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Jurnal Gaussian

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Published by Universitas Diponegoro

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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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Articles 733 Documents

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PERAMALAN MENGGUNAKAN METODE WEIGHTED FUZZY INTEGRATED TIME SERIES (Studi Kasus: Harga Beras di Indonesia Bulan Januari 2011 s/d Desember 2017) Setya Adi Rahmawan; Diah Safitri; Tatik Widiharih
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Fuzzy Time Series (FTS) is a time series data forecasting technique that uses fuzzy theory concepts. Forecasting systems using FTS are useful for capturing patterns of past data and then to using it to produce information in the future. Initially in the FTS each pattern of relations formed was considered to have the same weight besides using only the first order. In its development the Weighted Fuzzy Integrated Time Series (WFITS) which gave a difference in the weight of each relation and high order usage has been appeared. Measuring the accuracy of forecasting results is used the value of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). In this study both the first-order and high-order WFITS methods were applied to forecast rice prices in Indonesia based on data from January 2011 to December 2017. In this regard, the results of the analysis obtained data forecasting using Lee's high-order model WFITS algorithm (1,2,3) giving the value of RMSE and MAPE on the data testing in a row as many as 69,898 and 0.47% while for the RMSE and MAPE on the training data is as many as 70.4039 and 0.54%. Keywords: Fuzzy Time Series, Weighted Fuzzy Integrated Time Series, RMSE, MAPE, High-Order, Rice Prices

PERBANDINGAN METODE RUNTUN WAKTU FUZZY-CHEN DAN FUZZY-MARKOV CHAIN UNTUK MERAMALKAN DATA INFLASI DI INDONESIA Lintang Afdianti Nurkhasanah; Suparti Suparti; Sudarno Sudarno
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Inflation data are financial time series data which often violate assumption if it is modeled with ARIMA Box-Jenkins classic method. Therefore, to forecast inflation data are used forecast method which has not requirement classic assumptions, like as fuzzy time series method. Fuzzy time series is a method of predicting data that use principles of fuzzy as basis. Many researches has been developed about this method, such as fuzzy time series developed by Chen (1996) and fuzzy time series-Markov chain developed by Tsaur (2012). In this case, both methods are used to predict inflation data in Indonesia. Result of predicting from both methods are compared with MSE value to in sample data. Method of fuzzy time series-Chen get MSE value 0,656, whereas method of fuzzy time series-Markov chain get MSE value 0,216. Because of this reason, method of fuzzy time series-Markov chain get smallest MSE value. So, this method as the best method. Furthermore, to evaluate the best of predicting model used MAPE value to out sample data. The MAPE value in method of fuzzy time series-Markov chain is 6,610%. As conclusion, model of fuzzy time series Markov chain have best performance.Keywords : fuzzy time series, Markov chain , MSE, MAPE.

PENGUKURAN PROBABILITAS KEBANGKRUTAN DAN VALUASI OBLIGASI KORPORASI DENGAN METODE CREDITRISK+ Yudia Yustine; Abdul Hoyyi; Di Asih I Maruddani
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In capital market investment particularly the bonds, an investor must consider the credit risk and valuation of bonds. Credit risk refers to the risk due to unexpected changes in the credit quality of a counterparty or issuer. Valuation is amount that investor will receive on future. CreditRisk+ is from Reduced-Form Model which is used to calculate the probability of default and valuation of bonds. This method assumes that default occurs without warning and is therefore unpredictable. Default arrival is described by a Poisson process. Default intensity can expected by rate of corporate. An empirical example use a data set of bond from PT Berlian Laju Tanker, Tbk between 2007 and 2012. Probability of default from Berlian Laju Tanker III Bond is 0,6321206 and its valuation is Rp 153.481.545.500,00.

