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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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ANALISIS PENGARUH KURS RUPIAH TERHADAP INDEKS HARGA SAHAM GABUNGAN MENGGUNAKAN DISTRIBUTED LAG MODEL Wilis Ardiana Pradana; Rita Rahmawati; Sugito Sugito
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Analysis of distributed lag Statistics is a branch of science that discuss the case of time series data. The method can be used in a distributed lag analysis there are two Koyck Method and Almon Method. At Koyck Method of regression coefficients are assumed to have the same sign and decreases geometrically. In this method there is the dependent variable at a time ago as the independent variable so that the equation is autoregressive. By using this method to analyze the effect of the exchange rate against IHSG. Data used were 35 data. The results showed that the regression coefficient does not decrease geometrically, so that Koyck Method can not be used to resolve this case. To resolve this case, the used Almon Method. In the Almon Method assumed regression coefficient can be approximated by a polynomial has degree. Before applying this method to be specified maximum length of lag and the degree polynomial. To determine performed several experiments using lag length and degree polynomial different. Through these experiments the best results are obtained with a lag of three and a maximum length of second degree polynomials. The results indicate that the effect of the exchange rate against IHSG inversely. Keywords: Distributed Lag, Koyck Method, Almon Method, Autoregressive, Lag, Polynomial

PERBANDINGAN ANALISIS DISKRIMINAN LINIER KLASIK DAN ANALISIS DISKRIMINAN LINIER ROBUST UNTUK PENGKLASIFIKASIAN KESEJAHTERAAN MASYARAKAT KABUPATEN/KOTA DI JAWA TENGAH Kartikawati, Ana; Mukid, Moch. Abdul; Ispriyanti, Dwi
Jurnal Gaussian Vol 2, No 3 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Discriminant analysis is a statistics method which is used to classify an individual or object into certain group which has determined based on its independent variables. Discriminant analysis that commonly used is classical discriminant analysis which consist of classical linear discriminant analysis and classical quadratic discriminant analysis. In classical linear discriminant analysis there are two assumptions to be fulfilled i.e. independent variables have to be normal multivariate distributed and the covariance matrix from the two observed objects should be the same. Classical discriminant analysis cannot work properly if the data which being analyzed consists of many outliers. In order to make discriminant analysis works optimally within the classification though in the condition of data which contains of many outliers, robust estimator is needed. The robust discriminant analysis is used to get the high classification accuracy for data which contains of many outliers. Fast-MCD estimator is one of the robust estimators which is aimed to get the smallest determinant of covariance matrices. The robust linear discriminant analysis with fast-MCD method in this graduating paper is implemented to determine the prosperity status of the people in the regencies or towns in Central Java. The total proportion of classification accuracy using robust linear discriminant analysis method on the data of Central Java people prosperity is 77.14 percent. It is equal with the result from classic linear discriminant analysis which is also 77.14 percent. It is caused by the few amount of outlier on the data of Central Java people prosperity.

PEMODELAN JARINGAN SYARAF TIRUAN DENGAN CASCADE FORWARD BACKPROPAGATION PADA KURS RUPIAH TERHADAP DOLAR AMERIKA SERIKAT Ekky Rosita Singgih Wigati; Budi Warsito; Rita Rahmawati
Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Neural Network Modeling (NN) is an information-processing system that has characteristics in common with human brain. Cascade Forward Neural Network (CFNN) is an artificial neural network that its architecture similar to Feed Forward Neural Network (FFNN), but there is also a direct connection from input layer and output layer. In this study, we apply CFNN in time series field. The data used isexchange rate of rupiah against US dollar period of January 1st, 2015 until December 31st, 2017. The best model was built from 1 unit input layer with input Zt-1, 4 neurons in the hidden layer, and 1 unit output layer. The activation function used are the binary sigmoid in the hidden layer and linear in the output layer. The model produces MAPE of training data equal to 0.2995% and MAPE of testing data equal to 0.1504%. After obtaining the best model, the data is foreseen for January 2018 and produce MAPE equal to0.9801%. Keywords: artificial neural network, cascade forward, exchange rate, MAPE

