Garuda - Garba Rujukan Digital

Article Per Year (5 Year)

All

Jurnal Gaussian

gaussian
Website

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 693 Documents

1 2 3 4 5

OPTIMALISASI PORTOFOLIO SAHAM MENGGUNAKAN METODE MEAN ABSOLUTE DEVIATION DAN SINGLE INDEX MODEL PADA SAHAM INDEKS LQ-45 Diah Wulandari; Dwi Ispriyanti; Abdul Hoyyi
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Stock investment is the planting of money in a securities that indicates the ownership of a company in order to provide benefits in the future. In obtaining optimal results from stock investments, investors are expected to create a series of portfolios. The portfolio will help investors in allocating some funds in different types of investments in order to achieve optimal profitability. For selection of optimal stocks representing LQ-45 Index, used 2 methods of Mean Absolute Deviation (MAD) method and Single Index Model (SIM) method. In MAD method, 5 best stocks are BBCA with weight 23%, INDF 8%, KLBF 23%, TLKM 23%, and UNVR 23%. While the SIM method of candidate portfolio obtained is AKRA with weight 15,459%, BBCA 48,193%, BBNI 5,028%,KLBF 0,258% and TLKM 31,062%. Portfolio performance meter is used by sharpe ratio. The value of sharpe ratio is 0,36754 for optimal portfolio using MAD method and 0,40782 for optimal portfolio using SIM method, this means that optimal portfolio using SIM method has better performance than MAD. Keywords: Investment, Portfolio, Index LQ-45, Mean Absolute Deviation, Single Index Model, Sharpe Ratio

ANALISIS ANTRIAN ANGKUTAN PENYEBERANGAN PELABUHAN MERAK Ariyo Kurniawan; Sugito Sugito; Yuciana Wilandari
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Marine transportation has an important role on economy and migration from one island to another island. Port is a gateway to enter an area and connecting infrastructure between islands. Merak port as the connector of traffic lanes between Java’s island and Sumatra’s island with Ro-Ro ship. Ro-Ro ship is marine transportation that can load a vehicle rolling on and rolling off the ship with its auto-movement (Roll on Roll off). As a service provider of the Ro-Ro ships, the port of Merak trying to serve Ro-Ro ship as good as possible. Measurement of performance system can be analyzed with direct research in the port. The research is being done by observation and recording of the Ro-Ro ships at the pier port of Merak. Based on the results of the analysis, queue model at the port of Merak is (G/G/5) :(GD/∞/∞). Queuing system simulation and ship docking’s cost analysis can be a reference for the port in optimizing management performance the port of Merak crossing. Keywords: supplemental cost, defined benefit plans, accrued benefit cost.

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

IDENTIFIKASI BREAKPOINT DAN PEMODELAN AUTOREGRESSIVE STRUCTURAL CHANGE PADA DATA RUNTUN WAKTU (Studi Kasus Indeks Harga Konsumen Umum Kota Semarang Tahun 1994 – 2010) Mamuroh Mamuroh; Sudarno Sudarno; Hasbi Yasin
Jurnal Gaussian Vol 3, No 1 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Perubahan Indeks Harga Konsumen (IHK) merupakan indikator ekonomi makro yang cukup penting untuk memberikan gambaran tentang laju inflasi suatu daerah/wilayah serta pola konsumsi masyarakat. IHK Umum Kota Semarang dalam kurun waktu tahun 1994-2010 terlihat mengalami kenaikan terus menerus. Plot data menunjukkan IHK bergerak naik perlahan sebelum bulan Januari 1998 dan setelahnya IHK meningkat secara curam. Untuk mengetahui apakah dalam kurun waktu tersebut terdapat perubahan struktur pola data dan untuk mengetahui titik-titik patah (breakpoints / titik perubahan struktur) yang terjadi pada IHK maka perlu dilakukan uji perubahan struktur, hal ini dilakukan dengan pendekatan autoregressive structural change. Hasil penelitian menunjukkan terjadi perubahan struktur dengan titik patah pada t=47 yaitu Januari 1998 bertepatan dengan krisis moneter 1998 dan t=79 yaitu September 2000 bertepatan dengan kenaikan tarif angkutan per 1 September 2000, sehingga data memiliki 3 segmen model. Metode ini sesuai untuk mengidentifikasi titik-titik patah IHK serta dapat digunakan untuk memodelkan IHK Umum Kota Semarang tahun 1994-2010.