ANALISIS KLASTER KECAMATAN DI KABUPATEN SEMARANG BERDASARKAN POTENSI DESA MENGGUNAKAN METODE WARD DAN SINGLE LINKAGE Annisa Nur Fathia; Rita Rahmawati; Tarno Tarno
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Physical and non-physical aspects are the ways to explain a diversity among regions, including a diversity among districts. Village potential providing data about the existence, availability and development potential of each administrative area. To know the district that has the same characteristics, do the grouping using cluster analysis. Cluster analysis is a grouping of objects or cases into groups smaller where each group contains objects that are similar to one another. Clustering process is done for 19 districts in Semarang Regency by ward’s method and single linkage. Four cluster are chosen for the process of potential developing more specific in each district. From the analysis using ward’s method, 1st cluster obtained with minimal educational facilities. 2nd cluster with minimal health facilities. 3rd cluster with the districts which caracteristics itself have a good condition. 4th cluster with minimal power line facilities. From the analysis using single linkage method, 1st cluster obtained with a good condition of power line facilities. 2nd cluster with a good condition of educational facilities. 3rd cluster with a minimal educational facilities. 4th cluster with minimal power line facilities. R-Squared value from single linkage method is higher than ward’s method, this shows the single linkage clustering method produces cluster features with each other more heterogeneous compared to the clustering method ward. Keywords: Cluster Analysis, Ward’s Method, Single Linkage, Distict, Village Potential.

PERBANDINGAN METODE KLASIFIKASI NAÏVE BAYES DAN K-NEAREST NEIGHBOR PADA ANALISIS DATA STATUS KERJA DI KABUPATEN DEMAK TAHUN 2012 Riyan Eko Putri; Suparti Suparti; Rita Rahmawati
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Large population in Indonesia is closely related to the working status of the population which is unemployed or employed. It can lead to the high unemployment when the avaliable jobs arent balance with the population. Used two methods to perform the classification of employment status on the number of residents in the labor force in Demak for 2012 which is Naïve Bayes and K-Nearest Neighbor. Naïve Bayes is a classification method based on a simple probability calculation, while the K-Nearest Neighbor is a classification method based on the calculation of proximity. Variables used in determining whether a person's employment status is idle or not are gender, status in the household, marital status, education, and age. Employment status of the data processing methods of Naïve Bayes with the accuracy obtained is equal to 94.09% and the K-Nearest Neighbor method obtained is equal to 96.06% accuracy. To evaluate the results of the classification used calculations Press's Q and APER. Based on the analysis, the Press's Q values obtained indicate that both methods are already well in the classification of employment status data in Demak. Based on the calculation of APER, the classification of data in the employment status of Demak using the K-Nearest Neighbor method has an error rate smaller than the Naïve Bayes method. From this analysis it can be concluded that the K-Nearest Neighbor method works better compared with the Naïve Bayes for employment status data in the case of Demak for 2012. Keywords : Classification, Naïve Bayes, K-Nearest Neighbor (K-NN), Classification evaluation

PERBANDINGAN METODE REGRESI LOGISTIK BINER DAN METODE BACKPROPAGATION DALAM MENENTUKAN MODEL TERBAIK UNTUK KLASIFIKASI PENGGUNA PROGRAM KELUARGA BERENCANA Muhammad Mujahid; Hasbi Yasin; Moch. Abdul Mukid
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Indonesia is one of the highest population density in the world has high birth level. One of the regulation to get the population density lower than before that is used by Government is Family Planning Program. On the reality, not all of the productive age join this program. The method is Binary Logistic Regression and Backpropagation. The predictor variables that is researched are husband’s age, wife’s age, age of the last child, count of children, husband’s education, wife’s education, husband’s job, wife’s job and the level of family prosperity. The aim of the research is to compare the classification accuracy between Binary Logistic Regression and Backpropagation. The result of the research by binary logistic regression method, shows the variables that affect the status of KB user is age of the last child and wife’s education with the classification accuracy are 66.98%, and the classification accuracy of Backpropagation are 67,30%. The conclution based on the research that is the Backpropagation is better than Binary Logistic Regression when classification the status of KB user in Semarang on March 2013 until Januari 2014. Keywords : Binary Logistic Regression, Backpropagation, Keluarga Berencana, Classification

