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Jurnal Gaussian

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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.

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Search results for , issue "Vol 8, No 4 (2019): Jurnal Gaussian" : 13 Documents clear

PERBANDINGAN REGRESI KOMPONEN UTAMA DENGAN REGRESI KUADRAT TERKECIL PARSIAL PADA INDEKS PEMBANGUNAN MANUSIA PROVINSI JAWA TIMUR Vetranella .T.R.A. Sinaga; Diah Safitri; Rita Rahmawati
Jurnal Gaussian Vol 8, No 4 (2019): 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.v8i4.26749

The multiple regression classic assumptions are used to give linear unbiased and minimum variance estimator. In Human Development Index (HDI) and influencing factors in East Java, there are two variables with VIF more than 10 so the assumption of non-multicollinearity is not fulfilled. Principal component regression (PCR) and partial least squares regression (PLS-R) can solve this problem. By doing principal component analysis, there are two linear combinations to take as the new independent variables which are free from collinearity. In the PLS-R, NIPALS algorithm is used to calculate the components and other structures and to estimate the parameter. While in PCR all independent variables are significant, the percentage of households with drinking water is feasibles is not significant in the model. PLS-R’s is 95,85% is greater than PCR’s = 93,42%. PCR’s PRESS = 81,78 is greater than PLS-R’s PRESS = 61,0595.Keywords: Human Development Index (HDI), Multicollinearity, Principal Component Regression, Partial Least Squares Regression, , PRESS

PEMILIHAN INPUT MODEL ADAPTIVE FUZZY INFERENCE SYSTEM (ANFIS) BERBASIS LAGRANGE MULTIPLIER TEST DILENGKAPI GUI MATLAB (Aplikasi pada Data Harga Beras Kualitas Rendah di Indonesia Periode Januari 2013 – Februari 2019) Khusnul Umi Fatimah; Tarno Tarno; Abdul Hoyyi
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Adaptive Neuro Fuzzy Inference System (ANFIS) is a method that uses artificial neural networks to implement fuzzy inference systems. The optimum ANFIS model is influenced by the selection of inputs, number of membership and rules. In general, the selection of ANFIS input is based on Autoregressive (AR) unit as a result of ARIMA preprocessing. Thus it requires several assumptions. In this research, an alternative selection of ANFIS input based on Lagrange Multiplier Test (LM Test) is used to test hypothesis for the addition of one input. Preprocessing is conducted to obtain the value of partial autocorrelation against Zt. The input lag variable which has the highest partial autocorrelation is the first input ANFIS. The next input selection is selected based on LM test for adding one variable. To test the performance of LM Test, an empirical study of two groups of generated data and low quality rice prices is conducted as a case study. Generating data with stationary and non-stationary criteria has a good performance because it has very good forecasting ability with MAPE out sample for each characteristic are 5.6785% and 9.4547%. In the case study using LM Test, the selected input are and with the number of membership 2. The chosen model has very good forecasting ability with MAPE outsampel 6.4018%. Keywords : ANFIS, ANFIS Input, LM-Test, Low Quality Rice Prices, Forecasting

OPTIMASI REGRESI LOGISTIK MENGGUNAKAN ALGORITMA GENETIKA UNTUK PEMODELAN FAKTOR-FAKTOR YANG MEMPENGARUHI PENGGOLONGAN KREDIT BANK (Studi Kasus: Debitur di PT BPR Gunung Lawu Klaten Periode Tahun 2017) Reno Penggalih Surya Wardhani; Sudarno Sudarno; Di Asih I Maruddani
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Credit is the greatest asset managed by banks and also the most dominant contributor to the bank’s income. But in its implementation, the provision of credit to the public is at risk for non-performing loans. For this reason, creditors try to minimize the occurrence of non-performing loans by predicting credit risk appropriately. In this study, modeling the factors that influence credit classification at PT BPR Gunung Lawu is useful for predicting the credit risk of prospective debtors. Modeling are done using logistic regression and genetic algorithms. Factors suspected of influencing credit classification include age, gender, marital status, education, home ownership, employment, net income, tenor, type of business, type of loan, type of loan interest, and loan size. Estimated model parameters obtained from logistic regression were optimized using genetic algorithms. The fitness function used is pseudo or and MSE. The best model is generated by modeling with genetic algorithms based on MSE fitness. The model produces the highest value of 0.1958 and the lowest MSE value of 0.1648 with classification accuracy of 75.33%. Keywords: credit classification, logistic regression, genetic algorithms