PERBANDINGAN METODE KLASIFIKASI REGRESI LOGISTIK BINER DAN NAIVE BAYES PADA STATUS PENGGUNA KB DI KOTA TEGAL TAHUN 2014 Rajagukguk, Nanci; Ispriyanti, Dwi; Wilandari, Yuciana
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Indonesia is a country that includes having the highest population density in the world.It is because the Indonesian state has a birth rate is so high. One of the efforts to control that population growth can be controlled by using the Keluraga Berencana program. In this study, the method used is the Binary Logistic Regression and Naive Bayes. To perform classification KB User Status in Tegal 2014, the variable used is the wife’s age, the age of first marriage, type of wife’s job, type of husband’s job, wife's education, husband's education, and number of children. The training data comparison testing is 70:30. Based on the research results using binary logistic regression showed that a significant predictor variables that affect the status of keluarga Berencana user are wife’s age, type of wife’s job, and number of children with a classification accuracy of testing data 83.33% .While with the Naive Bayes method obtained classification accuracy of 81.75%. From this analysis it can be concluded that the Binary Logistic Regression method is better than the Naive Bayes in classifying the status of KB users in Tegal 2014. Keywords : Binary Logistic Regression, Naive Bayes, Keluarga Berencana, Classification.

ANALISIS KECENDERUNGAN PEMILIHAN KOSMETIK WANITA DI KALANGAN MAHASISWI JURUSAN STATISTIKA UNIVERSITAS DIPONEGORO MENGGUNAKAN BIPLOT KOMPONEN UTAMA Rizka Asri Brilliani; Diah Safitri; Sudarno Sudarno
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

This study aims to reviews trend of using the cosmetics brand among the students of Department of Statistics at Diponegoro University. The observed cosmetics brand are Wardah, Sariayu Martha Tilaar, Pixy, Pond's, and Garnier. The data used in the form of primary data with total samples drawn 180 students, then it's been analyzed using principal component biplot. The result showed that Wardah has advantages in safety of product composition, and its benefit as a skin care. Wardah also more attractive to students. Sariayu Martha Tilaar, Pixy, and Pond's have the same profit, they are safety of product composition, the variations according the skin type, and their use as a skin care and make up. The diversity is 73,01% which means that principal component analysis biplot is able to explained 73,01% of the total diversity of the actual data. Keywords: principal component biplot analysis, cosmetics brand, perceptions

PENGGUNAAN PENDEKATAN CAPITAL ASSET PRICING MODEL DAN METODE VARIANCE-COVARIANCE DALAM PROSES MANAJEMEN PORTOFOLIO SAHAM (Studi Kasus: Saham-Saham Kelompok Jakarta Islamic Index) Ikhsan, Aulia; Ispriyanti, Dwi; Rahmawati, Rita
Jurnal Gaussian Vol 3, No 1 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The great amount of risk arising from stock investment make investors create a portfolio in order to minimize it. To achieve this aim, a portfolio management in which consist of several processes is required. There are three important processes in portfolio management. First, the selection of stocks that will be selected into the portfolio by Capital Asset Pricing Model (CAPM). Second, portfolio optimization by defining the weight of fund allocation for every stock in portfolio by Mean Variance Efficient Portofolio (MVEP), and third, estimating the risk of the optimal portfolio by Variance-Covariance. There are seven stocks picked into portfolio through the research done by Jakarta Islamic Index (JII) group, where the biggest fund allocation given to stock of EXCL (PT XL Axiata, Tbk) and the smallest fund allocation given to stock of ITMG (PT Indo Tambangraya Megah, Tbk). The amount of loss that estimated on 95% confidence level is 2,65% from initial capital invested on stock portfolio during one day holding period after portfolio were created.

PEMODELAN INDEKS PEMBANGUNAN MANUSIA DI JAWA TENGAH DENGAN REGRESI KOMPONEN UTAMA ROBUST Tsania Faizia; Alan Prahutama; Hasbi Yasin
Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Robust principal component regression is development of principal component regression that applies robust method at principal component analysis and principal component regression analysis. Robust principal component regression does not only overcome multicollinearity problems, but also overcomes outlier problems. The robust methods used in this research are Minimum Covariance Determinant (MCD) that is applied when doing principal component analysis and Least Trimmed Squares (LTS) that is applied when doing principal component regression analysis. The case study in this research is Human Development Index (HDI) in Central Java in 2017 which is influenced by labor force participation rates, school enrollment rates, percentage of poor population, population aged 15 years and over who are employed, health facilities, gross enrollment rates, and net enrollment rates. The model of HDI in Central Java in 2017 using robust principal component regression MCD-LTS provides the most effective result for handling multicollinearity and outliers with Adjusted R2 value of 0.6913 and RSE value of 0.469. Keywords: Robust Principal Component Regression, Multicollinearity, Outliers, Minimum Covariance Determinant (MCD), Least Trimmed Squares (LTS), Human Development Index (HDI).

