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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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Articles 733 Documents

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PERAMALAN INDEKS HARGA KONSUMEN 4 KOTA DI JAWA TENGAH MENGGUNAKAN MODEL GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) Lina Irawati; Tarno Tarno; Hasbi Yasin
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Generalized Space Time Autoregressive (GSTAR) models are generalization of the Space Time Autoregressive (STAR) models which has the data characteristics of time series and location linkages (space-time). GSTAR is more flexible when faced with the locations that have heterogeneous characteristics. The purposes of this research are to get the best GSTAR model and the forecasting results of Consumer Price Index (CPI) data in Purwokerto, Solo, Semarang and Tegal. The best model obtained is GSTAR (11) I(1) using cross correlation normalization weight because it generated white noise and multivariate normal residuals with average value of MAPE 3,93% and RMSE 10,02. The best GSTAR model explained that CPI of Purwokerto is only affected by times before, it does not affect to other cities but can be affecting to other cities. Otherwise, CPI of Surakarta, Semarang and Tegal are affecting each others. Keywords: GSTAR, Space Time, Consumer Price Index, MAPE, RMSE

PERAMALAN DAYA LISTRIK BERDASARKAN JUMLAH PELANGGAN PLN MENGGUNAKAN MODEL FUNGSI TRANSFER DENGAN OUTLIER (Studi Kasus di PT PLN (Persero) Rayon Semarang Selatan) Retza Bahtiar Anugrah; Sudarno Sudarno; Budi Warsito
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Electrical energy is one of the components of Gross Domestic Product which able to stimulate the economic matter because it has been becoming a primary needs in the society. In order to meet the growing electrical energy, State-Owned Enterprises (SOEs) need to develop systems and proper planning. It needs a forecasting of electric power based on customer to meet a sufficient electricity supply. This study aims to predict the electrical power by electric customers using transfer function model with outliers. The use of transfer function model is intended to determine the role of power users that have an impact on the electric power. One of the stages of modeling the transfer function is to set the order of the transfer function parameters, they are b, r, and s. And by modeling the outlier is useful to eliminate the effect of outliers itself. The analysis and discussion show that based on the AIC value, the best model is the transfer function model by weighting the impulse response of the parameter that is ω_0 = 55,55652 and the noise series model of the transfer function is ARIMA (1,0,1) with 8 outliers. The details of the outliers consist of one Additive Outliers type in the 33rd and seven Level Shift Outliers in the 14th, 31st, 9th, 10th, 21st, 22nd and 58th. Size forecasting accuracy using MAPE value 19.77%. Keywords: Transfer function, outliers, ARIMA, electrical power, AIC, MAPE

REGRESI ROBUST ESTIMASI-M DENGAN PEMBOBOT ANDREW, PEMBOBOT RAMSAY DAN PEMBOBOT WELSCH MENGGUNAKAN SOFTWARE R Aulia Desy Deria; Abdul Hoyyi; Mustafid Mustafid
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Robust regression is one of the regression methods that robust from effect of outliers. For the regression with the parameter estimation used Ordinary Least Squares (OLS), outliers can caused assumption violation, so the estimator obtained became bias and inefficient. As a solution, robust regression M-estimation with Andrew, Ramsay and Welsch weight function can be used to overcome the presence of outliers. The aim of this study was to develop a model for case study of poverty in Central Java 2017 influenced by the number of unemployment, population, school participation rate, Human Development Index (HDI), and inflation. The result of estimation using OLS show that there is violation of heteroskedasticity caused by the presence outliers. Applied robust regression to case study proves robust regression can solve outliers and improve parameter estimation. The best robust regression model is robust regression M-estimation with Andrew weight function. The influence value of predictor variables to poverty is 92,7714% and MSE value is 370,8817. Keywords: Outliers, Robust Regression, M-Estimator, Andrew, Ramsay, Welsch

ANALISIS KESENJANGAN KUALITAS PELAYANAN TERHADAP PENGUNJUNG PERPUSTAKAAN UNIVERSITAS DIPONEGORO Dedy Douglas Harianja; Rita Rahmawati; Moch. Abdul Mukid
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Good quality service is not based on the perception of the service provider, but based on the perception of service users. If the service received exceed the expectations of users, the quality of service perceived as an ideal quality. This study aimed to analyze the quality of service to visitors to Diponegoro University library based on five variables dimensions of service quality (Service Quality), namely Tangibels, Reliability, Responsiveness, Assurance, and Empathy. Collecting data in this study using a questionnaire distributed to 97 students as respondents. The sampling method used was accidental sampling sampling method. The data obtained and analyzed using Importance Performance Analysis (IPA) and the Customer Satisfaction Index (CSI). Based on this research, calculations showed that all Service Quality indicator variable is negative, which means that all services provided is still below the expectations of library visitors. While the Cartesian diagram shows that there are four indicator variables are in quadrant Concentrate Here is the complete collection, ease of finding references, employee awareness of the needs of visitors, and the friendliness and courtesy of service that means should gradually be corrected immediately. Value Customer Satisfaction Index (CSI) of 72% which means that the overall level of visitor satisfaction is the criterion satisfied. Keywords: Service Quality, Importance Performance Analysis, Customer Satisfaction Index

