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Jurnal Gaussian

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Published by Universitas Diponegoro

ISSN : - EISSN : 23392541 DOI : -

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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 8, No 1 (2019): Jurnal Gaussian" : 15 Documents clear

ANALISIS ANTREAN DAN KINERJA SISTEM PELAYANAN GARDU TOL OTOMATIS GERBANG TOL MUKTIHARJO (Studi Kasus: Gardu Tol Otomatis Gerbang Tol Muktiharjo) Erna Fransisca Angela Sihotang; Sugito Sugito; Moch. Abdul Mukid
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Queue process is a process related to the arrival of customers in a service facility, waiting in line queue if it cannot be served, get service and finally leaves the facility after being served. Research on the queue process can be seen directly through the queue system at the automatic toll booth Muktiharjo. Queue models and their distribution were obtained using the Sigma Magic program. The model of the vehicle queue system at the Muktiharjo Automatic Toll Gate is (GAMM/ GAMM/ 4): (GD/ ∞/ ∞). Based on the values of the queue system performance measures obtained through the MATLAB GUI program as a whole it can be concluded that the queue of vehicles at the Muktiharjo Automatic Toll Gate has a condition where the average number of vehicles estimated in the system every 15 minutes is 25,5646 vehicles. The average number of vehicles in the queue system every 15 minutes is 24,5639 vehicles. The waiting time in the system is estimated to be around 7,99332 seconds. The estimated waiting time in line is around 7,68042 seconds. The queue system has a busy opportunity of 63.2849% and the remaining 36.7106% is a chance the queue system is not busy. The simulation of the vehicle queue system at the Automatic Toll Gate of Muktiharjo Toll Gate by using ARENA is optimal with the number of service points as many as 4 automatic toll booths. Keywords: Automatic Toll Booth, Queue, Gamma Distribution, Performance Size, Queue Simulation

ANALISIS TEKNIKAL SAHAM DENGAN INDIKATOR GABUNGAN WEIGHTED MOVING AVERAGE DAN STOCHASTIC OSCILLATOR Yustian Dwi Saputra; Di Asih I Maruddani; Abdul hoyyi
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The Stochastic Oscillator which is one of the leading indicators has the disadvantage of opening the gap for false signals. To minimize false signals, the smoothing process is carried out using the Moving Average. Stochastic Oscillator is usually combined with SMA (Simple Moving Average). But SMA has the disadvantage of giving the same weight to all data, even though in reality the data that best reflects the next value is the last data. This makes the basis of weighting the WMA (Weighted Moving Average) method.This study aims to test the combination of Stochastic Oscillator with SMA and WMA and use the best combination to predict the trends that will occur and trading decisions taken from the results of these predictions. The research samples were ANTM, BBRI, and GIAA stocks from November 9 2015 to November 9, 2018.The results show a combination of Stochastic Oscillator and WMA is a better combination of predictions than Stochastic Oscillator and SMA because it has a smaller MSE value. Based on the comparison of signal accuracy based on Overbought and Oversold, the best period of combination of Stochastic Oscillator and WMA is period 25. From the predicted trend that will occur with a combination of Stochastic Oscillator and WMA period 25 a decision is made to buy shares for ANTM shares, sell shares for BBRI shares, and waiting for a buy signal for GIAA shares.Keywords: Stochastic Oscillator, SMA, WMA, Predictions, Trends

ANALISIS PORTOFOLIO OPTIMAL MENGGUNAKAN MULTI INDEX MODEL (Studi Kasus: Kelompok Saham IDX30 periode Januari 2014 – Desember 2018) Bramadita Kunni Fauziyyah; Alan Prahutama; Sudarno Sudarno
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Investment is the placement of a number of funds at this time in the hope of making a profit in the future. The purpose of investors investing is to get many profit by understanding that there is a possibility of losses. But, the higher the expected return then the risk also greater. The method to minimize risk is portfolio. One of the optimum portfolio method is Multi Index Model. Multi Index Model is model that use more than one index or factor that affects the return on stock. The stock in this research is 10 stocks of IDX30 period January 2014 – December 2018. Index in this research is IHSG, Hang Seng Index and DJIA. Multi Index Model has assumptions: residual variance of stock i equals , variance of index j equals , E(ci) = 0, covariance between index equals 0, covariance between the residual for stock and index equals 0 and covariance between the residual for stock equals 0. The result of this research is there are 4 stocks that fulfill the assumpions to be made as the optimum portfolio, that is GGRM (Gudang Garam Tbk) 23.67%, UNVR (Unilever Indonesia Tbk) 37.09%, BBCA (Bank Central Asia Tbk) 25.15% dan ASII (Astra International Tbk) 14.09% with a value of expected return portfolio is 1.19% and risk of portfolio is 3.79%. Keywords: Investment, Optimum Portfolio, Multi Index Model