Analisis Kesehatan Bank Menggunakan Local Mean K-Nearest Neighbor dan Multi Local Means K-Harmonic Nearest Neighbor Alwi Assegaf; Moch. Abdul Mukid; Abdul Hoyyi
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The classification method continues to develop in order to get more accurate classification results than before. The purpose of the research is comparing the two k-Nearest Neighbor (KNN) methods that have been developed, namely the Local Mean k-Nearest Neighbor (LMKNN) and Multi Local Means k-Harmonic Nearest Neighbor (MLM-KHNN) by taking a case study of listed bank financial statements and financial statements complete recorded at Bank Indonesia in 2017. LMKNN is a method that aims to improve classification performance and reduce the influence of outliers, and MLM-KHNN is a method that aims to reduce sensitivity to a single value. This study uses seven indicators to measure the soundness of a bank, including the Capital Adequacy Ratio, Non Performing Loans, Loan to Deposit Ratio, Return on Assets, Return on Equity, Net Interest Margin, and Operating Expenses on Operational Income with a classification of bank health status is very good (class 1), good (class 2), quite good (class 3) and poor (class 4). The measure of the accuracy of the classification results used is the Apparent Error Rate (APER). The best classification results of the LMKNN method are in the proportion of 80% training data and 20% test data with k=7 which produces the smallest APER 0,0556 and an accuracy of 94,44%, while the best classification results of the MLM-KHNN method are in the proportion of 80% training data and 20% test data with k=3 which produces the smallest APER 0,1667 and an accuracy of 83,33%. Based on APER calculation shows that the LMKNN method is better than MLM-KHNN in classifying the health status of banks in Indonesia.Keywords: Classification, Local Mean k-Nearest Neighbor (LMKNN), Multi Local Means k-Harmonic Nearest Neighbor (MLM-KHNN), Measure of accuracy of classification

PENGUKURAN RISIKO KREDIT HARGA OBLIGASI DENGAN PENDEKATAN MODEL STRUKTURAL KMV MERTON Anang Asdriargo; Di Asih I Maruddani; Abdul Hoyyi
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Obligasi merupakan salah satu instrumen keuangan yang merupakan suatu pernyataan utang dari penerbit obligasi kepada pemegang obligasi beserta janji untuk membayar kembali pokok utang beserta bunganya pada saat jatuh tempo. Pada saat melakukan investasi obligasi, selain mendapatkan keuntungan juga memberikan potensi risiko investasi. Salah satu risiko yang dapat terjadi adalah risiko kredit. Risiko kredit adalah potensi risiko yang akan timbul bagi investor apabila penerbit obligasi tidak bisa melakukan kewajiban atas pembayaran bunga atau kewajiban pokok pada saat jatuh tempo. Untuk memodelkan risiko kredit salah satu pendekatan utamanya adalah Model Struktural. Model struktural mengasumsikan kebangkrutan perusahaan terjadi ketika nilai aset perusahaan berada di bawah nilai obligasi perusahaan. Model Merton dimodifikasi dan dikembangkan oleh KMV (sebuah perusahaan konsultan keuangan di Amerika Serikat) yang dikenal dengan KMV Model. Studi empiris dilakukan pada data aset PT Bank Daerah Khusus Ibukota Tbk dan PT Bank Lampung Tbk. Berdasarkan output pemrograman R, untuk PT Bank Daerah khusus Ibukota Tbk diperoleh nilai probabilitas kegagalan sebesar 9,412932E-24% dan nilai Distance to Default adalah 10,4262. Sedangkan untuk PT Bank Lampung Tbk diperoleh nilai probabilitas kegagalan sebesar 3.801958E-07% dan nilai Distance to Default adalah 5.777011

