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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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Jurnal Gaussian
Published by Universitas Diponegoro
ISSN : -     EISSN : 23392541     DOI : -
Core Subject : Education,
Other
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.
Arjuna Subject : -
Articles 733 Documents
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PERBANDINGAN METODE K-MEANS DAN METODE DBSCAN PADA PENGELOMPOKAN RUMAH KOST MAHASISWA DI KELURAHAN TEMBALANG SEMARANG Sisca Agustin Diani Budiman; Diah Safitri; Dwi Ispriyanti
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (478.624 KB) | DOI: 10.14710/j.gauss.v5i4.14732

Abstract

Students as well as community or household, as well as economic activities daily, including consumption. The student needs to choose a place to stay is also one form of consumption activities. There are many factors that affect student preferences in the selection of boarding houses, including price, amenities, location, income, lifestyle, and others. The rental price boarding and facilities offered significant positive effect on student preferences in choosing a boarding house. Based on rental rates and facilities it offered to do the grouping in order to know the condition of the student boarding house in the Village Tembalang. Grouping is one of the main tasks in data mining and have been widely applied in various fields. The method used to classify is K-Means and DBSCAN with a number of groups of three. Furthermore, the results of both methods were compared using the Silhouette index values to determine which method is better to classify the student boarding house. Based on the research that has been conducted found that the K-Means method works better than DBSCAN to classify the student boarding house as evidenced by the value of the Silhouette index on K-Means of 0.463 is higher than the value at DBSCAN Silhouette index is equal to 0.281. Keywords: student boarding houses, data mining, clustering, K-Means, DBSCAN
KAJIAN RELIABILITAS DAN AVAILABILITAS PADA SISTEM KOMPONEN PARALEL Pradewi, Riana Ayu Andam; Sudarno, Sudarno; Suparti, Suparti
Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (428.85 KB) | DOI: 10.14710/j.gauss.v3i2.5911

Abstract

Reliability and availability are a measure of item or system performance. System reliability and system availability obtained from the calculation of reliability and availability of the components in the system. Reliability of components in the system are affected by the time to failure (TTF). While the availability of components in the system are affected by the mean time to failure (MTTF) and mean time to repair (MTTR). Given observed time data of lifting machines consists of trolley drive and hoist in parallel, is measured its system availability. Parameter values determined using simple linear regression and maximum likelihood estimation. Furthermore observation time test data distributions in the Kolmogorov-Smirnov test. Trolley drive has exponential distribution for failure time data with  while repair time data is normal distribution with  and . Hoist has weibull failure time data with  and  while lognormal repair time data has  and . The higer value of ti,system reliability value will be close to 0 and the engine can survive until the specified time. Due to MTTF is 4000 hours and MTTR is 45,70 hours, trolley drive’s availability is 98,87%. Availability of hoist is 98,84% from MTTF is 5821,61 hours and MTTR is 67,80 hours. The parallel system availability is 99,986% means the probability of system is in the state of functioning at given time is 99,986%.
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

Abstract

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
PROYEKSI DATA PRODUK DOMESTIK BRUTO (PDB) DAN FOREIGN DIRECT INVESTMENT (FDI) MENGGUNAKAN VECTOR AUTOREGRESSIVE (VAR) Indra Satria; Hasbi Yasin; Suparti Suparti
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (661.248 KB) | DOI: 10.14710/j.gauss.v4i4.10224

Abstract

Gross Domestic Product (GDP) and Foreign Direct Investment (FDI) is an economic instrument that has an attachment and often used for economic development of a country. To predict these two variables there are several methods that can be used, one of which is a method of Vector Autoregressive (VAR). VAR method has some assumptions that the data to be foreseen must have an attachment, stationary in the mean and variance and the resulting error must meet the test of independence and normal distribution. In the early stages of identification done by considering the value of AIC as a determinant of the optimal lag value, which in this case lag 4 who came out as the optimal lag. Granger causality test as an attachment test between variable and Augmented Dickey Fuller test (ADF) as a stationary test. In the parameter estimation phase used Ordinary Least Square method (OLS) to determine the values of the parameters to be used as a model. After getting the model it is necessary to do verification on condition that the residuals must comply with the independence test and multivariate normal test. With a second fulfillment verification test is carried out projections for the next 5 years with a value of R-Square 64% to GDP and 48% for the variable FDI Keywords: FDI, GDP, VAR, causality, independency, multivariate normal, R-Square
KAJIAN DATA KETAHANAN HIDUP TERSENSOR TIPE I BERDISTRIBUSI EKSPONENSIAL DAN SIX SIGMA Murti, Victoria Dwi; Sudarno, Sudarno; Suparti, Suparti
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (675.946 KB) | DOI: 10.14710/j.gauss.v1i1.917