PENERAPAN METODEEXPECTED SHORTFALLPADA PENGUKURAN RISIKO INVESTASI SAHAM DENGAN VOLATILITAS MODEL GARCH Nurul Fitria Fitria Rizani; Mustafid Mustafid; Suparti Suparti
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

One of the methods that can be used to measure stock investment risk is Expected Shortfall (ES). ES is an expectation of risk size which value is greater than Value at Risk (VaR), ES has characteristics of sub-additive and convex. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to model stock data that has high volatility. Calculating ES is done with data that shows deviations from normality using Cornish-Fisher's expansion. This researchapplies the ES at the closing stock price of PT Astra International Tbk. (ASII), PT Bank Negara Indonesia (Persero) Tbk. (BBNI), and PT Indocement Tunggal Prakarsa Tbk. (INTP) for the period of 11 February 2013 - 31 March 2019. Based on the volatility of GARCH (1,1) analysis, we find ES calculation for each stock by 95% level confidence. The ES for ASII shares is 4.1%, greater than the VaR value which isonly 2.64%.The ES for BBNI shares is 4.38%, greater than it’s VaR value which is only 2,86%. The ES for INTP shares is 6.22%, which is also greater than it’s VaR value which is only3,99%. The greather of VaR then Thegreather of ES obtained.Keywords: Expected Shortfall, Value at Risk, GARCH

MODEL FEED FORWARD NEURAL NETWORK (FFNN) DENGAN ALGORITMA PARTICLE SWARM SEBAGAI OPTIMASI BOBOT (Studi Kasus : Harga Daging Sapi dari Bank Dunia Periode Januari 2007 – Desember 2018) Faisal Fikri Utama; Budi Warsito; Sugito Sugito
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Beef is one of the important food commodities to fulfill the nutritional adequacy of humans. The World Bank notes the beef prices that are exported worldwide every month. For this reason, those data becomes a predictable series for the next period. Feed Forward Neural Network is a non-parametric method that can be used to make predictions from time series data without having to be bound by classical assumptions. The initiated weight will be evaluated by an algorithm that can minimize errors. Particle Swarm Optimization (PSO) is an optimization algorithm based on particle speed to reach the destination. The FFNN model will be combined with PSO to get predictive results that are close to the target. The best architecture on FFNN is obtained with 2 units of input, 1 unit of bias, 3 hidden units, and 1 unit of output by producing MAPE training 11.7735% and MAPE testing 8.14%. According to Lewis (1982) in Moreno et. al (2013), the MAPE value below 10% is highly accurate forecasting. Keywords: Feed Forward Neural Network (FFNN), Particle Swarm Optimization (PSO), neurons, weights, predictions.

PERBANDINGAN DIAGRAM KONTROL MEWMA DAN DIAGRAM KONTROL T2 HOTELLING UNTUK PENGENDALIAN KUALITAS PRODUK KAIN POLYESTER (Studi Kasus : PT Daya Manunggal Kota Salatiga) Abdiyasti Nurul Arifa; Rukun Santoso; Tatik Widiharih
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Fabrics is one of the most important needs of human life, so demand for clothing is greatly increased. Polyester fabric is a superior product at PT Daya Manunggal Salatiga because it has good quality. The quality of the fabric is very important because it is very influential in the competition to attract consumer interest. To maintain the consistency of the quality of the products produced in accordance with specifications, it is necessary to control the quality of the production process. The quality characteristics used in the production process of polyester fabric are thick layers, thin layers, two weft threads partially and two weft threads one more interconnected with one another, so multivariate control diagrams are used. Multivariate Exponentially Weighted Moving Average (MEWMA) and T2 Hotelling are control diagrams for monitoring mean process. The results showed that the MEWMA control diagram with lambda 0.7 yielded controlled results with a BKA value of 14.56021. Whereas in the Hotelling T2 control diagram a data reduction of four revisions was made to achieve controlled results with a final BKA value of 10.10928. The controlled production process obtained multivariate process capability values of 0.9672105 <1 which means the process is not capable. Comparison of results from the two methods shows that the MEWMA control diagram is more sensitive than the T2 Hotelling control diagram.Keywords: Fabric, Multivariate Exponentially Weighted Moving Average (MEWMA), Hotelling T2, Process Capability Analysis

PERAMALAN PRODUK DOMESTIK BRUTO (PDB) SEKTOR PERTANIAN, KEHUTANAN, DAN ‎PERIKANAN MENGGUNAKAN SINGULAR SPECTRUM ANALYSIS (SSA) Desy Tresnowati Hardi; Diah Safitri; Agus Rusgiyono
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Forecasting is the process of estimating conditions in the future by testing conditions from the past. One of the forecasting methods is Singular Spectrum Analysis (SSA) which aim of SSA is to make a decomposition of the original series into the sum of a small number of independent and interpretable components such as a slowly varying trend, oscillatory components and a structureless noise. Gross Domestic Product data in the agriculture, forestry, and fisheries sector are time series data with trend and seasonal pattern so that it can be processed using the SSA method. The forecasting process of SSA method uses the main parameter (L) of 21 obtained by the Blind Source Separation (BSS) method. From forecasting, acquired group of 3 groups. Forecasting resulted the value of Mean Absolute Percentage Error (MAPE) is 1.59% and the value of tracking signal is 2.50, which indicates that the results of forecasting is accurate. Keywords: Forecasting, Gross Domestic Product in the agriculture, forestry, and fisheries sector, Singular Spectrum Analysis (SSA)

