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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,
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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.
Arjuna Subject : -
Articles 733 Documents
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PENERAPAN METODE WAVELET NEURO-FUZZY SYSTEM (WNFS) DALAM MEMPREDIKSI HARGA BERAS DUNIA (Studi Kasus: Harga Beras Thailand sebagai Harga Acuan Dunia) Sri Endah Moelya Artha; Hasbi Yasin; Budi Warsito
Jurnal Gaussian Vol 6, No 4 (2017): 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.v6i4.30381

Abstract

Rice trade is one of the food resistance components in terms of its availability. The comprehensive integration between international commodity rice prices and domestic prices encourage the prediction of world rice prices, using the Thai rice price as the world's reference price. In this study, the wavelet neuro-fuzzy system which combines the wavelet transform and the neuro-fuzzy technique has been applied to monthly predict the world rice price. The observed monthly rice price data are decomposed into some sub-series components by maximal overlap discrete wavelet transform (MODWT), and then the appropriate sub-series that have higher correlation to the real data are used as inputs of the neuro-fuzzy model for monthly predicting world rice prices for six months in advance. The neuro-fuzzy model is begun with determining the membership value of each data using Fuzzy C-Means, followed by fuzzy inference procedure of the Sugeno zero-order model. Obtained results showed that the WNFS method can be used to predict the world rice price, with the error value resulted from learning process of MSE 20,69097 and MAPE 0,65584%. While the error measurement results for the six months in advance prediction shows the acquisition of MSE 3610,14847 and MAPE 13,62334%. Keywords : Prediction of Monthly World Rice Price, Maximal Overlap Discrete Wavelet Transform, Neuro-fuzzy System.
PENGUKURAN KINERJA PORTOFOLIO OPTIMAL CAPITAL ASSET PRICING MODEL (CAPM) DAN ARBITRAGE PRICING THEORY (APT) (Studi Kasus : Saham-saham LQ45) Dedi Baleo Pasaribu; Di Asih I Maruddani; Sugito Sugito
Jurnal Gaussian Vol 7, No 4 (2018): 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.v7i4.28870

Abstract

Investing is placing money or funds in the hope of obtaining additional or specific gains on the money or funds. The capital market is one place to invest in the financial field of interest to investor. This is because the capital market gives investor the freedom to choose securities traded in the capital market in accordance with the wishes of investor. Investor are included in risk averter, that means investor will always try to avoid risk. To avoid risk, investor try to diversify their investment. Diversification concept commonly used is portfolio. To maximize the return to be earned, the investor will invest his funds into several stocks in order to earn a greater profit. Capital Asset Pricing Model (CAPM) is a balance model that describes the relation of a risk with return more simply because it uses only one variable to describe the risk. Arbitrage Pricing Theory (APT) is a balance model that used many risk variables to see the relation of risk and return. With both models will be obtained a portfolio with each constituent stock is four stocks selected from 45 stocks in the LQ45 index. To find out which portfolio is the best performed a performance analysis using the Sharpe index. From the measurement result, it is found that the best portfolio is the CAPM portfolio with composite stock is PTBA with investment weight of 0.467%, BUMI with investment weight of 12.855%, ANTM with investment weight of 53.077% and PPRO with investment weight of 33.601%. Keywords: LQ45, portfolio, Capital Asset Pricing Model (CAPM), Arbitrage Pricing Theory                       (APT), Sharpe Index 
ANALISIS SENTIMEN GOJEK PADA MEDIA SOSIAL TWITTER DENGAN KLASIFIKASI SUPPORT VECTOR MACHINE (SVM) Nur Fitriyah; Budi Warsito; Di Asih I Maruddani
Jurnal Gaussian Vol 9, No 3 (2020): 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.v9i3.28932

Abstract

Appearance of PT Aplikasi Karya Anak Bangsa or as known as Gojek since 2015 give a convenience facility to people in Indonesia especially in daily activities. Sentiment analysis on Twitter social media can be the option to see how Gojek users respond to the services that have been provided. The response was classified into positive sentiment and negative sentiment using Support Vector Machine method with model evaluation 10-fold cross validation. The kernel used is the linear kernel and the RBF kernel. Data labeling can be done with manually and sentiment scoring. The test results showed that the RBF kernel gets overall accuracy and the highest kappa accuracy on manual data labeling and sentiment scoring. On manual data labeling, the overall accuracy is 79.19% and kappa accuracy is 16.52%. While the labeling of data with sentiment scoring obtained overall accuracy of 79.19% and kappa accuracy of 21%. The greater overall accuracy value and kappa accuracy obtained, the better performance of the classification model. Keywords: Gojek, Twitter, Support Vector Machine, overall accuracy, kappa accuracy
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PENERIMA BERAS RASKIN MENGGUNAKAN REGRESI LOGISTIK BINER DENGAN GUI R Agustinus Salomo Parsaulian; Tarno Tarno; Dwi Ispriyanti
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.v10i1.30934

