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Jurnal Gaussian

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Published by Universitas Diponegoro

ISSN : - EISSN : 23392541 DOI : -

Core Subject : Education,

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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 693 Documents

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PENGUKURAN KINERJA PORTOFOLIO SAHAM MENGGUNAKAN MODEL BLACK-LITTERMAN BERDASARKAN INDEKS TREYNOR, INDEKS SHARPE, DAN INDEKS JENSEN (Studi Kasus Saham-Saham yang Termasuk dalam Jakarta Islamic Index Periode 2009-2013) Siti Azizah; Sugito Sugito; Alan Prahutama
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The composing of portfolio is one of the way to minimize the risk of investment. Through portfolio, it is expected that some stocks still give return when other stocks are loss. From this composed portfolio, every investor expect appropriate return. The higher the return is better. Black-Litterman Model is the method which optimize the investor’s return by giving difference financial capital proportion for every stocks of portfolio. This method combines both the aspect historical data and the investor view to make new prediction about return of portfolio as the basic to compose the weight model of assets. Investor often compose some portfolio to plan their investment, to compare the performance (capability to produce return and also risk) from any number of portfolio, before evaluating whether the performance of chosen portfolio has been appropriate with the expectation. The measurement of the performance of portfolio is done by using Sharpe, Treynor, and Jensen Indeks. The result of the case study of eleven Jakarta Islamic Indexstocks in the period of 2009-2013 recommend the portfolio with the best perform, whichis optimized which Black-Litterman Model. Based on Sharpe Indeks, the best portfolio consists of SMGR 60,79% and INTP 39,21% of capital allocation. Based on Treynor and Jensen Indeks, the best portfolio consists of SMGR 22,59%, INTP 37,67%, PTBA 1,62%, ANTM 2,69%, ITMG 16,17%, and KLBF 19,26%. Keywords : JII, Portfolio, Black-Litterman Model, Treynor Index, Sharpe Index, Jensen Index.

PENDEKATAN METODE SIX SIGMA-TAGUCHI DALAM MENINGKATKAN KUALITAS PRODUK (Studi Kasus PT. Asaputex Jaya Spinning Mill Tegal) Nesvi Intan Oktajayanti; Mustafid Mustafid; Sudarno Sudarno
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The main factors to achieve business success in the era of globalization is the quality. In the business world, quality control is the key to maintaining customer loyalty. For a company doing quality control is expected to achieve the company's goals, related to the company's revenue. This is the purpose of PT. Asaputex Jaya Spinning Mill Tegal to make efforts to improve the production activities, especially in improving quality by reducing defects. Six Sigma-Taguchi method can be used to improve quality yarns product. From the analysis we found that the control diagram p, data of defects is uncontrolled, so the capability process is still low with capability value is 0,502. So, it need to be improved to enhance product quality yarns. By using the Taguchi method we can know factors and optimal level to improve the quality of the yarn. That Factor and level is TPI with the optimum level that can be used are Level 2 (13,5 rpm), level 1 (383 tpm) for Delivery Speed factor, for the weight of cotton the optimum level is level 1 (2,0 Kw) and factor Grain the optimal level is level 2 (400 Ne). Keywords: Six Sigma Method, Design Experiment of Taguchi, Capability Process

PERHITUNGAN DAN ANALISIS PRODUK DOMESTIK REGIONAL BRUTO (PDRB) KABUPATEN/KOTA BERDASARKAN HARGA KONSTAN (Studi Kasus BPS Kabupaten Kendal) Fitriani Fitriani; Agus Rusgiyono; Triastuti Wuryandari
Jurnal Gaussian Vol 2, No 2 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Gross Regional Domestic Product (GRDP) is technical term that always we heard in the civil government or in the public society. According to Statistics Indonesia, GRDP is total number of added value who producting by effort unit in that domestic area. GRDP is one of economics growth indicator in the domestic area. If GRDP is higher, then people economics prosperity must be high too, and do also that opposite. GRDP contains of 2 methods, that is GRDP at Current Market Prices and GRDP at Constant Prices. In this report will discuss about GRDP at Constant Prices with GRDP the Kendal Regency at 2000 Constant Prices in 2010 for example. Arranging GRDP at Constant Prices has purpose to find out economics condition from year to year by discern the GRDP every year. The methods to arranging GRDP at Constant Prices are revaluasi, ekstrapolasi, and deflasi. After doing the accounting by Statistics Indonesia, we obtainable GRDP the Kendal Regency at Constant Prices in 2010 in million rupiahs is 5.394.079,31. And according the analysis, GRDP from 1983 to 2011 show the linear graph that has model GRDP = -986933 + 220901 (X). This model, can use to forecasting for GRDP the Kendal Regency at Constant Prices over the next years.