KLASIFIKASI CALON DEBITUR KREDIT PEMILIKAN RUMAH (KPR) MULTIGUNA TAKE OVER MENGGUNAKAN METODE k NEAREST NEIGHBOR DENGAN PEMBOBOTAN GLOBAL GINI DIVERSITY INDEX Inas Hasimah; Moch. Abdul Mukid; Hasbi Yasin
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

House credit (KPR) is a credit facilities for buying or other comsumptive needs with house warranty. The warranty for KPR is the house that will be purchased. The warranty for KPR multiguna take over is the house that will be owned by debtor, and then debtor is taking over KPR to another financial institution. For fulfilled the credit to prospective debtor is done by passing through the process of credit application and credit analysis. With the credit analysis, will acknowledge the ability of debtor for repay a credit. Final decision of credit application is classified into approved and refused. k Nearest Neighbor by attributes weighting using Global Gini Diversity Index is a statistical method that can be used to classify the credit decision of prospective debtor. This research use 2443 data of KPR multiguna take over’s prospective debtor in 2018 with credit decision of prospective debtor as dependent variable and four selected independent variable such as home ownership status, job, loans amount, and income. The best classification result of k-NN by Global Gini Diversity Index weighting is when using 80% training data set and 20% testing data set with k=7 obtained APER value 0,0798 and accuracy 92,02%. Keywords: KPR Multiguna Take Over, Classification, KNN by Global Gini Diversity Index weighting, Evaluation of Classification

PERBANDINGAN METODE MOORA DAN TOPSIS DALAM PENENTUAN PENERIMAAN SISWA BARU DENGAN PEMBOBOTAN ROC MENGGUNAKAN GUI MATLAB Rafida Zahro Hasibuan; Alan Prahutama; Dwi Ispriyanti
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

MAN Asahan is an educational institution that selects new students every year. MAN Asahan sets certain criteria in choosing new students so that selected students are of high quality. The criteria determined are the Al-Qur'an test scores, national exam scores, Academic Potential Test scores and achievement certificates. In selecting new students who were accepted as many as 271 of the 530 registrants the school still used the manual process so that it needed accuracy and a long time. In this study a decision support system was created that could be a solution to assist the selection process according to school criteria. The system will applied is MOORA (Multi-Objective Optimization on the Base of Ratio Analysis) method and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) with the weighting method of ROC (Rank Order Centroid). Then the sensitivity analysis is done to determine the appropriate method to be chosen to obtain optimal results. This research was conducted with the help of the MATLAB GUI as a computing tool. The GUI that is built can simplify and speed up the selection process. Based on the results of the study, the average percentage value of sensitivity for the MOORA method is -1.61% while the TOPSIS method is -7.96%. With the existence of sensitivity analysis it can be known the most appropriate method for this case is the MOORA method.Keywords: Students, MOORA, TOPSIS, ROC, Sensitivity, GUI Matlab

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

PEMODELAN GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) SEASONAL PADA DATA CURAH HUJAN EMPAT KABUPATEN DI PROVINSI JAWA TENGAH Eko Siswanto; Hasbi Yasin; Sudarno Sudarno
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In many applications, several time series data are recorded simultaneously at a number of locations. Time series data from nearby locations often to be related by spatial and time. This data is called spatial time series data. Generalized Space Time Autoregressive (GSTAR) model is one of space time models used to modeling and forecasting spatial time series data. This study applied GTSAR model to modeling volume of rainfall four locations in Jepara Regency, Kudus Regency, Pati Regency, and Grobogan Regency. Based on the smallest RMSE mean of forecasting result, the best model chosen by this study is GSTAR (11)-I(1)12 with the inverse distance weighted. Based on GSTAR(11)-I(1)12 with the inverse distance weighted, the relationship between the location shown on rainfall Pati Regency influenced by the rainfall in other regencies. Keywords: GSTAR, RMSE, Rainfall

