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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 5, No 4 (2016): Jurnal Gaussian" : 23 Documents clear

MODEL REGRESI DATA PANEL SIMULTAN DENGAN VARIABEL INDEKS HARGA YANG DITERIMA DAN YANG DIBAYAR PETANI Bayyina Zidni Falah; Mustafid Mustafid; Sudarno Sudarno
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Interdependent relationship (simultaneity) between endogenous variables, that’s Farmers Recieved and Paid Price Index, can’t be modeled in a single equation, but there are two equations in a system of simultaneous equations. Each of these equations can’t be estimated separately without entering information from other equations. The purpose of this research is modelling panel data regression simultaneously. The method that’s used is Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (REM) with estimation technique is Two Stages Least Square (2SLS). The modelling is done by a panel data consisting of 32 provinces in 2013, 2014, and 2015. Based on the results of the Chow test, Hausman test, F statistic, and the value of R2, the result is that REM is the most suitable model to model the simultaneity of the panel data. REM has different intercepts in each province. F statistic value for the first equation of 152,658 with a significance of 0.000, and R2 value of 83,2%. For the second equation, statistics F value of 44396,16 with siginifikansi 0,000, and R2 value of 99.9%. From the results of this modelling, the model that’s created can express the interdependent relationship between endogenous variables as well the diversity of variables between provinces.Keywords: Panel data, CEM, FEM, REM, Farmers Recieved Price Index,Farmers Paid Price Index

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

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

PEMILIHAN PENGRAJIN TERBAIK DENGAN METODE ELECTRE DAN TOPSIS MENGGUNAKAN GUI MATLAB (STUDI KASUS : PT. Asaputex Jaya, Tegal) Hafii Risalam; Rita Rahmawati; Suparti Suparti
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Company is technical unity that aims to produce goods services. One of determinants of succesful company is its human resources or known as employees. PT. Asaputex Jaya is one of company that enganged in the manufacture of sarong. Poor quality of human resources, especially on the production is still an obstacle for PT. Asaputex Jaya. Therefore selection of the best craftsmen need to be done so that production process doesn’t meet any probelms. This research was conducted to determine top craftsmen in sarong production on PT. Asaputex Jaya, and also used for PT. Asaputex Jaya’s human resources management interests. ELECTRE is based on rankings concept through pair comparison between alternatives on the suitable criteria. While TOPSIS can determine the value of preference for each alternative, the concept is simple and easy to understand. There are 8 criteria in the selection of top craftsmen: design, fabrics assembly, merger with filler material, manufacture of sarong, punctuality, statutes of size, tailoring results, and neatness or stitching cleanliness. Through the TOPSIS method selected 10 top craftsmen: 5th, 14th, 15th, 9th, 3rd, 13th,6th, 20th, 18th, and 10th , which then only one top craftsman will be chosen using the ELECTRE method. This study also produced a GUI Matlab programming application that can help users in performing data processing using TOPSIS and ELCTRE to select the best craftsmen on PT. Asaputex Jaya Keyword: SDM, TOPSIS, ELECTRE, GUI Matlab, top craftsmen

GEOGRAPHICALLY WEIGHTED REGRESSION PRINCIPAL COMPONENT ANALYSIS (GWRPCA) PADA PEMODELAN PENDAPATAN ASLI DAERAH DI JAWA TENGAH Nurmalita Sari; Hasbi Yasin; Alan Prahutama
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Linear Regression Analysis is a method for modeling the relation between a response variable with two or more independent variables. Geographically Weighted Regression (GWR) is a development of the regression model where each observation location has different regression parameter values because of the effects of spatial heterogenity. Regression Principal Component Analysis (PCA) is a combination of PCA and are used to remove the effect of multicolinearity in regression. Geographically Weighted Regression Principal Component Analysis (GWRPCA) is a combination of PCA and GWR if spatial heterogenity and local multicolinearity occured. Estimation parameters for the GWR and GWRPCA using Weighted Least Square (WLS). Weighting use fixed gaussian kernel function through selection of the optimum bandwidth is 0,08321242 with minimum Cross Validation (CV) is 3,009035. There are some variables in PCA that affect locally-generated revenue in Central Java on 2012 and 2013, which can be represented by PC1 that explained the total variance data about 71,4%. GWRPCA is a better model for modeling locally-generated revenue for the districts and cities in Central Java than RPCA because it has the the smallest Akaike Information Criterion (AIC) and the largest R2. Keywords : Spatial Heterogenity, Local Multicolinearity, Principal Component Analysis, Geographically Weighted Regression Principal Component Analysis.