Abstract

The Rice Subsidy Program for Low-Income Communities or the Raskin Program is one of the government's programs to eradicate poverty. However, in practice, determining the criteria for Raskin recipients is a complicated problem. The Raskin program is a cross-sectoral national program both horizontally and vertically, to help meet the rice needs of low-income citizens. Determining the criteria for Raskin recipients is often a complicated issue. This study aims to analyze the classification of the Target Households (RTS) for the Raskin Program. The method used is binary logistic regression by utilizing R GUI. Binary logistic regression method is a method to find the relationship between independent and dependent variables, with a binary or dichotomous dependent variable. The data used is the March 2018 National Socio-Economic Survey (Susenas) data for Brebes Regency. The independent variables used in this study are the criteria for determining poor households, namely the area of the house, floor type of the house, wall type of the house, defecation facilities, lighting used, fuel used, ability to buy meat/milk, education level of the head of the household, and the capacity of installed electricity in the main residence. The results of the analysis show that in the final model, the variables that significantly affect the classification of RTS are the ability to eat healthy food, the capacity of installed electricity in the main residence, the education level of the head of the household, and defecation facilities with an accuracy value of 85.4%.Keywords: Raskin Program, Binary Logistic Regression, R GUI
PEMODELAN SEMIPARAMETRIC GEOGRAPHICALLY WEIGHTED REGRESSION PADA KASUS PNEUMONIA BALITA PROVINSI JAWA TENGAH Putri Fajar Utami; Agus Rusgiyono; Dwi Ispriyanti
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.v10i2.30945

Abstract

Geographical and inter-regional differences have contributed to the diversity of child pneumonia cases in Central Java, so  a spatial regression modelling is formed that is called Geographically Weighted Regression (GWR). GWR is a development of linear regression by involving diverse factors geographical location, so that local parameters are produced.  Sometimes, there are non-local GWR parameters. To overcome some non-local parameters, Semiparametric Geographically Weighted Regression (SGWR) is formed to develop a GWR model with local and global influences simultaneously. SGWR Model is used to estimate the model of percentage of children with pneumonia in Central Java with population density, average temperature, percentage of children with severe malnutrition, percentage of children with under the red line weight, percentage of households behave in clean and healthy lives, and percentage of children who measles immunized. SGWR models on percentage of children with pneumonia in Central Java produce locally significant variables that is population density, average temperature, and percentage of households behave in clean and healthy lives. Variable that globally significant is percentage of children with severe malnutrition. Based on Akaike Information Criterion (AIC), SGWR is a better model to analize percentage of children with pneumonia in Central Java because of smallest AIC. Keywords: Akaike Information Criterion, Geographically Weighted Regression, Semiparametric Geographically Weighted Regression
METODE MODIFIED JACKKNIFE RIDGE REGRESSION DALAM PENANGANAN MULTIKOLINIERITAS (STUDI KASUS INDEKS PEMBANGUNAN MANUSIA DI JAWA TENGAH) Arya Huda Arrasyid; Dwi Ispriyanti; Abdul Hoyyi
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.v10i1.29922

Abstract

The human development index is a value where the value showed the measure of living standards comparison in a region. The Human Development Index is influenced by several factors, one of them is the education factor that is the average years of schooling and expected years of schooling. A statistical method to find the correlation between the independent variable and the dependent variable can be conducted using the linear regression method. Linear regression requires several assumptions, one of which is the multicollinearity assumption. If the multicollinearity assumption is not fulfilled, another alternative is needed to estimate the regression parameters. One method that can be used to estimate regression parameters is the ridge regression method with an ordinary ridge regression estimator. Ordinary ridge regression then developed more into several methods, such as generalized ridge regression, jackknife ridge regression, and modified jackknife ridge regression method. The generalized Ridge Regression method causes a reduction to variance in linear regression, while the jackknife ridge regression method is obtained by resampling jackknife process on the generalized ridge regression method. Modified jackknife ridge regression is a combination of generalized ridge regression and jackknife ridge regression method. In this journal, the three ridge regression methods will be compared based on the Mean Squared Error obtained in each method. The results of this study indicate that the jackknife ridge regression method has the smallest MSE value. Keywords: Generalized Ridge Regression, Jackknife Ridge Regression, Modified Jackknife Ridge Regression, Multicolinearity  
PERAMALAN INDEKS HARGA SAHAM MENGGUNAKAN ENSEMBLE EMPIRICAL MODE DECOMPOSITION (EEMD) Rosinar Siregar; Rukun Santoso; Puspita Kartikasari
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.v10i2.29919

