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<![CDATA[KOMPUTASI METODE EXPONENTIALLY WEIGHTED MOVING AVERAGE UNTUK PENGENDALIAN KUALITAS PROSES PRODUKSIMENGGUNAKAN GUI MATLAB (STUDI KASUS : PT Djarum Kudus SKT Brak Megawon III) ]]>
<![CDATA[Iyan Antono]]>;
<![CDATA[Rukun Santoso]]>;
<![CDATA[Yuciana Wilandari]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i4.14724
<![CDATA[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]]>
<![CDATA[IMPLEMENTASI METODE RESPONSE SURFACE SEBAGAI UPAYA OPTIMALISASI JUMLAH BINTIL AKAR PADA TANAMAN KEDELAI ]]>
<![CDATA[Muchammad Aziz Chusen]]>;
<![CDATA[Rukun Santoso]]>;
<![CDATA[Rita Rahmawati]]>
<![CDATA[ Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v6i2.16949
<![CDATA[Response surface method is a set of statistics and mathematical techniques, useful to analyze the issue of multiple independent variables that affect to the dependent variable of response, and aim to optimize the response. The existence of response surface method is able to assist researchers in conducting improvised to get optimum results accurately and efficiently. In this experiment using the data factorial CRD (completely randomized design) with two factors and three levels. Two factors were tested consists of the elements cobalt and ferrum, with the level in the form of element concentrations with each ie cobalt (0 mg/L, 0.1 mg/L and 0.2 mg/L), and ferrum (0 mg/L, 1mg/L and 2 mg / L). Variable response is the number of nodules roots of soybean crops. After testing the response surface method produced a linear model of the first order Jumlah Bintil Akar Kedelai = 10.3 + 10.2 Ferum + 238.3 Kobalt – 1340.1 Kobalt 2 – 93 Ferum*(Kobalt 2). with the value of concentration at ferum = 2 mg/L and cobalt = 0.1 mg/L is able to generate growth in the number of nodules optimum soybean crop in these experiments. Keywords: Factorial design, Response surface ]]>
<![CDATA[Penerapan Text Mining untuk Melakukan Clustering Data Tweet Akun Blibli Pada Media Sosial Twitter Menggunakan K-Means Clustering ]]>
<![CDATA[Syiva Multi Fani]]>;
<![CDATA[Rukun Santoso]]>;
<![CDATA[Suparti Suparti]]>
<![CDATA[ Jurnal Gaussian Vol 10, No 4 (2021): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v10i4.30409
<![CDATA[Social media is computer-based technology that facilitates the sharing of ideas, thoughts, and information through the building of virtual networks and communities. Twitter is one of the most popular social media in Indonesia which has 78 million users. Businesses rely heavily on Twitter for advertising. Businesses can use these types of tweet content as a means of advertising to Twitter users by Knowing the types of tweet content that are mostly retweeted by their followers . In this study, the application of Text Mining to perform clustering using the K-means clustering method with the best number of clusters obtained from the Silhouette Coefficient method on the @bliblidotcom Twitter tweet data to determine the types of tweet content that are mostly retweeted by @bliblidotcom followers. Tweets with the most retweets and favorites are discount offers and flash sales, so Blibli Indonesia could use this kind of tweet to conduct advertising on social media Twitter because the prize quiz tweets are liked by the @bliblidotcom Twitter account followers.]]>
<![CDATA[PEMODELAN REGRESI SEMIPARAMETRIK DENGAN PENDEKATAN DERET FOURIER (Studi Kasus: Pengaruh Indeks Dow Jones dan BI Rate Terhadap Indeks Harga Saham Gabungan ]]>
<![CDATA[Laili Rahma Khairunnisa]]>;
<![CDATA[Alan Prahutama]]>;
<![CDATA[Rukun Santoso]]>
<![CDATA[ Jurnal Gaussian Vol 9, No 1 (2020): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v9i1.27523
<![CDATA[The Composite Stock Price Index (CSPI) is a composite index all of types of shares listed on the stock exchange and their movements indicate conditions that occur in the capital market. CSPI is influenced by macroeconomic factors and foreign exchange index. Dow Jones Industrial Average has a linear relationship with CSPI and BI Rate has a repeated relationship with CSPI, so the method is used semiparametric regression with the Fourier series approach. Estimators in semiparametric regression with Fourier series approach were obtained by the Ordinary Least Square (OLS) method. This study uses monthly data which is divided into in sample data and out sample data. Semiparametric regression modelling with Fourier series approach is done by determining the optimal K value which results in a minimum General Cross Validation (GCV) value. In this study, semiparametric regression model with Fourier series approach formed by the optimal K value is 13 and GCV is 2826122. The results of the evaluation of the accuracy of the model performance and forecasting obtained the coefficient of determination is 0,9226, Mean Absolute Percentage Error (MAPE) data in sample 3,8154% and data out sample is 8,4782% which shows that the model obtained has a very accurate performance.Keywords: Composite Stock Price Index (CSPI), Semiparametric Regression, Fourier Series, OLS, GCV]]>