PERHITUNGAN BIAYA TAMBAHAN DENGAN METODE ACCRUED BENEFIT COST PADA PENDANAAN PROGRAM PENSIUN MANFAAT PASTI Siti Nurlatifah; Sudarno Sudarno; Abdul Hoyyi
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Supplemental costs in funding pension plan is a cost to be issued by the employer to the pension fund in case shortage of funds (deficit) in the funding of defined benefit plans. There are several methods can be used, one of them is accrued benefit cost method. This research explained about the calculation of the supplemental costs on defined benefit plans with a case study on BMKG Semarang. The data used 34 BMKG employee salaries who had not attained 50 years old in 2015. The calculation is done by concern the beginning of an employee salary, interest rate, period of employment, and increase of salary rate. Supplemental costs that must be issued by BMKG in 2015 is Rp. 81.748.084. That cost can sufficient the pension benefits that will be received by the employee if the funding was deficit. If the funding pension had a surplus, that cost can be used as an investment company. Keywords: supplemental cost, defined benefit plans, accrued benefit cost.

PEMODELAN NEURO-GARCH PADA RETURN NILAI TUKAR RUPIAH TERHADAP DOLLAR AMERIKA Umi Sulistyorini Adi; Budi Warsito; Suparti Suparti
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Exchange rate can be defined as the value of a currency against other currencies. Exchange rates always fluctuate all the time. Very high fluctuations and unconstant becoming problem in forecasting where the data changed extremely. Most of economic data have heteroskedasticity characteristic analyzed using (Generalized Autoregressive Conditional Heteroskedasticity) GARCH models. Another model that commonly used as an alternative is Artificial Neural Network (ANN). However, both models have weaknesses. ARIMA models are linear, but the residual probably still contains non-linear relationship, while the ANN model used to non-linear relationship there is difficulty in determining the input. In this research combination of the two models is Neuro-GARCH model, with GARCH model used as input of ANN model. The purpose of this study was determined the best variance model Neuro-GARCH of return exchange rates rupiah against US dollar. The data used is daily return value of the rupiah (IDR) against the US dollar (USD) from August 27th, 2012 to March 31st, 2016. In this research, the mean model obtained is MA (1) and varian model is GARCH (1,1). The best model is Neuro-GARCH (2-10-1) which MSE smaller than the GARCH (1,1). Keywords: exchange rate, return, GARCH, Neuro-GARCH.

PEMODELAN MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS) PADA FAKTOR-FAKTOR RESIKO ANGKA KESAKITAN DIARE (Studi Kasus : Angka Kesakitan Diare Di Jawa Tengah, Jawa Timur Dan Daerah Istimewa Yogyakarta Tahun 2011) Wasis Wicaksono; Yuciana Wilandari; Suparti Suparti
Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Diarrhea morbidity is a number of diarrhea suffers in specific region in period of one year per 1000 populations. Diarrhea morbidity is the impact from some factors such as environment, education, socioeconomic, nutrition and foods. Environmental factors that can affect the morbidity of diarrhea include the percentage of families who have a healthy latrine and percentage of households using clean water. For educational factors include the average length of school and literacy rates. On socio-economic factors include the percentage of poor and average people per household. While the food and nutritional factors are the percentage TUPM (Public Places and Food Management) healthy.Diarrhea morbidity can be pressed by analyzing those factors so that the prevention can be devised. Regression curve is used to draw the relationship of response variable and predictor variable and mostly approached by parametric regression, where the curve design is known (such as linear, quadratic and cubic). If curve design is unknown, then regression curve can be derived by approaching using non parametric regression. Multivariate Adaptive Regression Spline (MARS) is one of nonparametric regression method that can be used on high dimension data. the best MARS model is derived by combination of Minimal Observation (MO), Maximum Basic Function (BF), and Maximal Interaction (MI) through trial and error. MARS model to predict diarrhea morbidity in Central Java, East Java and Yogyakarta is MARS (MO=2;BF=28;MI=3) and equation is = -0.526742 + 0.264444 * BF2 + 12.2382 * BF5 - 7.76719 * BF15 + 4.96445 * BF17

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Jawa tengah

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