PERAMALAN PASANG SURUT AIR LAUT DI PULAU JAWA MENGGUNAKAN MODEL GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) (Studi Kasus : Ketinggian Pasang Surut Air Laut di Stasiun Pasang Surut Jakarta, Cirebon, Semarang dan Surabaya) Chyntia Arum Widyastusti; Abdul Hoyyi; Rita Rahmawati
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In daily life is often found time series data contains not only connection among the events in previous times, but also has a relationship between one location to another. Data with time series and location linkage is called space-time data. Generalized Space Time Autoregressive (GSTAR) model is one of the commonest used to make model and forecast space-time data. The purposes of this research are to get the best GSTAR model and the forecasting results for the data ocean tide heights at four stations of Java island, those are Stations of Jakarta, Cirebon, Semarang and Surabaya. The best model obtained is GSTAR(1;1)-I(1) which is using cross correlation normalization weight because its residuals fulfill white noise assumption with the smallest value of MAPE and RMSE. The best GSTAR model explains that the elevation ocean tide data in Stations of Cirebon and Semarang is only influenced by the earlier times, and not influenced by other locations but can affect the height of the tide at other locations. As for the elevation ocean tide data stations of Jakarta and Surabaya are influence each other. Keywords: GSTAR, Space-Time, Ocean Tide, MAPE and RMSE.

ANALISIS KECENDERUNGAN INFORMASI DENGAN MENGGUNAKAN METODE TEXT MINING (Studi Kasus: Akun twitter @detikcom) Syaifudin Karyadi; Hasbi Yasin; Moch. Abdul Mukid
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The internet is an extraordinary phenomenon. Starting from a military experiment in the United States, the internet has evolved into a 'need' for more than tens of millions of people worldwide. The number of internet users is large and growing, has been creating internet culture. One of the fast growing social media twitter. Twitter is a microblogging service that stores text database called tweets. To make it easier to obtain information that is dominant discussed, then sought the topic of twitter tweet using clustering. In this research, grouping 500 tweets from twitter account @detikcom using k-means clustering. The results of this study indicate that the maximum index Dunn, the best grouping K-means clustering to obtain the dominant topic as many as three clusters, namely the government, Jakarta, and politics.Keywords: text mining, clustering,, k-means , dunn index, and twitter.

PENERAPAN METODE TAGUCHI UNTUK KASUS MULTIRESPON MENGGUNAKAN PENDEKATAN GREY RELATIONAL ANALYSIS DAN PRINCIPAL COMPONENT ANALYSIS (Studi Kasus Proses Freis Komposit GFRP) Annisa Ayu Wulandari; Triastuti Wuryandari; Dwi Ispriyanti
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Taguchi method is a method for quality control of product by off line. Taguchi method usually used to solve optimization problem with single respon. Multirespon case was done by using Grey Relational Analyisis (GRA) and Principal Component Analysis (PCA). With GRA method is obtained many Grey Relational Grade value. For weight is estimated using PCA. The case study use freis process GFRP composite with characteristic smaller is better. From the research is obtained combination in optimal canditions for factor fiber orientation angle at 150, helix angle at 250, and feed rate at 0,04 mm/rev. While the respon that observed are surface roughness, machine force, and delamination factor. The value of contribution percentage for each factor is 69,596% for fiber orientation angle, 9,768% for helix angle and 11,9841% for feed rate..Keywords : Multirespon Optimization, Taguchi Method, Grey Relational Analysis, Principal Component Analysis, Freis Process GFRP Composite