Abstract

Analisis data tahan hidup biasanya digunakan untuk mengetahui ketahanan hidup suatu produk dalam bidang industri. Data waktu hidup dapat berupa data tersensor tipe I, tipe II dan tipe III. Dalam penelitian ini digunakan data tersensor tipe I yang merupakan suatu data waktu kematian atau kegagalan dimana semua unit uji n masuk pada waktu yang sama dan percobaan dihentikan sampai waktu tertentu. Salah satu distribusi yang dapat digunakan untuk menggambarkan waktu hidup adalah distribusi eksponensial dengan parameter l. Parameter l diestimasi dengan menggunakan metode Maximum Likelihood Estimation (MLE). Untuk mengetahui hubungan linear data kegagalan dengan intensitas kegagalan produk digunakan regresi linier. Selain itu, untuk memperkecil tingkat kegagalan yaitu dengan memprediksi kegagalannya menggunakan tingkat sigma. Nilai tingkat sigma bisa didapatkan dari DPMO (Defect Per Million Opportunity) yang berhubungan dengan MTTF (Mean Time To Failure) atau fungsi Reliabilitas. Jika nilai DPMO semakin kecil maka nilai tingkat sigma semakin besar.
ESIMASI PARAMETER REGRESI RIDGE MENGGUNAKAN ITERASI HOERL, KENNARD, DAN BALDWIN (HKB) UNTUK PENANGANAN MULTIKOLINIERITAS Nur Aeniatus Solekakh; Dwi Ispriyansti; Sudarno Sudarno
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1277.774 KB) | DOI: 10.14710/j.gauss.v4i4.17099

Abstract

Regression analysis is statistical method used to analyze the dependence of respond variables to predictor variable. In multiple linear regression analysis, there are assumptions that must be met, they are normality, homoscedasticity, absence of multicollinearity, and absence of autocorrelation. One of assumption frequently found is multicollinearity. If multicollineraity is exist between predictor variables, then regression analysis with ordinary least square is no longer used. Ridge regression is regression method to handle multicollinearity. The ridge estimator involves adding biasing constant (k) to each diagonal element of  X’X. Biasing constant (k) is determined by Hoerl, Kennard, and Baldwin (HKB) iteration method. This regression can be applied to inflation rate in Indonesia data and the factors that influence, they are BI rate, money supply, and exchange rate of rupiah. Ridge regression analysis, the VIF (Variance Inflation Factor) values for each predictor variables BI rate, money supply, and exchange rate of rupiah are 1.6637, 3.2712, and 4.3309. SinceVIF values are not exceed to 10, then there is no multicollinearity in ridge regression model.Keywords: Inflation,  Multikollinearity, Ridge Regression,  HKB Iteration, VIF
PENERAPAN METODE KLASIFIKASI SUPPORT VECTOR MACHINE (SVM) PADA DATA AKREDITASI SEKOLAH DASAR (SD) DI KABUPATEN MAGELANG Octaviani, Pusphita Anna; Wilandari, Yuciana; Ispriyanti, Dwi
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (587.385 KB) | DOI: 10.14710/j.gauss.v3i4.8092

Abstract

Accreditation is the recognition of an educational institution given by a competent authority, that is Badan Akreditasi Nasional Sekolah/Madrasah (BAN - S/M) after it is assessed that the institution has met the eight components of the accreditation assessment. An elementary school, as one of the compulsory basic education, should have the status of accreditation to ensure the quality of education. This study aimed to apply the classification method Support Vector Machine (SVM) on the data accreditation SD in Magelang. Support Vector Machine (SVM) is a method that can be used as a predictive classification by using the concept of searching hyperplane (separator functions) that can separate the data according to the class. SVM using the kernel trick for non-linear problems which can transform data into a high dimensional space using a kernel function, so that the data can be classified linearly. The results of this study indicate that the prediction accuracy of SVM classification using Gaussian kernel function RBF is 93.902%. It is calculated from 77 of 82 elementary schools that are classified correctly with the original classes. Keywords : Accreditation, Classification, Support Vector Machine (SVM), hyperplane, Gaussian RBF Kernel, Accuracy 
PENERAPAN METODE ORDINARY KRIGING PADA PENDUGAAN KADAR NO2 DI UDARA (Studi Kasus: Pencemaran Udara di Kota Semarang) Gera Rozalia; Hasbi Yasin; Dwi Ispriyanti
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (796.326 KB) | DOI: 10.14710/j.gauss.v5i1.11034