ANALISIS KOMODITAS UNGGULAN PERIKANAN BUDIDAYA PROVINSI JAWA TENGAH TAHUN 2012-2016 MENGGUNAKAN METODE LOCATION QUOTIENT DAN SHIFT SHARE Dian Mariana L Manullang; Agus Rusgiyono; Budi Warsito
Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Condition of capture fisheries is currently stagnating, even tended to decline, which is indicated by the decrease of production in some fishery development areas in Indonesia. Aquaculture is one solution that can be done. Central Java Province is a province that has a large aquaculture potential, therefore of course Central Java province has leading commodities that become the sector of regional economic development. This research discusses about the potential location for the development of each leading commodities in Central Java Province as a recommendation related to the centre of fisheries production. Analytical methods in this research are Location Quotient (LQ) dan Shift share. It used to see how big these locations have a potential in the development of aquaculture production and to identify spatial autocorrelation in the amount of aquaculture production using Moran’s index. The analysis of LQ and shift share shows that each district has a different potential in the development of leading commodities production. The value of the Moran’s index obtained equal to -0.1381, that is in the range of -1 <I ≤ 0, indicating that the presence of spatial autocorrelation is negative but small because of near to zero. It can be concluded that there is no similarity of the values between the districts or indicate that amount of aquaculture production among the districts in Central Java are not correlated.Keywords: Leading Commodities, Location Quotient (LQ), Shift Share, Moran’s Index

PENDEKATAN SERVQUAL-LEAN SIX SIGMA MENGGUNAKAN DIAGRAM KONTROL T2 HOTELLING UNTUK MENINGKATKAN KUALITAS PELAYANAN PENDIDIKAN (Studi Kasus di Jurusan Statistika Universitas Diponegoro) Darwati, Lulus; Mustafid, Mustafid; Suparti, Suparti
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Measurement the service of quality has an important role in improving and evaluating the performance of a service process. Measuring the service of quality is not as easy as measuring the goods quality, because the assessment service is subjective. Therefore, ServQual dimension is used as a tool to measure the performance of service from the perspective of service’s users. Lean Six Sigma method is used to improve the performance of the services of quality that focused on the reduction of variations and the increasing of the speed of the process through the elimination of waste that occur in the flowing process. This research aims to implement the integration of ServQual and Lean Six Sigma method by controlling the process using Hotelling T2 control charts on the improvement of the quality of education services. The performance of the education services process overall is indicated by the value of the capabilities and the level of the sigma. The capability value amount 0.8407 and the level of sigma amount 2.748 indicates that the waste percentage in the process of educational services is about 10.6%. The waste of dominant on improving the quality of education services such as lecturer competencies, the status of departement accreditation, the speed in the administrative services, and the refinement of laboratory facilities especially the improvement on the computer facilities.Keywords : ServQual, Hotelling T2 control charts, Process Capability, Lean Six Sigma

ANALISIS KINERJA PORTOFOLIO OPTIMAL DENGAN METODE MEAN-GINI Mega Susilowati; Rita Rahmawati; Alan Prahutama
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Investments in financial assets has a special attraction that investors can form a portfolio. Portfolio is investment which comprised of various stocks from different companies. A common issue is the uncertainty when investors are faced with the need to choose stocks to be formed into a portfolio of his choice. A rational investor, would choose the optimal portfolio. Determination of the optimal portfolio can be performed by various methods, one of which is a method of Mean-Gini. Mean-Gini is the expected value of the portfolio return is the weight density function while the random variable is the average of the shares. Mean-Gini methods used to generate the greatest value of portfolio return expectations with the smallest risk. Mean-Gini does not require the assumption of normality on stock returns. Mean-Gini was first introduced by Shalit and Yitzhaki in 1984. This research uses data of closing prices stocks from January 2008 to December 2015. Measurement of portfolio performance with Mean-Gini performed using the Sharpe index. Based on Sharpe index, the optimal portfolio is second portfolio with three stocks portfolio and the proportion investments are 25.043% for SMGR, 68.148% for UNVR and 6.809% for BMRI. Keywords: Stock, Portfolio, Mean-Gini, Sharpe index.