KOMPUTASI METODE EXPONENTIALLY WEIGHTED MOVING AVERAGE UNTUK PENGENDALIAN KUALITAS PROSES PRODUKSIMENGGUNAKAN GUI MATLAB (STUDI KASUS : PT Djarum Kudus SKT Brak Megawon III) Iyan Antono; Rukun Santoso; Yuciana Wilandari
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Control chart is one of tools for quality control of production. control chart is one of tool that can be used to control the quality of production for variable data such as weight of product. However, there is a weakness of control chart, which is sensitivless in detecting small shift of the mean process. Exponentially Weighted Moving Average (EWMA) control chart is one of the quality control tool that can improve the weakness of control chart. EWMA control chart has a weight smoothing parameter (λ) which makes EWMA control chart more sensitive in detecting small shifts the process mean. Each production data will be weighted and past production data will be affected by present production data. EWMA control chart will be used to make a control chart by weight of cigarette data in Brak Megawon III PT Djarum Kudus. In this study, will be established to assist in the GUI Matlab computational EWMA methods chart controller to control the quality of production at PT Djarum Kudus.In this study showed that the most optimum weight refiner which is at a value of 0.6.Keyword : EWMA, Smoothing weight (λ), GUI, Weight of cigarette

PENDEKATAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION UNTUK PEMODELAN PERTUMBUHAN EKONOMI MENURUT KABUPATEN/KOTA DI JAWA TENGAH Pratama Ganang Widayaka; Mustafid Mustafid; Rita Rahmawati
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Global regression models with a multitude of residual variance in each region causing non-homoskedastisitas assumptions are not met. The diversity of the geographic location factors causing spatial heterogeneity. Geographically Weighted Regression (GWR) is a development of linear regression by involving diverse factors geographical location, so that the parameters generated will be local. GWR model is not able to model the combination of local and global influences in a model. So the purpose of forming a GWR Mixed models are able to establish a model GWR with local and global influences simultaneously. GWR Mixed Model is used to estimate the model Gross Regional Domestic Product (GRDP). As independent variables that influence is revenue (PAD/X1), a variable amount of labor (JAK/X2), the human development index (HDI/X3), unemployment rate (TPT/X4) and the regional minimum wage (UMR/X5). Mixed GWR model the variables that are local and which are global variables. Methods for estimating model parameters MGWR using Weighted Least Square (WLS). Weights obtained the appropriate model to estimate the optimal bandwidth by using the reference method Cross Validation (CV) is a minumum. MGWR models with adaptive exponential kernel function weighting on Gross Domestic Product in the districts / cities in Central Java to produce variable JAK, IPM and TPT have the nature of the locality an area that is significant to the later model PAD have a global nature that sigbifikan against the model. To mengengetahui error rate value model is used Akaike Information Criterion (AIC). Keywords: Akaike Information Criterion, Bandwidth Cross Validation, Fungsi Kernel Gaussian, Mixed Geographically Weighted Regression, Weighted Least Square.

PERBANDINGAN MODEL ARCH/GARCH MODEL ARIMA DAN MODEL FUNGSI TRANSFER (Studi Kasus Indeks Harga Saham Gabngan dan Harga Minyak Mentah Dunia Tahun 2013 sampai 2015) Fakhriyana, Deby; Hoyyi, Abdul; Widiharih, Tatik
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Indonesian Composite Index is a value that used to measure the combined performance of shares listed in stock market. Price of crude oil is one of the factors that affect Indonesian Composite Index. If the prices of crude oil is increasing, it will be responsed by Indonesian goverment directly with also increasing the fuel prices, that will have an impact on Indonesian Composite Index. ARIMA and transfer function are methods of modeling time series data and it have assumption that the residual models have to be homogen. To overcome violations of those assumption, this study continue to modelling ARCH/GARCH with ARIMA and transfer function approach. The data used in this study are daily of Indonesian Composite Index and West Texas Intermediate (WTI) crude oil prices data from 2013 to 2015. This study gained two models, the first is ARIMA (1,1,[3]) which variance model of ARCH(1), it’s AIC value is equal to 7707,4287. The second is transfer fuction model (1,0,0) which noise model ARMA(0,[1,3) as well as variance model ARCH(1), it’s AIC value equal to 7689,18984. The best model is the one that has smallest AIC value. From this study can be concluded that the best of ARCH/GARCH model is ARCH/GARCH model with transfer function approach. Keywords : Indonesian Composite Index, crude oil prices, ARIMA, transfer function, ARCH/GARCH

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Jawa tengah

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