Abstract

Air pollution must be addressed. Nitrogen Dioxide is one of the important factors in air pollution. To determine concentration level of the pollutant “Badan Lingkungan Hidup Kota Semarang” already take measurements  at several  points.  However,  because of  blocked  considerable cost, is  not  much  point to do measurements. In this study, will be used Ordinary Kriging method to estimate at some points in Semarang. In  this  methode will compare the value of  the eksperimental semivariogram  with  some theoretical semivariogram models (spherical, eksponensial, and gaussian) to get the best model that will be used in the estimation. In this study, estimate the concentration of Nitrogen Dioxide in the air in a number of village in Semarang. Based on analysis we found the best model is spherical model with Nitrogen Dioxide produces estimates is the highest in Sub Gebangsari and Nitrogen Dioxide lowest in Sub Patemon. Keywords: Ordinary Kriging, Semivariogram, Nitrogen Dioxide.
ANALISIS FAKTOR-FAKTOR TINGKAT KEMISKINAN DI KABUPATEN WONOSOBO DENGAN PENDEKATAN GEOGRAPHICALLY WEIGHTED REGRESSION Permana, Maulana Taufan; Yasin, Hasbi; Rusgiyono, Agus
Jurnal Gaussian Vol 2, No 1 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (649.886 KB) | DOI: 10.14710/j.gauss.v2i1.2744

Abstract

Poverty reduction is the main priority in development strategies in Indonesia, but during this computation is modeled as a function of the poor global regression. That is, the value of the regression coefficient applies to all geographic regions. In reality each region has different characteristics according to the geographical location, therefore Geographically Weighted Regression models are developed (GWR). GWR model is used to consider the element of geography or location as the weighting in estimating the model parameters. In the model GWR model parameter estimation is obtained by using Weighted Least Square (WLS) is to give a different weighting at each location. This study discusses the factors that affect the level of poverty in the District Wonosobo. The results of testing the suitability of the model shows that there is no spatial factors influence the level of poverty in the District Wonosobo. Based on research, there are 3 variables thought to affect the level of household poverty in Wonosobo district, percentage of the number of families that have slums, percentage number of families severely malnourished, percentage of the number of families who have agricultural land. These variables have a similar effect in each district.Keywords: Poverty, Geographically Weighted Regression, Weighted Least Square, Wonosobo
ANALISIS ANTREAN DAN KINERJA SISTEM PELAYANAN GARDU TOL OTOMATIS GERBANG TOL MUKTIHARJO (Studi Kasus: Gardu Tol Otomatis Gerbang Tol Muktiharjo) Erna Fransisca Angela Sihotang; Sugito Sugito; Moch. Abdul Mukid
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (538.082 KB) | DOI: 10.14710/j.gauss.v8i1.26625

Abstract

Queue process is a process related to the arrival of customers in a service facility, waiting in line queue if it cannot be served, get service and finally leaves the facility after being served. Research on the queue process can be seen directly through the queue system at the automatic toll booth Muktiharjo. Queue models and their distribution were obtained using the Sigma Magic program. The model of the vehicle queue system at the Muktiharjo Automatic Toll Gate is (GAMM/ GAMM/ 4): (GD/ ∞/ ∞). Based on the values of the queue system performance measures obtained through the MATLAB GUI program as a whole it can be concluded that the queue of vehicles at the Muktiharjo Automatic Toll Gate has a condition where the average number of vehicles estimated in the system every 15 minutes is 25,5646 vehicles. The average number of vehicles in the queue system every 15 minutes is 24,5639 vehicles. The waiting time in the system is estimated to be around 7,99332 seconds. The estimated waiting time in line is around 7,68042 seconds. The queue system has a busy opportunity of 63.2849% and the remaining 36.7106% is a chance the queue system is not busy. The simulation of the vehicle queue system at the Automatic Toll Gate of Muktiharjo Toll Gate by using ARENA is optimal with the number of service points as many as 4 automatic toll booths. Keywords: Automatic Toll Booth, Queue, Gamma Distribution, Performance Size, Queue Simulation

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