PEMODELAN RETURN PORTOFOLIO SAHAM MENGGUNAKAN METODE GARCH ASIMETRIS Muhammad Arifin; Tarno Tarno; Budi Warsito
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Investment in stocks is an alternative for investors and companies to obtain external funding sources. In the investment world there is a strong relationship between risk and return (profit), if the risk is high then return will also be high. Risks can be minimized by performing stock portfolio. Stock is the time series data in the financial sector, which usually has a tendency to fluctuate rapidly from time to time so that variance of error is not constant. Time series model in accordance with these condition is Generalized Autoregressive Conditional Heteroscedasticity (GARCH). This research will apply asymmetric GARCH covering Exponential GARCH (EGARCH), Threshold GARCH (TGARCH), and Autoregressive Power ARCH (APARCH) in stock data Indocement Tunggal Tbk (INTP), Astra International Tbk (ASII), and Adaro Energy Tbk (ADRO) commencing from the date of March 1, 2013 until February 29, 2016 during an active day (Monday to Friday). The purpose of this research is to predict the value of the volatility of a portfolio of three assets stocks. The best models used for forecasting volatility in asset stocks which have asymmetric effect is ARIMA ([13],0,[2,3]) EGARCH (1,1) on a single asset data INTP, ARIMA ([2],0,[2,3]) EGARCH (1,1) on the 2 asset portfolio data ASII INTP, and ARIMA ([3],0,[2]) EGARCH (1,1) on the 3 asset portfolio data INTP-ASII-ADRO.Keywords: Stocks, Portfolio, Return, Volatility, Asymmetric GARCH.

PERBANDINGAN METODE VARIANCE COVARIANCE DAN HISTORICAL SIMULATION UNTUK MENGUKUR RISIKO INVESTASI REKSA DANA Bayu Heryadi Wicaksono; Yuciana Wilandari; Agus Rusgiyono
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

One of the instruments of financial assets are investments in mutual funds. Every day of the total fair value of the assets in the mutual fund is always changing because the market value of each type of asset that is changing. Thus causing mutual fund has a risk. It is necessary for the measurement of risk in mutual funds using the Value at Risk (VaR). There are three methods of calculating the VaR Variance-covariance method, Monte Carlo simulation methods and methods Historical Simulation. In this study, the variance-covariance method used and the Historical Simulation method to measure potential losses on investments largest mutual fund shares at 95% confidence level. The test used is the Kolmogorov-Smirnov normality test and Kupiec test return data to test the accuracy of the calculation of VaR. Because the data are not normally distributed returns, the adjustment is then performed using the Cornish-Fisher Expansion. By using the t test results show that the calculation of VaR with variance-covariance and Historical Simulation did not differ significantly. The test results show that the accuracy of the VaR VaR accurately all used to measure the magnitude of the maximum potential loss on investments in mutual fund shares. Keywords : Value at Risk (VaR), Variance-covariance, Historical Simulation, Mutual Fund, Risk.

