TRANDUISANT LA REALITÉ (ELECTRE) DENGAN PEMBOBOTAN ENTROPY MENGGUNAKAN GUI MATLAB (Studi Kasus: Pemilihan Hero Terkuat Arena of Valor) Sukanianto, Eko Adyan; Sugito, Sugito; Rahmawati, Rita
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Arena of Valor (AOV) is a mobile game published by Garena in Indonesia. There will be 5 players in each team, selecting a hero to play in the game. By selecting the strongest hero each role can help facilitate team to strategize the composition of heroes that will be used to achieve victory. Weighting each criteria and selecting the strongest hero also become a consideration to control the game to be stable and balanced by the developer. The alternatives are all hero from each role (Tank, Warrior, Assassin, Mage, Archer and Support), while the criterias are skill effect points, maximum HP (Health Points), physical attack, physical defense, movement speed and HP recovery every 5 seconds. In this study, the writer uses WP and ELECTRE methods to select the strongest hero with Entropy weighting method. This study produce a Matlab GUI that can be used to facilitate computational selection. The results show that the strongest hero in AOV are Grakk (Tank), Astrid (Warrior), Ormarr (Warrior), Murad (Warrior/Assassin), Lauriel (Mage/Assassin), The Joker (Archer) and Alice (Support). While the criteria with the highest weighting is the skill. Keywords: AOV, Garena Indonesia, WP, ELECTRE, Entropy, GUI Matlab

PENGUKURAN PROBABILITAS KEBANGKRUTAN OBLIGASI KORPORASI DENGAN SUKU BUNGA COX INGERSOLL ROSS MODEL MERTON (Studi Kasus Obligasi PT Indosat, Tbk) Muhammad Akhir Siregar; Mustafid Mustafid; Rukun Santoso
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Nowadays bonds become one of the many securities products that are being prefered by investors. Observing the level of the company's rating which good enough or in the criteria of investment grade can’t be a handle of investors. Investing in long-term period investors should understand the risks to be faced, one of investment credit risk on bonds is default risk, this risk is related to the possibility that the issuer fails to fulfill its obligations to the investor in due date. The measurement of the probability of default failure by the structural method approach introduced first by Black-Scholes (1973) than developed by Merton (1974). In Bankruptcy model, merton’s model assumed the company get default (bankrupt) when the company can’t pay the coupon or face value in the due date. Interest rates on the Merton model assumed to be constant values replaced by Cox Ingersoll Ross (CIR) rates. The CIR rate is the fluctuating interest rate in each period and the change is a stochastic process. The empirical study was conducted on PT Indosat, Tbk's bonds issued in 2017 with a face value of 511 Billion in payment of obligations by the issuer for 10 years. Based on simulation results done with R software obtained probability of default value equal to 7,416132E-215 Indicates that PT Indosat Tbk is deemed to be able to fulfill its obligation payment at the end of the bond maturity in 2027. Keywords: Bond, CIR Rate, Merton Model, Ekuity, Probability of default

ROBUST GEOGRAPHICALLY WEIGHTED REGRESSION DENGAN METODE MUTLAK SIMPANGAN TERKECIL PADA PEMODELAN KEJADIAN DIARE DI KOTA SEMARANG Ika Chandra Nurhayati; Agus Rusgiyono; Hasbi Yasin
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Diarrhea is one of many health issues in developing country like Indonesia, because the sickness and the death number are still high. According to health profile of Semarang City, the people who suffer from diarrhea from 2010-2015 are decreasing. The lowest point happened at the year 2013 with the total case of 38.001, however there are an increasing number from 2014-2015. The distribution data of diarrhea is a spatial data. The differences between environment and sanitation could cause spatial heterogeneity. The spatial heterogeneity could cause the produced variant value no longer constant, but instead it is different on each region. Therefore, regression model that involves the effects of spatial heterogeneity is needed, which are Geographically Weighted Regression (GWR) that is built by Weighted Least Square (WLS) adjuster. Although, GWR parameter adjuster that used WLS is very sensitive with the existence of outliers. The existence of the outlier in the data will create a huge residual. Thus, more robust method is needed, which is Least Absolute Deviation (LAD) methods in order to estimate the parameter on model GWR. This model is called Robust GWR (RGWR). The result shows that the model events of diarrhea on each region in Semarang City are different. Furthermore, the model events of diarrhea with RGWR model generate MAPE 16,3396% which means the performance of RGWR is formed well. Keyword: Diarrhea, Robust, Geographically Weighted Regression, Least Absolute Deviation

GUI MATLAB UNTUK METODE FUZZY SAW DAN FUZZY TOPSIS DALAM PEMILIHAN PENERIMA BEASISWA PPA DENGAN PEMBOBOTAN ENTROPI (Studi Kasus : Pemilihan Penerima Beasiswa PPA tahun 2017 Mahasiswa FSM UNDIP, Semarang) Rahmaniar, Ratna; Widiharih, Tatik; Ispriyanti, Dwi
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

