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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 36 Documents

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Search results for , issue "Vol 3, No 4 (2014): Jurnal Gaussian" : 36 Documents clear

APLIKASI METODE MOMEN PROBABILITAS TERBOBOTI UNTUK ESTIMASI PARAMETER DISTRIBUSI PARETO TERAMPAT PADA DATA CURAH HUJAN (Studi Kasus : Data Curah Hujan di Kota Semarang Tahun 2004-2013) Rengganis Purwakinanti; Agus Rusgiyono; Alan Prahutama
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The method used to analyze the extreme rainfall is Extreme Value Theory (EVT). One of the approaches in the EVT is Peak Over Threshold (POT) which follows the Generalized Pareto Distribution (GPD). The shape and scale parameter estimates obtained using the method of probability weighted moment. The results of this research were presumptive maximum value within a period of 1 year to the period 2004 to 2013 showed that year 2009/2010 has the possibility of extreme value compared with other years. Also obtained Mean Absolute Percentage Error values ( MAPE ) of 33.19 %. This result is a big difference because the MAPE values above 10 %, thus allowing the emergence of extreme values. Keywords: Rainfall, Extreme Value Theory, Peak Over Threshold, Generalized Pareto Distribution, Probability Weighted Moment

PEMBENTUKAN POHON KLASIFIKASI BINER DENGAN ALGORITMA QUEST (QUICK, UNBIASED, AND EFFICIENT STATISTICAL TREE) PADA DATA PASIEN LIVER Muhammad Rosyid Abdurrahman; Dwi Ispriyanti; Alan Prahutama
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In this modern era of fast food commonly found that sometimes have chemical substances and the increasing number of motor vehicles that cause the uncontrolled circulation of air pollution that can affect the health of the human liver. To assist in analyzing the presence of liver disorders in humans can be used QUEST (Quick, Unbiased, and Efficient Statistical Tree) algorithm to classify the characteristics of the patient's liver by liver function tests performed in clinical laboratories. QUEST construct rules to predict the class of an object from the values of predictor variables. The tree is constructed by partitioning the data by recuresively, where class and the values of the predictor variables of each observation in the data sample is known. Each partition is represented by a node in the tree. QUEST is one of the binary classification tree method. The results of the classification tree is formed, an important variable in classifying a person affected by liver disease or not, that is the variable Direct Bilirubin, Alkaline Phosphatase, Serum Glutamic Oxaloacetic Transaminase (SGOT), and age of the patient. Accuracy of the QUEST algorithm classifying liver patient data by 73,4 %. Keywords: binary classification trees, QUEST algorithm, liver patient data.

KLASIFIKASI TINGKAT KELUARGA SEJAHTERA DENGAN MENGGUNAKAN METODE REGRESI LOGISTIK ORDINAL DAN FUZZY K-NEAREST NEIGHBOR (STUDI KASUS KABUPATEN TEMANGGUNG TAHUN 2013) Dini Puspita; Suparti Suparti; Yuciana Wilandari
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Indonesian is a country that have a lot of people, its about 250 millions people. Each of they have a family. Family is a group of person who have relationship and responsibility for each other. The characteristic of family is very important in relationship with society. A lot of requirement must to be have in family. Ownership requirement in family can be figure of that family. In case, accuracy of classification about prosperity family in Kabupaten Temanggung 2013th will be analysed, in BKKBN is have 5 level of prosperity family, there are pra prosperity family, prosperity family 1, prosperity family 2, prosperity family 3, and prosperity family 3 plus. Regression Logistics Ordinal method and Fuzzy K-Nearest Neighbor (FK-NN) method be use for analysis this minithesis. From the analysis regression logistics ordinal accuracy of classification have value 80,47%, and FK-NN have value 87,60%. Both of the value accuracy of classification can get conclusion regression logistics ordinal method have a less value than FK-NN. So FK-NN method is a best method for level of prosperity family in Kabupaten Temanggung 2013th.Keywords : Prosperity Family, Regression Logistics Ordinal, Fuzzy K-Nearest Neighbor (FK-NN)

