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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 6, No 3 (2017): Jurnal Gaussian" : 18 Documents clear

PERBANDINGAN METODE K–MEANS DAN SELF ORGANIZING MAP (STUDI KASUS: PENGELOMPOKAN KABUPATEN/KOTA DI JAWA TENGAH BERDASARKAN INDIKATOR INDEKS PEMBANGUNAN MANUSIA 2015) Rachmah Dewi Kusumah; Budi Warsito; Moch. Abdul Mukid
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Cluster analysis is a process of separating the objects into groups, so that the objects that belong to the same group are similar to each other and different from the other objects in another group. In this study used two method to classify data of district / city in Central Java based on indicators of Human Development Index (HDI) 2015 are K-Means and Self Organizing Map (SOM) with the number of groups as much as two to seven. Furthermore, the results of both methods were compared using the Davies-Bouldin Index (DBI) values to determine which method is better. Based on the research that has been conducted found that the K-Means (K=4) method works better than SOM (K=2) to classify district / city in Central Java based on indicators of Human Development Index (HDI) as evidenced by the value of the Davies-Bouldin Index (DBI) on K-Means (K=4) of 0.786 is smaller than the value at SOM (K=2) Davies-Bouldin Index (DBI) which is equal to 0.893. Keywords: clustering, HDI, K-Means, SOM, DBI

PEMODELAN VECTOR AUTOREGRESSIVE X (VARX) UNTUK MERAMALKAN JUMLAH UANG BEREDAR DI INDONESIA Rosyidah, Haniatur; Rahmawati, Rita; Prahutama, Alan
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The economic stability of a country can be seen from the value of inflation. The money supply in a country will affect the value of inflation, so it is necessary to control the money supply. The money supply in Indonesia consists of currency, quasi money, and securities other than shares. One of the factors affecting the amount of currency, quasi money, and securities other than shares is the SBI interest rate. Time series data from the money supply components are correlated. To explain multiple time series data variables that are correlated we can use the VAR approach. VAR model with the addition of an exogenous variable is called VARX. The purpose of this study is to obtain models to predict the amount of currency, quasi money, securities other than shares using the VARX approach with the SBI interest rate as an exogenous variable. The results of data analysis in this study, the model obtained is VARX (1,1). Based on t test with 5% significance level, SBI interest rate variable has no significant effect to variable of currency amount, amount of quasi money, or amount of securities other than shares. Residual model VARX (1,1) satisfies the white noise assumption, while the normal multivariate assumption is not satisfied. The value of MAPE for currency variables (7,53969%), quasi money (0,49036%), and securities other than shares (9,64245%) indicates that the VARX (1,1) model has excellent forecasting ability that can be used for forecasting future periods. Forecasting results indicate an increase in the amount of currency, quasi money, or securities other than shares in each period..Keywords : Amount of currency, amount of quasi money, amount of securities other than shares, SBI interest rate, VARX, MAPE

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI DIVIDEND PAYOUT RATIO (DPR) MENGGUNAKAN ANALISIS REGRESI LINIER DENGAN BOOTSTRAP (Studi Kasus: PT. Unilever Indonesia, Tbk Tahun 1999-2015) Lia Safitri; Di Asih I Maruddani; Rukun Santoso
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The amount of dividend paid by the company to shareholders or dividend payout ratio is the main factor that investors pay attention to invest their capital into the company. Investors want a relative dividend, even increasing over time. Factors influencing the level of dividend payout ratio are Return on Equity (ROE), stock price, liquidity ratio, and leverage level. Based on this, multiple linear regression analysis with bootstrap is used. The purpose of this study is to analyze the factors that significantly affect the dividend payout ratio based on the best model used to predict the value of dividend payout ratio for the next period. The bootstrap method is used to overcome the occurrence of multicollinearity among independent variables due to the small sample size. Based on the simulation done with software R using PT data. Unilever Indonesia, Tbk from 1999-2015 obtained best model is bootstrap residual with 2 significant independent variable are ROE and level of leverage. Based on the best model, the predicted value of dividend payout ratio of 2016 is 41.60196 with percentage error of 7.0812%. Keywords : Regression analysis, Bootstrap, Dividend Payout Ratio, ROE, leverage