ESTIMASI PARAMETER REGRESI LOGISTIK MULTINOMIAL DENGAN METODE BAYES Wayaning Apsari; Hasbi Yasin; Sugito Sugito
Jurnal Gaussian Vol 2, No 1 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Multinomial logistic regression is a logistic regression where the dependent variable is polychotomous is dependent variable value of more than two categories. Multinomial logistic regression parameter estimation usually use classical method that is based only on current information obtained from the sample without taking into account the initial information of logistic regression parameters. If have early information about parameter is prior distribution, the parameter estimation can use Bayes method. Bayesian methods combine information on the sample with prior distribution of information, and the results are expressed in the posterior distribution. If posterior distribution can not be derived analytically so approximated using Markov Chain Monte Carlo (MCMC) algorithm especially Metropolis-Hastings algorithm. This algorithm uses acceptance and rejection mechanism to generate a sequence of random samples. Keyword: Multinomial Logistic Regression, Bayes Method, Markov Chain Monte Carlo algorithm (MCMC), Metropolis-Hastings algorithm.

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

RANCANGAN ACAK KELOMPOK TAK LENGKAP SEIMBANG PARSIAL (RAKTLSP) Gustriza Erda; Tatik Widiharih; Yuciana Wilandari
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Partially Balanced Incomplete Block Designs (PBIBD) is a design with v treatments arranged into b blocks with every block which is consist of into k treatment (k < v) that in every treatment only occurs once in every block, and there are pair treatment which occur together in the same block as much as λm times. The pair treatments on PBIBD is based on the association scheme. This undegraduate thesis uses triangular association scheme that is two-class association scheme (first and second association). This scheme is used to determine the first and second association of every treatment. Based on formed association, it will obtain the number of pairs treatment that occurs in every block that will be designed (λm, m=1,2). The test that is used is test of treatments effect because only treatments that is important which are adjusted treatment for the reason that not all treatments occurs in every block. Assumptions which is required is the assumption of residual normality, equal variances, and independence assumption. The advanced test to be held is Tuckey Test (Honest Significance Difference). To clarify the discussion on PBID, examples of applications in the field of animal husbandry are given to observe the effect of the type of foods that contain alfalfa effect toward weight gain of turkey. The result obtained indicate that there are significant types of foods that contain alfalfa effect toward weight gain of turkey. Where is the recommended type of food is the food of A that contain 2,5% alfafa type 22.Keywords : PBIBD, Triangular association, Tuckey Test, Normality, Equal Variances, Independence

PEMODELAN RETURN INDEKS HARGA SAHAM GABUNGAN MENGGUNAKAN THRESHOLD GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (TGARCH) Maidiah Dwi Naruri Saida; Sudarno Sudarno; Abdul Hoyyi
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

ARIMA model is one of modeling method that can be applied on time series data. It assumes that the variance of residual is constant. Time series data, particularly the return of composite stock price index, tend to change rapidly from time to time and also fluctuating, which cause heteroscedasticity where the variance of residual is not constant. Autoregressive Conditional Heteroscedasticity (ARCH) or Generalized Autoregressive Conditional Heteroscedasticity (GARCH) can be used to construct model of financial data with heteroscedasticity. Besides of having inconsistent variance, financial data usually shows phenomenon where the difference of the effect between positive error value and negative error value towards data volatility, called asymmetric effect. Therefore, one of the GARCH asymmetric models, Threshold Generalized Autoregressive Conditional Heteroscedasticity (TGARCH) is used in this research to solve heteroscedasticity and asymmetric effect in stock price index return. The data in this research is stock price index return from January 2nd, 2013 until October 30th, 2015. From the analysis, TGARCH models are obtained. ARIMA([3],0,[26])-TGARCH(1,1) is the best model because it has the smallest AIC value compared to other models. It produces the forecast value of stock price index return nearly the same with actual return value on the same day. Keywords: Return, Heteroscedasticity, Asimmetry effect, ARCH/GARCH, TGARCH.

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