Peramalan Inflasi Menurut Kelompok Pengeluaran Makanan Jadi, Minuman, Rokok dan Tembakau Menggunakan Model Variasi Kalender (Studi Kasus Inflasi Kota Semarang) Berlian, Amanda Lucky; Wilandari, Yuciana; Yasin, Hasbi
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Inflation is rising prices in general and continuously. Inflationary expenditure groups are divided into seven groups, and one group which spending considerable influence current inflation in Indonesia is by expenditure groups, food, beverages, cigarettes and tobacco. This is because the Indonesian people are very consumptive, especially when it coming to Eid. The movement of the month when Eid occurs once in every three years, so that changes raises a calendar variation. Calendar variation method is a method which modifies the dummy regression models with ARIMA models. In this final project, modeling and forecasting of inflation data by type of expenditure, food, beverages, cigarettes and tobacco in Semarang using variations of the calendar with holidays variation effects due to Eid. Based on the analysis and discussion shows that the best calendar variation model is ARIMA (1,0,0), with the forecasting results shows a significant increase of inflation when the month of Ramadan come.Keywords : inflation, calendar variation, the dummy regression, ARIMA

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEMISKINAN DI JAWA TENGAH MENGGUNAKAN MODEL GALAT SPASIAL Octafinnanda Ummu Fairuzdhiya; Rita Rahmawati; Agus Rusgiyono
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Poverty is one of problems in developing country like Indonesia. From year to year, poverty in Central Java has decreased. This study is aimed to know the poverty model in Central Java by using Spatial Error Model. This research uses data from the number of poor people in Central Java in 2012. Spatial Error Model is a spatial method that showed spatial autocorrelation in the error. In Spatial Error Model, there are spatial dependency effect and spatial heterogenity. The variables that significantly affect the number of poor people in Central Java through Spatial Error Model are the percentage of 10 years old–over population with the highest education is primary school ( X2) and the number of households that have access to reliable drinking water (X3). This Spatial Error Model results R2 are 75,39% with the AIC are 63,36. It is better than regression model of Ordinary Least Square (OLS) which produces 66,3% of R2 with AIC are 69,286. It showed the poverty model in Central Java by using Spatial Error Model is better than regression model of Ordinary Least Square (OLS) and in OLS assumption of homoskedasticity not significant. Keywords: Poverty, Regression, Ordinary Least Square, Spastial Error Model

PEMODELAN PROPORSI PENDUDUK MISKIN KABUPATEN DAN KOTA DI PROVINSI JAWA TENGAH MENGGUNAKAN GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION Khusnul Yeni Widiyanti; Hasbi Yasin; Sugito Sugito
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Regression analysis is a statistical analysis that aims to quantify the effect of predictor variables on the response variable. Geographically Weighted Regression (GWR) is a local form of regression and a statistical method used to analyze spatial data. Geographically and Temporally Weighted Regression (GTWR) is the development of GWR models to handle data that is not stationary both in terms of spatial and temporal simultaneously. In obtaining estimates of parameters of the GTWR model can be used Weighted Least Square method (WLS). Selection of the optimum bandwidth used method of Cross Validation (CV). Conformance testing global regression and GTWR models approximated by the distribution of F, whereas the partial testing of the model parameters using the t distribution. Application GTWR models at the level of poverty in Central Java province in 2008 to 2012 showed GTWR models differ significantly from the global regression model. Based on R2 and Mean Squared Error (MSE) value between the global regression model and GTWR models, it is known that the GTWR model with exponential weighting kernel function is the best model is used to analyze proportion of poor people in Central Java province in 2008 to 2012 because it has a value of R2 larger and MSE is the smallest. Keywords: Bandwidth, Cross Validation, Exponential Kernel Functions, Geographically and Temporally Weighted Regression, Weighted Least Square, R2, Mean Squared Error.