STRUCTURAL VECTOR AUTOREGRESSIVE UNTUK ANALISIS DAMPAK SHOCK NILAI TUKAR RUPIAH TERHADAP DOLAR AMERIKA SERIKAT PADA INDEKS HARGA SAHAM GABUNGAN Annisa Rahmawati; Di Asih I Maruddani; Abdul Hoyyi
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Instability and depreciation of the rupiah be a motivating factor for investors to pull out a portfolio in Indonesia. The weakening of rupiah led to a decline in investor demand for stocks. Measurement of stock price fluctuations or portfolio using the Composite Stock Price Index (CSPI). The exchange rate and CSPI is a sensitive macroeconomic variables affected by shock and it takes restriction of macroeconomic structural model. Based on this, Structural Vector Autoregressive (SVAR) model is used. The purpose of this thesis is to analyze the impact of the exchange rate shock on CSPI through the description of Structural Impulse Response Function and Structural Variance Decomposition modeling based on a restriction on SVAR. SVAR also called the theoretical VAR used to respond to criticism on the VAR model where necessary the introduction of restrictions on economic models. By using daily data exchange rate of the rupiah against the US dollar and CSPI from January 2013 to December 2016 acquired the VAR model is stable and meets the white noise assumption as the basis for modeling residual SVAR and has a short-term restriction. The response of CSPI from the impact of the shock rupiah exchange rate is likely to experience an increase, while the response to the shock CSPI itself is fluctuating but tends to decrease. Patterns proportion shock effect on the exchange rate is increasingly rising stock index in the period of time, whereas the effect of the shock CSPI itself getting down on each period of time. Keywords : exchange rate, CSPI, SVAR, Structural Impulse Response Function, Structural Variance Decomposition

KLASIFIKASI PASIEN DIABETES MELLITUS MENGGUNAKAN METODE SMOOTH SUPPORT VECTOR MACHINE (SSVM) Rizky Adhi Nugroho; Tarno Tarno; Alan Prahutama
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Diabetes Mellitus (DM) is a high-risk metabolic diseases. Laboratory tests are needed to determine if the patients suffer from a Diabetes Mellitus. Therefore, it needs a classification methods that can precisely classify data according to the classes criteria. SVM is one of commonly used methods of classification. The basic concept of SVM is to find the bes separator function (hyperplane) that separates the data according its class. SVM uses a kernel trick for nonlinear problems, which transforms data into high-dimensional space using kernel functions, so it can be classified linearly. This research will use a developed methods of SVM called SSVM, that adds smoothing function using Newton-Armijo method. The smoothing methods is needed to correct the effectiveness of SVM in big data classifying. The result is indicating tha SSVM classification prediction using Gaussian RBF kernel function, can classify 98 out of 110 patient data of Diabetes Mellitus correctly according the original class.Keywords : Diabetes Mellitus, Classification, Support Vector Machine (SVM), Smooth Support Vector Machine (SSVM), Kernel Gaussian RBF.

PEMILIHAN MEREK LIPSTIK TERFAVORIT DENGAN MADM BERBASIS GUI MATLAB Finisa, Husnul; Widiharih, Tatik; Mukid, Moch. Abdul
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Lipstick is a cosmetic usually worn by women to improve appearance with apply to the lips. The interest on lipstick among student at indonesia based on the various brands lipstick of national and international land of selling in indonesia. Based on this condition , it takes a method that can evaluate most favorite brand lipstick according to college student . The method applied to choose most favorite brand lipstick are Simple Additive Weighting (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Both this method can do the decision to establish an alternative best of a number of alternatives based on a number of certain criteria in overcoming Multi Attribute Decision Making (MADM), The concept of SAW is looking for a sum of the weighted performance rating for each alternative in all criteria. While TOPSIS using the principle that alternative chosen should have the shortest distance of a solution ideal positive and farthest of a solution ideal negative. There are 10 alternative brand lipstick and 10 criteria, the criterias are the price, color, form, packaging, resilience, pigmentation, texture, scent, the availability of code expired lipstick. The result of the research indicated that to the SAW method most favorite brand lipstick is of NYX and to the TOPSIS method most favorite brand lipstick is Wardah. The research also produce an application programming GUI Matlab that can help users in process data uses the method saw and topsis for an election most favorite brand lipstick.Keywords : GUI, Lipstick, MADM, SAW, TOPSIS

PENGGUNAAN REGRESI LOGISTIK BINER DAN ITERATIVE DICHOTOMISER 3 (ID3) DALAM PEMBUATAN KLASIFIKASI STATUS KERJA (Studi Kasus Penduduk Kota Surakarta Tahun 2015) Winastiti, Lugas Putranti; Rusgiyono, Agus; Safitri, Diah
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Discussing about the macro economy usually discuss about unemployment. Unemployment basically can not be fully eliminated. Unemployment usually symbolized with an employment status of person. In this research, two methods were used in making the classification of employment status in the population of the city of Surakarta in February 2015, the methods are binary logistic regression and Iterative Dichotomiser 3 (ID3) Algorithm. Predictor variables used in determining employment status were age, gender, status in the household, marital status, education and work training. Comparison of the training data and testing data is 60:40. Based on calculations obtained binary logistic regression variables that significantly affect the employment status are age, gender and marital status and the accuracy using testing data is 75%, while the calculations of a decision tree using iterative dichotomiser 3 algorithm the accuracy using testing data is 75%. Keywords: Classification, Iterative Dichotomiser 3 Algorithm, Binary Logistic Regression