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI BAYI BERAT LAHIR RENDAH DENGAN MODEL REGRESI LOGISTIK BINER MENGGUNAKAN METODE BAYES (Studi Kasus di Rumah Sakit Umum Daerah Kota Semarang) Laily Nadhifah; Hasbi Yasin; Sugito Sugito
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

This study aims to elucidate several factors which affect low-birth-weight (LBW) infants in Semarang General Hospital (RSUD) in the period from July to December 2011. With regard to the MilleniumDevelopment Goals’s targets, which are predominantly intended to reduce the child mortality rate, serious investigations are highly needed to identify the factors that determine the rate of babies born with the low-birth-weight category. This problem can be solved with the Binary Logistic Regression model,using the Bayesian method. The Bayesian method is one of the parameter estimation technique which employ prior value as initial knowledge. The conducted research is to argue that both factors of age and the maternal hemoglobin level considerably give influence on LBW birth. Based on the research analysis, it is extremely recommended that mother to be pays much attention not to be pregnant at relatively young age and maintain the secure level of hemoglobin during pregnancy.

KLASIFIKASI DIAGNOSA PENYAKIT DEMAM BERDARAH DENGUE (DBD) MENGGUNAKAN SUPPORT VECTOR MACHINE (SVM) BERBASIS GUI MATLAB Chainur Arrasyid Hasibuan; Moch. Abdul Mukid; Alan Prahutama
Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Dengue Hemorrhagic Fever (DHF) is a disease caused by the bite of infected Aedes mosquito by one of the four types of dengue virus with clinical manifestations of fever, muscle aches or joint pain which followed by leukopenia, rash, thrombocytopenia and hemorrhagic diathesis. There are six criteria for determining and catagorizing a positive or negative dengue patients, the variable gender of the patient, the patient's age, the increase in hemoglobin (Hb), increased hematocrit (Hct), the level of platelet and leukocyte levels.Based on these criteria, data of positive and negative catagorized patient will be classified by Support Vector Machine (SVM) using Matlab software. The concept of classification with SVM define as a search for the best hyperplane which serves as a divider of two classes of data in the input space. Kernel function is used to convert the data into a higher dimensional space to allow separation. In order to determine the best parameters of kernel function, hold-out method is used. In the classification by SVM method, 96.4286% obtained as the best accuracy value by using polynomial kernel function. Keywords: Dengue Hemorrhagic Fever (DHF), Classification, Support Vector Machine (SVM), hold-out, Kernel Function.

VERIFIKASI MODEL ARIMA MUSIMAN MENGGUNAKAN PETA KENDALI MOVING RANGE (Studi Kasus : Kecepatan Rata-rata Angin di Badan Meteorologi Klimatologi dan Geofisika Stasiun Meteorologi Maritim Semarang) Kiki Febri Azriati; Abdul Hoyyi; Moch. Abdul Mukid
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Forecasting method Box-Jenkins ARIMA (Autoregressive Integrated Moving Average) is a forecasting method that can provide a more accurate forecasting results. To verify the model obtained using the one Moving Range Chart. The control charts are used to determine the change in the pattern of file seen from the residual value (the difference between the actual file and the file forecasting). File used in this study the average wind speed in the Tanjung Emas harbor during January 2008 to December 2013. The best of Seasonal ARIMA model is ARIMA (0,0,1) (0,0,1) 12. The results of the verification using the Moving Range Control Chart on the model showed that all residual values are within control limits to the length of the shortest interval, means of verification results show that the model is a good model used for forecasting future periods. Forecasting is generated during the period of the next 15 shows the seasonal pattern. This is shown in the figure forecast 2014 average wind speeds are highest in January, as well as forecasting the 2015 figures the average speed of the highest winds also occurred in January. Forecasting results reflect past file, because the actual file used also showed a seasonal pattern with the same seasonal period is annual, where the numbers mean wind speeds are highest in January. Keywords : Seasonal ARIMA, Moving Range Control Chart, Mean wind speeds.