<![CDATA[MODEL ASURANSI KENDARAAN BERMOTOR MENGGUNAKAN DISTRIBUSI MIXED POISSON ]]>
<![CDATA[Tina Diningrum]]>;
<![CDATA[Yuciana Wilandari]]>;
<![CDATA[Rukun Santoso]]>
<![CDATA[ Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v1i1.916
<![CDATA[Motor vehicle insurance is a form of protection of motor vehicles owned by the insured. One of the activities in insurance companies is claim. Claim is risk of loss claim is paid by the insurance company to the insured. Analysis of motor vehicle insurance claims typically uses poisson distribution approach. Nevertheless in many cases of motor vehicle insurance claim, the value of variance greater than the mean value. In this case overdispersed has been going on the assumption poisson distribution. If the poisson distribution continued to be used when going overdispersed, so the poisson distribution is inefficient because it affects the error standard. To solve the problem can be used mixed Poisson distribution. This final project used two mixed Poisson distribution which is a mixture of gamma poison known as negative binomial distribution and poisson-exponential mixture known as a geometric distribution. Carried out on the data motor vehicle claim in PT. Jasa Asuransi Indonesia, Semarang branch year 2010 to 2011 it is estimated that of the 100 vehicle type Car policyholders aged <1 year will be 2 claims per year.]]>
<![CDATA[ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI DIVIDEND PAYOUT RATIO (DPR) MENGGUNAKAN ANALISIS REGRESI LINIER DENGAN BOOTSTRAP (Studi Kasus: PT. Unilever Indonesia, Tbk Tahun 1999-2015) ]]>
<![CDATA[Lia Safitri]]>;
<![CDATA[Di Asih I Maruddani]]>;
<![CDATA[Rukun Santoso]]>
<![CDATA[ Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v6i3.19342
<![CDATA[The amount of dividend paid by the company to shareholders or dividend payout ratio is the main factor that investors pay attention to invest their capital into the company. Investors want a relative dividend, even increasing over time. Factors influencing the level of dividend payout ratio are Return on Equity (ROE), stock price, liquidity ratio, and leverage level. Based on this, multiple linear regression analysis with bootstrap is used. The purpose of this study is to analyze the factors that significantly affect the dividend payout ratio based on the best model used to predict the value of dividend payout ratio for the next period. The bootstrap method is used to overcome the occurrence of multicollinearity among independent variables due to the small sample size. Based on the simulation done with software R using PT data. Unilever Indonesia, Tbk from 1999-2015 obtained best model is bootstrap residual with 2 significant independent variable are ROE and level of leverage. Based on the best model, the predicted value of dividend payout ratio of 2016 is 41.60196 with percentage error of 7.0812%. Keywords : Regression analysis, Bootstrap, Dividend Payout Ratio, ROE, leverage ]]>
<![CDATA[IMPLEMENTASI SUBSET AUTOREGRESSIVE MENGGUNAKAN PAKET FITAR ]]>
<![CDATA[Tomi Ardi]]>;
<![CDATA[Rukun Santoso]]>;
<![CDATA[Alan Prahutama]]>
<![CDATA[ Jurnal Gaussian Vol 6, No 4 (2017): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v6i4.30385
<![CDATA[Time series data analysis is one of the important points in statistics that is a time-dependent analysis. The commonly used model for time series data is ARIMA (Autoregressive Integrated Moving Average) or often also called the Box-Jenkins time series method. A model of ARIMA used in time clock data forecasting is the AR subset (autoregressive). The AR subset model is suitable for a long time series with a more than 5th order lag. The statistical software used is the R. time series AR subset approach on R using the FitAR package. The main function of the FitAR package is SelectModel and FitAR. SelectModel function to get the model automatically while FitAR is used to determine the temporary suspect model. Data used in the form of dataset contained in package FitAR that is SeriesA. The SeriesA data is data about the chemical concentration process observed every 2 hours for 17 days. SeriesA is processed using FitAR package so that the best model is AR [1,2,7].Keywords : Time Series, Time Series Non-stasioner, Subset AR, FitAR Package]]>
<![CDATA[ANALISIS KECENDERUNGAN LAPORAN MASYARAKAT PADA “LAPORGUB..!” PROVINSI JAWA TENGAH MENGGUNAKAN TEXT MINING DENGAN FUZZY C-MEANS CLUSTERING ]]>
<![CDATA[Ratna Kurniasari]]>;
<![CDATA[Rukun Santoso]]>;
<![CDATA[Alan Prahutama]]>
<![CDATA[ Jurnal Gaussian Vol 10, No 4 (2021): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v10i4.33101