APLIKASI METODE GOLDEN SECTION UNTUK OPTIMASI PARAMETER PADA METODE EXPONENTIAL SMOOTHING Mahkya, Dani Al; Yasin, Hasbi; Mukid, Moch. Abdul
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Forecasting is predicting the activities values that have been previously known. One of the methods that can be used to predict is Exponential Smoothing. In this study, exponential smoothing method used is Single Exponential Smoothing (SES), Holt Double Exponential Smoothing (DES) and Triple Exponential Smoothing Holt-Winter (TES) Additive and Multiplicative models. Data used is value of Central Java Export from the period January 2006 until December 2013. There is some weighting parameters were evaluated in this method in order to produce a minimum error. Trial error method is used to obtain the weighting parameters. For SES method parameters evaluated were the parameters α, in DES method there are α and γ. And TES method there are α, γ and β. The value that will be minimize is Persentage Mean Absolute Error (MAPE). This study used the Golden Section method to find the parameter values that minimize the weighting function of MAPE. And built a Graphical User Interface (GUI) MATLAB in order to facilitate the analysis process. The Golden Section analysis found the best model is the TES Holt Winters Additive because it has a minimum value of MAPE. With Use the TES Holt Winters Additive will continue to predict the value of exports of Central Java 12 periods ahead with weighting parameters that minimize MAPE. Keywords : Exponential Smoothing, Graphical User Interface (GUI), Export, Golden Section, Predict

PEMODELAN PERSENTASE PENDUDUK MISKIN DI KABUPATEN DAN KOTA DI JAWA TENGAH DENGAN PENDEKATAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION Hakim, Arief Rachman; Yasin, Hasbi; Suparti, Suparti
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Regression analysis is a statistical analysis that models the relationship between the response variable and the predictor variable. Geographically Weighted Regression (GWR) is the development of linear regression with the added factor of the geographical location where the response variable is taken, so that the resulting parameters will be local. Mixed Geographically Weighted Regression (MGWR) has a basic concept that is a combination of a linear regression model and GWR, by modeling variables that are local and which are global variables. Methods for estimating the model parameters MGWR no different from the GWR using Weighted Least Square (WLS). Selection of the optimum bandwidth using the Cross Validation (CV). Application models MGWR the percentage of poor people in the district and town in Central Java showed MGWR models that different significantly from the global regression model. As well as models generated for each area will be different from each other. Based on the Akaike Information Criterion (AIC) between the global regression model, the GWR and MGWR models, it is known that MGWR models with Gaussian kernel weighting function is the best model is used to analyze the percentage of poor in the counties and cities in Central Java because it has the smallest AIC value.Keywords: Akaike Information Criterion, Cross Validation, Kernel Gaussian function, Mixed Geographically Weighted Regression, Weighted Least Square.

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

PENENTUAN MODEL DAN PENGUKURAN KINERJA SISTEM PELAYANAN PT. BANK NEGARA INDONESIA (PERSERO) Tbk. KANTOR LAYANAN TEMBALANG Masfuhurrizqi Iman; Sugito Sugito; Dwi Ispriyanti
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

PT. Bank Negara Indonesia (Persero) Tbk. Tembalang Services Office is a provider of service facilities engaged in the financial sector. As a service facilities provider, queue problem is a problem that occurs absolute and must be considered. The queuing situation occurs because the number of customers in a service facility that exceeds the capacity available to perform such services. At PT. Bank Negara Indonesia (Persero) Tbk. Tembalang Services Office, the queue occurs both at the Teller and Customer Service. After analysis, the best model of a queuing system at the Teller is (M/M/3):(GD:∞:∞), while the best model of queuing system in the Customer Service section is (M/M/2):(GD:∞:∞). The model can be concluded that the queue system available in PT. Bank Negara Indonesia (Persero) Tbk. Tembalang Services office is optimal. Keywords : PT. Bank Negara Indonesia (Persero) Tbk. Tembalang Services Office, queuing system, Teller, Customer Service

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Department of Statistic, Faculty of Science and Mathematics , Universitas Diponegoro Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro Gedung F lt.3 Tembalang Semarang 50275

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Kota semarang,

Jawa tengah

INDONESIA