PERBANDINGAN ANALISIS DISKRIMINAN FISHER DAN NAIVE BAYES UNTUK KLASIFIKASI RISIKO KREDIT (Studi Kasus Debitur di Koperasi Jateng Amanah Mandiri Cabang Sukorejo Kendal) Abdur Rofiq; Triastuti Wuryandari; Rita Rahmawati
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Credit is a form of money lending to debitors conducted by financial institutions such as cooperatives. In practice there are obstacles in the form of bad credit. Analyze by Fisher discriminant analysis method and Naive Bayes is used to classify the debitors fall into the category bad debitorr or not. This study uses data from the Debitors of Cooperative of Central Java Amanah Independent in Sukorejo Kendal Branch. The data obtained is used for classification by Fisher discriminant analysis and Naive Bayes method. Data obtained has multivariate normal distribution, has the same of variance-covariance matrix and has metric scale. Fisher discriminant analysis and Naive Bayes calculated and compared to the level of accuracy. From this research, the degree of accuracy of each method, namely 90% for Fisher Discriminant Analysis and 83.33% for the Naive Bayes. Having tested using the proportion test, Fisher discriminant analysis method is no different accuracy when compared with Naive Bayes to classify credit risk. Keywords: debitors, credit risk, Fisher discriminant analysis, Naive Bayes.

ANALISIS SUMBER-SUMBER PENDAPATAN DAERAH KABUPATEN DAN KOTA DI JAWA TENGAH DENGAN METODE GEOGRAPHICALLY WEIGHTED PRINCIPAL COMPONENTS ANALYSIS (GWPCA) Alfiyatun Rohmaniyah; Hasbi Yasin; Yuciana Wilandari
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The districts/cities sources of revenue in Central Java consists of Natural Revenue District (PAD), the equalization fund (DAPER), and other local income. PAD consists of four variables namely local tax (X1) , retribution (X2) , the results of regional company and wealth management that is separated (X3) , and other legal PAD (X4). DAPER consists of four variables namely sharing of tax revenue (X5) , sharing of non-tax revenue (X6) , the general allocation fund (X7) , and the special allocation fund (X8). Other region revenues (X9) is a source of local income that is not included in the PAD or DAPER. Sources of local revenue variables are mutually correlated multivariate data and have spatial effect. Therefore Geographically Weighted Principal Components Analysis (GWPCA) is suitable for analyzing sources of local revenue variables. GWPCA is a multivariate analysis method that is used to eliminate multicolliniearity in the multivariate data that have spatial effect. The result of this study is that the variables of revenue sources on each location can be replaced by three new variables called PC1, PC2, and PC3 which is independent each other. Variance Cumulative Proportion that can be explained by those new variables is approximately 80%. Based on the first principal component (PC1) that have variance proportion approximately 50%, there are three groups which has different carracteristics. The first group is the region that the revenue have influenced by variables X9 followed by X1. The second group is the region that the revenue have influenced by variables X9 followed by X2. The third group is the region that the revenue have influenced by variables X9 followed by X5. It is also seen that Kudus District has the most distinct characteristics which the revenue are influenced by variables X5 followed by X9.

PREDIKSI SIMPANAN BERJANGKA PADA BANK UMUM DAN BPR MENGGUNAKAN METODE ARIMA DENGAN OUTLIERS DAN ARIMA BOOTSTRAP Shinta Karunia Permata Sari; Rukun Santoso; Suparti Suparti
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Time deposits or often referred to as deposits are deposits that take it in accordance with the time agreed. The position of time deposits in commercial banks and BPRs is monitored by Bank Indonesia, Because large time deposits affect the level of the economy in Indonesia, one of them to facilitate public credit in an opening and building businesses. However, in the course of this term deposit data position is influenced by many other factors that resulted in the existence of the data of this condition leads to the assumption of normality becomes unfulfilled. Some methods that can be used to overcome this problem include ARIMA Box-Jenkins with outliers detection and Bootstrapping ARIMA. In this case, the data is public time deposits at commercial banks and BPR from January 2010 to April 2016. The best ARIMA model is ARIMA (1,1,0), With the best method is ARIMA Bootstrap because it has MAPE value (out sample) of 4.8257% less than MAPE value’s ARIMA with outliers detection which it has 6.1610%. Based on these results it is concluded that in this case the nonparametric method is more appropriate to be used by ignoring the distribution assumption. Keywords : Deposits, ARIMA, Outliers detection, Bootstrap ARIMA

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