PEMBENTUKAN PORTOFOLIO OPTIMAL DENGAN METODE RESAMPLED EFFICIENT FRONTIER UNTUK PERHITUNGAN VALUE AT RISK DILENGKAPI APLIKASI GUI MATLAB Henny Setyowati; Abdul Hoyyi; Di Asih I Maruddani
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The purpose of investors in investing is to get a return, but investors also have to bear the risks that might exist. There are 3 types of investors in investment based on their preference for risk, namely risk aversion (risk averter), moderate risk takers (risk moderate), and high risk takers (risk takers). To obtain an optimal portfolio for each type of investor, the Resampled Efficient Frontier Method is used with Monte Carlo Simulation as much as 700 times, to obtain more parameter estimates. The results of the Resampled Efficient Frontier from Efficient Frontier will take 51 efficient points to determine the optimal portfolio for each type of investor. The efficient point taken is the 1st, 26th and 51st efficient points for the investor risk averter type, risk moderate, and risk taker. To determine the estimated loss in investment, the VaR value is calculated based on the monthly return data of BBNI, UNTR, INKP, and KLBF shares for the period February 2013 to March 2017, with a capital allocation of Rp 100,000,000.00, a holding period of 20 days, and a level of trust of 95%. The Matlab GUI is used to facilitate users in processing data.Keywords: Efficient Frontier, Monte-Carlo Simulation, Normal Distribution, VaR, Matlab GUI

PEMODELAN REGRESI RIDGE ROBUST-MM DALAM PENANGANAN MULTIKOLINIERITAS DAN PENCILAN (Studi Kasus : Faktor-Faktor yang Mempengaruhi AKB di Jawa Tengah Tahun 2017) Eka Destiyani; Rita Rahmawati; Suparti Suparti
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The Ordinary Least Squares (OLS) is one of the most commonly used method to estimate linear regression parameters. If multicollinearity is exist within predictor variables especially coupled with the outliers, then regression analysis with OLS is no longer used. One method that can be used to solve a multicollinearity and outliers problems is Ridge Robust-MM Regression. Ridge Robust-MM Regression is a modification of the Ridge Regression method based on the MM-estimator of Robust Regression. The case study in this research is AKB in Central Java 2017 influenced by population dencity, the precentage of households behaving in a clean and healthy life, the number of low-weighted baby born, the number of babies who are given exclusive breastfeeding, the number of babies that receiving a neonatal visit once, and the number of babies who get health services. The result of estimation using OLS show that there is violation of multicollinearity and also the presence of outliers. Applied ridge robust-MM regression to case study proves ridge robust regression can improve parameter estimation. Based on t test at 5% significance level most of predictor variables have significant effect to variable AKB. The influence value of predictor variables to AKB is 47.68% and MSE value is 0.01538.Keywords: Ordinary Least Squares (OLS), Multicollinearity, Outliers, RidgeRegression, Robust Regression, AKB.

PEMODELAN REGRESI ROBUST S-ESTIMATOR UNTUK PENANGANAN PENCILAN MENGGUNAKAN GUI MATLAB (Studi Kasus : Faktor-Faktor yang Mempengaruhi Produksi Ikan Tangkap di Jawa Tengah) Dhea Kurnia Mubyarjati; Abdul Hoyyi; Hasbi Yasin
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Multiple Linear Regression can be solved by using the Ordinary Least Squares (OLS). Some classic assumptions must be fulfilled namely normality, homoskedasticity, non-multicollinearity, and non-autocorrelation. However, violations of assumptions can occur due to outliers so the estimator obtained is biased and inefficient. In statistics, robust regression is one of method can be used to deal with outliers. Robust regression has several estimators, one of them is Scale estimator (S-estimator) used in this research. Case for this reasearch is fish production per district / city in Central Java in 2015-2016 which is influenced by the number of fishermen, number of vessels, number of trips, number of fishing units, and number of households / fishing companies. Approximate estimation with the Ordinary Least Squares occur in violation of the assumptions of normality, autocorrelation and homoskedasticity this occurs because there are outliers. Based on the t- test at 5% significance level can be concluded that several predictor variables there are the number of fishermen, the number of ships, the number of trips and the number of fishing units have a significant effect on the variables of fish production. The influence value of predictor variables to fish production is 88,006% and MSE value is 7109,519. GUI Matlab is program for robust regression for S-estimator to make it easier for users to do calculations. Keywords: Ordinary Least Squares (OLS), Outliers, Robust Regression, Fish Production, GUI Matlab.

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