Abstract

 Stock price fluctuations make investors tend to hesitate to invest in stock markets because of an uncertain situation in the future. One method that can solve these problems is to use forecasting about the stock prices in the future. Generally, the huge size of data non linear and non stationary, and it is difficult to be interpreted in concrete. This problem can be solved by performing the decomposition process. One of decomposition method in time series data is Ensemble Empirical Mode Decomposition (EEMD). EEMD is process decomposition data into several Intrinsic Mode Function (IMF) and the IMF residue. In this research, this concept applied to data Stock Price Index in Property, Real Estate, and Construction from July 1, 2019 to July 30, 2020 as many as 272 data. Based on the results of data processing, as many as 6 IMF and IMF remaining were used as IMF forecasting and the IMF remaining in the future. The forecast was performed by choosing the best model of each IMF component and IMF remaining, used ARIMA and polynomial trend. Keywords: Time Series Data, Stock Price Index, EEMD, ARIMA, Polynomial Trend.
MODEL REGRESI COX PROPORTIONAL HAZARD PADA DATA KETAHANAN HIDUP PASIEN HEMODIALISA Aprilia Sekar Khinanti; Sudarno Sudarno; Triastuti Wuryandari
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.v10i2.30958

Abstract

Cox regression is a type of survival analysis that can be implemented with proportional hazard models or duration models. In the survival analysis data, there is a possibility that the data has ties, so it is necessary to use several approaches in estimating the parameters, namely the breslow, efron, and exact approaches. In this study, the Cox proportional hazard regression was used as a method of analysis for knowing the factors that influence the survival time on chronic kidney patients undergoing hemodialysis therapy. Based on the analysis that has been done, the best model is obtained with an exact approach and the factors that influence the survival time of hemodialysis patients are systolic blood pressure, hemoglobin level, and dialysis time. Hemodialysis patients who have high systolic blood pressure have a chance of failing to survive 12,950 times than normal systolic blood pressure.While the hemodialysis patient hemoglobin level increases, the hemodialysis patients chances of failing to survive is 0,6681 times less. Hemodialysis patients who received dialysis therapy with a dialysis time of more than four hours had 0.237 times the chance of failing to survive than patients with a dialysis time of less than or equal to 4 hours.Keywords: Cox Regression ,Survival, Ties, Hemodialysis.
PEMBENTUKAN DAN PENGUKURAN KINERJA PORTOFOLIO EFISIEN DENGAN METODE CONSTANT CORRELATION MODEL MENGGUNAKAN GUI MATLAB (Studi Kasus: Kelompok Saham pada Indeks JII, LQ45, dan INFOBANK15) Muhammad Zidan Eka Atmaja; Alan Prahutama; Dwi Ispriyanti
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.v10i2.28940

Abstract

Investment is an important part of financial management that is widely known by the public. One example of an investment is a stock, stock is favored by investors because many of companies issue stock investment. investors goal from investment are to get funds that have been invested. Besides advantage, investors also have to face risks that can befall on him. Risk in investment can be minimized by diversification, for example by forming a portfolio. A good portfolio is an efficient portfolio, which is a portfolio that has a high rate of return with minimal risk. One of the way to to form an efficient portfolio is the Constant Correlation Model (CCM) method. The CCM method focuses on Excess return to Standard Deviation (ERS) and correlation between paired stocks. And to measure the portfolio formed can be measured by the Sharpe Ratio. GUI MATLAB program was formed to make it easier to find portfolio from the CCM method. This research uses stock data on the stock index JII, LQ45, and INFOBANK15 with interest rate of SBI period 2 October 2017-30 December 2019. Based on the results and discussion with manual calculations and GUI MATLAB, it can be concluded that percentage of weight, expected return, risk, and Sharpe index produce the same numbers. Keywords: Stock, Efficient Portfolio, Constant Correlation Model, Sharpe Ratio
PEMODELAN ANGKA HARAPAN HIDUP PROVINSI JAWA TENGAH MENGGUNAKAN ROBUST SPATIAL DURBIN MODEL Maghfiroh Hadadiah Mukrom; Hasbi Yasin; Arief Rachman Hakim
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.v10i1.30935

Abstract

Spatial regression is a model used to determine relationship between response variables and predictor variables that gets spatial influence. If there are spatial influences on both variables, the model that will be formed is Spatial Durbin Model. One reason for the inaccuracy of the spatial regression model in predicting is the existence of outlier observations. Removing outliers in spatial analysis can change the composition of spatial effects on data. One way to overcome of outliers in the spatial regression model is by using robust spatial regression. The application of M-estimator is carried out in estimating the spatial regression parameter coefficients that are robust against outliers. The aim of this research is obtaining model of number of life expectancy in Central Java Province in 2017 that contain outliers. The results by applying M-estimator to estimating robust spatial durbin model regression parameters can accommodate the existence of outliers in the spatial regression model. This is indicated by the change in the estimating coefficient value of the robust spatial durbin model regression parameter which can increase adjusted R2 value becomes 93,69% and decrease MSE value becomes 0,12551.Keywords: Outliers, M-estimator, Spatial Durbin Model, Number of Life Expectancy.

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