PEMODELAN FUNGSI TRANSFER DAN BACKPROPAGATION NEURAL NETWORK UNTUK PERAMALAN HARGA EMAS (Studi Kasus Harga Emas Bulan Juli 2007 sampai Februari 2019) Silvia Nur Rinjani; Abdul Hoyyi; Suparti Suparti
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The prestige of investment is increasingly rising as the people educates in managing finances. Gold is an alternative that most people tend to choose to invest. One of the important knowledge in gold investing is to predict the price in the future with factors that influence the price of gold. Therefore, in this research we made a model of gold prices based on crude oil prices. One method to forecast gold prices based on crude oil prices is the transfer function and backpropagation neural network. The results of transfer function model will be used as input for the backpropagation neural network method. The purpose of this research is to get the right forecasting method through the transfer function and backpropagation neural network model that can be used to predict gold prices. The results showed that the transfer function model with b = 0, r = [2], s = 0 and the ARMA noise model (0, [6]) is the best model to forecast the price of gold with the MAPE value of data out sample as 3,3507%. Keywords : Gold Price, Crude Oil Prices, Transfer Function,Backpropagation Neural Network, Forecasting

PERBANDINGAN NILAI KORELASI PADA KANONIK ROBUST (METODE MINIMUM COVARIANCE DETERMINANT) DAN KANONIK KLASIK (Studi Kasus Data Struktur Ekonomi dan Kesejahteraan Rakyat di Jawa Barat 2016) Widi Rahayu; Sudarno Sudarno; Alan Prahutama
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Canonical correlation analysis is a multivariate statistical analysis that aims to examine the correlation between two groups of variabels in a way to maximize the value of correlation between variabels. The outlier in the data affect the covariance matrix is generated, So that use robust multivarat. There is robust multivariate approach to the analysis of canonical robust with MCD method (Minimum Covariance Determinant). This final project aims to determine comparison between correlation value of robust canonical with MCD and canonical classical methods. With a data theres containing of outliers in the case studies of people's welfare and economic structures in West Java in 2016. Used a set of variabels welfare of people consist of 6 variabel (Y) and a set of variabels economic structure which consists of four variabels (X). Based on the analysis results obtained that robust canonical correlation values better explain the correlation between two sets of variabels, the correlation value 0.99552, =0.91228, =0.71529, =0.63174, While the correlation value on classical canonical are 0.931489, 0.538672, 0.387099, 0.259318, Canonical robust can be interpreted more because it meets the test of significance are partially and directly, while the classical canon can not be interpreted further because it does not meet the test of the significance of the function. Keywords : Classical canonical correlation, canonical correlation robust correlation value, Minimum Covariance Determinant (MCD)

IMPLEMENTASI METODE SAW DAN WASPAS DENGAN PEMBOBOTAN ROC DALAM SELEKSI PENERIMAAN PESERTA DIDIK BARU (Studi Kasus: Madrasah Tsanawiyah (MTs) Negeri Kisaran Kabupaten Asahan Provinsi Sumatera Utara Tahun Ajaran 2018/2019) Nabila, Eva Salsa; Rahmawati, Rita; Widiharih, Tatik
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Multi Attribute Decision Making (MADM) is one of the decision-making methods to determine the best alternative from a number of alternatives based on certain criteria. There are several methods that can be used to solve MADM problems including Simple Additive Weighting (SAW) and Weighted Aggregated Sum Product Assesmen (WASPAS). Both methods are applied in the selection of prospective new students. In this study, MTsN Kisaran selected 192 students received from 422 registrans and determined certain criteria to get quality students. The criteria determined are the value of the national exam, the value of the Al-Qur'an test, and the value of the academic potential test. The method applied is SAW and WASPAS with theÂ weighting Rank Order Centroid (ROC). Then a sensitivity analysis is carried out to determine a viable methods selected to obtain optimal results. This research was designed with the help of the Matlab GUI as a computing tool to simplify and accelerate the selection process. Based on the results of the study, the average percentage value of sensitivity for the SAW method was -0.82% while the WASPAS method was -0.87%. With the existence of sensitivity analysis it can be known the most appropriate method for this case is the SAW method.Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Keywords: Students, SAW, WASPAS, ROC, Sensitivity, GUI Matlab.

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