<![CDATA[Effective communication between the government and society is essential to achieve good governance. The government makes an effort to provide a means of public complaints through an online aspiration and complaint service called “LaporGub..!”. To group incoming reports easier, the topic of the report is searched by using clustering. Text Mining is used to convert text data into numeric data so that it can be processed further. Clustering is classified as soft clustering (fuzzy) and hard clustering. Hard clustering will divide data into clusters strictly without any overlapping membership with other clusters. Soft clustering can enter data into several clusters with a certain degree of membership value. Different membership values make fuzzy grouping have more natural results than hard clustering because objects at the boundary between several classes are not forced to fully fit into one class but each object is assigned a degree of membership. Fuzzy c-means has an advantage in terms of having a more precise placement of the cluster center compared to other cluster methods, by improving the cluster center repeatedly. The formation of the best number of clusters is seen based on the maximum silhouette coefficient. Wordcloud is used to determine the dominant topic in each cluster. Word cloud is a form of text data visualization. The results show that the maximum silhouette coefficient value for fuzzy c-means clustering is shown by the three clusters. The first cluster produces a word cloud regarding road conditions as many as 449 reports, the second cluster produces a word cloud regarding covid assistance as many as 964 reports, and the third cluster produces a word cloud regarding farmers fertilizers as many as 176 reports. The topic of the report regarding covid assistance is the cluster with the most number of members. ]]>
<![CDATA[PENERAPAN STRUCTURAL EQUATION MODELLING (SEM) UNTUK MENGANALISIS FAKTOR – FAKTOR YANG MEMPENGARUHI KINERJA BISNIS (STUDI KASUS KAFE DI KECAMATAN TEMBALANG DAN KECAMATAN BANYUMANIK PADA JANUARI 2019) ]]>
<![CDATA[Ade Irma Pramudita]]>;
<![CDATA[Tatik Widiharih]]>;
<![CDATA[Rukun Santoso]]>
<![CDATA[ Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v9i2.27814
<![CDATA[This research is done to examine the effect of quality of service and product attractiveness toward business strategies based on service in order to improving business performance. The sample of this study were Cafe owners in Tembalang Subdistrict and Banyumanik Subdistrict, total are 116 respondents. In this Final Project, the processing of Structural Equation Modeling (SEM) is AMOS software. The results of the analysis show that service quality has a positive effect on business strategies based on service to improving business performance. The most significant factor that affecting business performance is quality of service. Quality of service is important in the performance of a café business. Cafe owners must always pay attention to the quality of café service to customers, because the quality of service is the main consideration for customers to visit cafes.]]>
<![CDATA[PENGUKURAN PROBABILITAS KEBANGKRUTAN OBLIGASI KORPORASI DENGAN SUKU BUNGA COX INGERSOLL ROSS MODEL MERTON (Studi Kasus Obligasi PT Indosat, Tbk) ]]>
<![CDATA[Muhammad Akhir Siregar]]>;
<![CDATA[Mustafid Mustafid]]>;
<![CDATA[Rukun Santoso]]>
<![CDATA[ Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v7i2.26652
<![CDATA[Nowadays bonds become one of the many securities products that are being prefered by investors. Observing the level of the company's rating which good enough or in the criteria of investment grade can’t be a handle of investors. Investing in long-term period investors should understand the risks to be faced, one of investment credit risk on bonds is default risk, this risk is related to the possibility that the issuer fails to fulfill its obligations to the investor in due date. The measurement of the probability of default failure by the structural method approach introduced first by Black-Scholes (1973) than developed by Merton (1974). In Bankruptcy model, merton’s model assumed the company get default (bankrupt) when the company can’t pay the coupon or face value in the due date. Interest rates on the Merton model assumed to be constant values replaced by Cox Ingersoll Ross (CIR) rates. The CIR rate is the fluctuating interest rate in each period and the change is a stochastic process. The empirical study was conducted on PT Indosat, Tbk's bonds issued in 2017 with a face value of 511 Billion in payment of obligations by the issuer for 10 years. Based on simulation results done with R software obtained probability of default value equal to 7,416132E-215 Indicates that PT Indosat Tbk is deemed to be able to fulfill its obligation payment at the end of the bond maturity in 2027. Keywords: Bond, CIR Rate, Merton Model, Ekuity, Probability of default]]>