For students, scholarships are important to ease the burden on parents, namely tuition fees.The large number of scholarship applicants is a challenge for FSM to be able to provide an appropriate, effective and efficient decision to manage data on scholarship recipients who are truly entitled to receive scholarships. Prospective scholarship recipients are selected based on the criteria determined by FSM.The criteria determined by the FSM are GPA (Grade Point Average), parent income, number of certificates, number of dependents of parents, semester, and electricity. The method applied to select 170 PPA scholarship recipients (Academic Achievement Improvement) is FSAW (Fuzzy Simple Additive Weighting) and FTOPSIS (Fuzzy Technique for Order Preference by Similarity to Ideal Solution) with entropy weighting. This entropy weighting does a combination of the initial weight that has been determined by FSM and the calculation weight. This research was conducted with the help of MATLAB (Matrix Laboratory) GUI (Graphical User Interface) as a computing tool. With the MATLAB GUI system built, it can simplify and speed up the selection process. FSAW and FTOPSIS calculation results are 96% the same, while FSAW with FSM is only 39% the same and FTOPSIS with FSM is only 42% the same.The FSAW and FTOPSIS methods are better used than the determination of the FSM, because the results of the FSM are not appropriate.FSM selects manually by looking at files collected by registrants. Keywords:Scholarship, FSAW, FTOPSIS, Entropy, GUI

PEMODELAN RETURN HARGA SAHAM MENGGUNAKAN MODEL INTERVENSI–ARCH/GARCH (Studi Kasus : Return Harga Saham PT Bayan Resources Tbk) Dea Manuella Widodo; Sudarno Sudarno; Abdul Hoyyi
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The intervention method is a time series model which could be used to model data with extreme fluctuation whether up or down. Stock price return tend to have extreme fluctuation which is caused by internal or external factors. There are two kinds of intervention function; a step function and a pulse function. A step function is used for a long-term intervention, while a pulse function is used for a short-term intervention. Modelling a time series data needs to satisfy the homoscedasticity assumptions (variance of residual is homogeneous). In reality, stock price return has a high volatility, in other words it has a non-constant variance of residuals (heteroscedasticity). ARCH (Autoregressive Conditional Heteroscedasticity) or GARCH (Generalized Autoregressive Conditional Heteroscedasticity) can be used to model data with heteroscedasticity. The data used is stock price return from August 2008 until September 2018. From the stock price return data plot is found an extreme fluctuation in September 2017 (T=110) that is suspected as a pulse function. The best model uses the intervention pulse function is ARMA([1,4],0) (b=0, s=1, r=1). The intervention model has a non-constant variance or there is an ARCH effect. The best variance model obtained is ARMA([1,4],0)(b=0, s=1, r=1)–GARCH(1,1) with the AIC value is -205,75088. Keywords: Stock Return, Intervention, Heteroscedasticity, ARCH/GARCH

ANALISIS DAMPAK SHOCK VOLUME PERDAGANGAN SAHAM PADA INDEKS HARGA SAHAM CONSUMER GOODS DENGAN STRUCTURAL VECTOR AUTOREGRESSIVE (SVAR) Infan Nur Kharismawan; Rukun Santoso; Budi Warsito
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The stock trading in the capital market will result daily volume of trading stock that impact on stock price. One of the indicators that describes the stock price movement is stock index. There are many types of stock index, one of them is consumer goods stock index. Stock index is a sensitive economic variable affected by shock and need a restriction to form its economic model. Based on that, Structural Vector Autoregressive (SVAR) is used to describe its economic model. SVAR is formed by a stable VAR, fulfilled white noise, k-variate normal distribution. The purpose of this study are to forecast data on each variables and analyze the impact of the shock through the descriptions of variance decomposition. VAR used as the basis for SVAR is VAR(8) whose the forming variable stationary at the first different degree. Performances of forecasting SVAR using MAPE (Mean Absolute Percentage Error) for in sample data are 13.87434% (volume of trading stock) and 0.87045% (consumer goods stock index) and for out sample data are 14.22964% (volume of trading stock) and 1.76054% (consumer goods stock index). Response of consumer goods stock index to the impact of the volume of trading stock shock shown by proportion of variance decomposition tends to increase, while the shock by itself has decreased until reach its equilibrium point. Keywords:cosumer goods stock index, SVAR, variance decomposition, volume of trading stock

PEMODELAN VOLATILITAS RETURN PORTOFOLIO SAHAM MENGGUNAKAN FEED FORWARD NERURAL NETWORK (Studi Kasus :PT Bumi Serpong Damai Tbk. Dan PT H.M Sampoerna Tbk.) Rizki Pradipto Widyantomo; Abdul Hoyyi; Tatik Widiharih
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

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.

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