KUALITAS PELAYANAN PADA BANK JAWA TENGAH (Studi Kasus : Bank Jateng Cabang Tembalang) Yosi Dhyas Monica; Abdul Hoyyi; Moch. Abdul Mukid
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Service attendance quality is superiority level which is be expected and control above its superiority level for satisfying consumen's desire. In this case, there are 5 service quality dimensions. Those are tangible, reliability, responsiveness, assurance, and emphaty. This research study was doing at Bank Jateng, where the respondents are the customer of Bank Jateng. Importance Performance Analysis consist of two components, there are quadrant analysis and discrepancy analysis (gap). Quadran analysis can find out the respond of cusumens against variable which has plotted based on interest and performance level from those variables. While gap analysis is being used for perceiving discrepancy between performance of a variable with the expectation from consumen against its variable. Customer Satisfaction Index (CSI) is used for discovering overall satisfaction level of customers. The T2 hotelling control chart is to know the qualiy controlof two or more related quality characteristics. Result of the research is showing that for quadran analysis, those variables which representing 5 service quality dimensions be located spread in different quadran. For gap analysis, the service perormance of a bank represented by 20 variables who representing 5 service quality dimensions, all of which is still under customers expectation. CSI value aa big as 72,22% which is mean customers satisfaction index is on the satisfaction criteria. On T Hotelling chart is said that the process is not restrained statistically yet because there are 4 points is on the top of control chart

PEMODELAN DEFORESTASI HUTAN LINDUNG DI INDONESIA MENGGUNAKAN MODEL GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION (GTWR) Thea Zulfa Adiningrumh; Alan Prahutama; Rukun Santoso
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Regression analysis is a statistical analysis method that is used to modeling the relationship between dependent variables and independent variables. In the linear regression model only produced parameter estimators are globally, so it’s often called global regression. While to analyze spatial data can be used Geographically Weighted Regression (GWR) method. Geographically and Temporally Weighted Regression (GTWR) is the development of GWR model to handle the instability of a data both from the spatial and temporal sides simultaneously. In this GWR modeling the weight function used is a Gaussian Kernel, which requires the bandwidth value as a distance parameter. Optimum bandwidth can be obtained by minimizing the CV (cross validation) coefficient value. By comparing the R-square, Mean Square Error (MSE) and Akaike Information Criterion (AIC) values in both methods, it is known that modeling the level of deforestation in protected forest areas in Indonesia in 2013 through 2016 uses the GTWR method better than global regression. With the R-square value the GTWR model is 25.1%, the MSE value is 0.7833 and AIC value is 349,6917. While the global regression model has R-square value of 15.8%, MSE value of 0.861 and AIC value of 361,3328. Keywords : GWR, GTWR, Bandwidth, Kernel Gaussian

ANALISIS REGRESI KEGAGALAN PROPORSIONAL DARI COX PADA DATA WAKTU TUNGGU SARJANA DENGAN SENSOR TIPE I (Studi Kasus di Fakultas Sains dan Matematika Universitas Diponegoro) Oka Afranda; Triastuti Wuryandari; Dwi Ispriyanti
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

One of the goals of studying in Higher Education Institutionis to obtain a job as soon as possible. A graduate is not required to be an unemployed. In Indonesia, the average period of waiting time for undergraduate (S1) to get the first job is 0 (zero) to 9 (nine) months. There are several factors have influenced the length of an undergraduate to get a job. They are Grade Point Average (GPA), Length of Study, etc. Therefore, it is important to know the factors influencing the waiting time of undergraduates to get a job. One method that can be used is the analysis of survival. Survival analysis is the analysis of survival time data from the initial time of the study until certain events occur. One method of survival analysis is Cox Proportional Hazard Regression. It is used to determine the relationship between one or more independent variables and the dependent variable. Cases raised in this study were the factors influencing the waiting time of graduates of the Faculty of Science and Mathematics, University of Diponegoro by using Type I data censoring. The conclusions state that the factors influencing the waiting time of graduates are Organization, Department, and Gender.Keywords: Waiting time of undergraduate, survival analysis, Cox Proportional Hazard, Regression, University of Diponegoro.

ANALISIS INFLASI KOTA SEMARANG MENGGUNAKAN METODE REGRESI NON PARAMETRIK B-SPLINE Alvita Rachma Devi; Moch. Abdul Mukid; Hasbi Yasin
Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Inflation is an important consideration for investors to invest in an area. An accurate prediction of inflation is required for investors in conducting a careful planning. One of the method to find the predicted value of inflation is by using B-Spline regression, a nonparametric regression which is not depend on certain assumptions, thus providing greater flexibility. The optimal B-Spline models rely on the optimal knots that has a minimum Generalized Cross Validation (GCV). By using Semarang year-on-year inflation data from January 2008 - August 2013, the optimal B-spline models in this study are on the order of 2 ( linear ) with 2 knots, that is 5,99 and 6,09. Prediction of Semarang inflation in 2014 fluctuated around the number five and six and inflation in the end of 2014 is 6,286394%.

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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