PEMODELAN REGRESI HECKIT UNTUK KONSUMSI SUSU DI PROVINSI JAWA TENGAH Dwi Asti Rakhmawati; Dwi Ispriyansti; Agus Rusgiyono
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In multiple regression if the response variable is dummy variable then it can not be used because it will produce biased and inconsistent estimator. The appropriate method for binary response variables is Heckit Regression. Estimation of Heckit Regression parameter using Two Step Method of Procurement is the selection equation and the result equation. In the selection equation will get new variable that is Invers Mills Ratio. While in Equation Result of new variable of Inverse Mills Ratio is added as independent variable along with other independent variable. Heckit Regression method is applied to household milk consume data obtained from 2015 SUSENAS results as many as 201 households. The response variable used is household expenditure for milk consumption. The independent variables used are the working status of the head of the household, the last education of the head of the household, the number of household members, the number of toddler age in the family and the income of the household.Keywords: Multiple Regression, OLS, Heckit Regression, Two Step Procedure, Milk consumption expenditure.

PEMILIHAN INPUT MODEL REGRESSION ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (RANFIS) UNTUK KAJIAN DATA IHSG Sari, Sasmita Kartika; Tarno, Tarno; Safitri, Diah
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The Jakarta Composite Index (JCI) is one of indexes issued by the Indonesia Stock Exchange (IDX) with its calculation component using all the registered emiten. Several factors affecting the JCI are Dow Jones Index, inflation, and USD/IDR exchange rate. The study used Regression Adaptive Neuro Fuzzy Inference System (RANFIS) to analyze the affect of predictor variables on the JCI. The role of regression in RANFIS is a preprocessing in the determination of input in ANFIS. The optimum ANFIS model in RANFIS is strongly influenced by three things, they are input determination, membership functions, and rule. The technique of defining rules followed the rule of genfis1 and genfis3. The model accuracy was measured using the smallest RMSE and MAPE. Based on the empirical studies which implemented Dow Jones Index, inflation, and USD/IDR exchange rate as the predictors and JCI as the response, it was obtained that optimum RANFIS model with gauss membership function, the number of cluster 2 with 2 rules generated by genfis3 produced RMSE in-sample 233.0 and out-sample 301.9, as well as MAPE in-sample 6.5% and out-sample 4.8%. While in regression analysis, it obtained RMSE in-sample 351.27 and out-sample 590.99, as well as MAPE in-sample 9.6% and out-sample 10.2% with violation of assumption. This shows that the result of RANFIS method is better than regression analysis. Keywords: JCI, regression analysis, neuro fuzzy, RANFIS, genfis

PERAMALAN JUMLAH KECELAKAAN DI KOTA SEMARANG TAHUN 2017 MENGGUNAKAN METODE RUNTUN WAKTU (Studi Kasus : Data Jumlah Kecelakaan Lalu Lintas di Kota Semarang Periode Januari 2012 – Desember 2016) Iantazar Rezqitullah Maharsi; Moch. Abdul Mukid; Yuciana Wilandari
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Accident data from Satlantas Polrestabes Semarang City is known that in 2016 there is an increase in the number of traffic accidents in the Semarang city. In the future the impact of accidents is predicted to be bigger so it is necessary to forecasting. Forecasting is one of the most important elements in decision making, because effective or not a decision generally depends on several factors that can not be seen at the time the decision was taken. In this time study the possible time series model is ARMA (2,2), ARMA (2,1), ARMA (1,2), ARMA (1,1), AR (2), AR (1), MA (2), MA (1). However, after testing, the model used is ARMA (1,1). This model is used because it meets all the assumption requirements that are parameter significant , residual indepedent test, residual normality test and the smallest Mean Square Error value. According to data forecasting results showed the highest number of crashes existed in January of 97 accidents and the lowest in December amounted to 93 accidents, So that the necessary to action from the relevant agencies to cope with the increasing number of traffic accidents in the city of Semarang. Keywords : Time Series Method, ARMA (1,1), Traffic Accident.

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Jawa tengah

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