PERBANDINGAN METODE REGRESI LOGISTIK BINER DAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) PADA PEMINATAN JURUSAN SMA (Studi Kasus SMA Negeri 2 Semarang) Ratih Binadari; Yuciana Wilandari; Suparti Suparti
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Major specialization at High School is aimed to gives opened opportunity for students to choose subject that are interest and develop their potential in accordance with the abilities, interests, talents, and personality. Major specialization at High school is influenced by some factors. To detect those factors, used biner logistic regression method and Multivariate Adaptive Regression Spline (MARS). Biner Logistic Regression is method that describes relationship between dependent variable and some independent variable, with independent variable has been coded 1 as representing the presence of the characteristic, and 0 as representing the absence of the characteristic. MARS is multivariate nonparametric regression method that development of Recursive Partitioning Regression (RPR) method and Spline method for high dimensional data that produces accurate prediction and continuous models on knots. Both of the methods are compared to know the best method used in research. From the result of analysis using biner logistic regression method and MARS, concluded that major specialization has been influenced by mathematic score, science score and relationship between students and friends. From proportion test, concluded classification that formed by regression logistic is as good as by MARS. Keywords : Major specialization at High School, Biner Logistic Regression, Mutlivariate Adaptive Regression Spline (MARS), Clasification

ANALISIS PENGENDALIAN KUALITAS MENGGUNAKAN DIAGRAM KENDALI DEMERIT (Studi Kasus Produksi Air Minum Dalam Kemasan 240 ml di PT TIW) Gita Suci Ramadhani; Yuciana Wilandari; Suparti Suparti
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The efforts to maintain and improve the quality of the resulting product with statistical process control. Demerit control chart is a control chart in which the defect type is categorized into several classes according to the level of disability interests. Types of defects in the production processes of bottled water 240 ml in PT TIW divided into critical defects, major defects and minor defects. Based on the results of the analysis that has been done shows that the production process has been controlled statistically using demerit control charts on the third iteration for each line 1 and line 2. Capability of production processes in line 1 and line 2 shows that although the production process has been controlled statistics, but the process still produces a product that is not in accordance with specifications. But in the end all defective products are produced, will be immediately discarded and will not be marketed or sold to the consumer. This is done for the commitment PT TIW who always maintain the best quality products. Based on pareto chart for this type of defect on line 1 and line 2, it is known that 20% of the total types of defects, obtained two types of defects which constitute 80% of disability of the entire production process. The defect type is slanted lid and reject filler. The factors that cause this type of defect are slanted lid and reject filler among others, there is a worn machine components and uncorrect machine settings, the operator has not been retrained and lack of focus so not accordance with the procedure in the work, the composition of the materials is uncorrect, and methods or procedures are less well executed.

Page 2 of 70 | Total Record : 693

1 2 3 4 5

Filter by Year

2012 2023

Filter By Issues

All Issue Vol 12, No 3 (2023): Jurnal Gaussian (Forthcomming Issue) Vol 12, No 2 (2023): Jurnal Gaussian Vol 12, No 1 (2023): Jurnal Gaussian Vol 11, No 4 (2022): Jurnal Gaussian Vol 11, No 3 (2022): Jurnal Gaussian Vol 11, No 2 (2022): Jurnal Gaussian Vol 11, No 1 (2022): Jurnal Gaussian Vol 10, No 4 (2021): Jurnal Gaussian Vol 10, No 3 (2021): Jurnal Gaussian Vol 10, No 2 (2021): Jurnal Gaussian Vol 10, No 1 (2021): Jurnal Gaussian Vol 9, No 4 (2020): Jurnal Gaussian Vol 9, No 3 (2020): Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian Vol 9, No 1 (2020): Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian Vol 7, No 4 (2018): Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian Vol 6, No 4 (2017): Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian Vol 5, No 2 (2016): Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian Vol 3, No 1 (2014): Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian Vol 2, No 3 (2013): Jurnal Gaussian Vol 2, No 2 (2013): Jurnal Gaussian Vol 2, No 1 (2013): Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian More Issue

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
-

Contact Email
-

Phone
-

Journal Mail Official
-

Editorial Address
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

Location
Kota semarang,

Jawa tengah

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name -

Contact Email -

Phone -

Journal Mail Official -

Editorial Address 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

Location Kota semarang, Jawa tengah INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
-

Contact Email
-

Phone
-

Journal Mail Official
-

Editorial Address
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

Location
Kota semarang,

Jawa tengah

INDONESIA