cover
Article Per Year (5 Year)
All
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
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 27 Documents
 1   2   3 
Search results for , issue "Vol 1, No 1 (2012): Jurnal Gaussian" : 27 Documents clear
ANALISIS INDEKS HARGA SAHAM GABUNGAN (IHSG) DENGAN MENGGUNAKAN MODEL REGRESI KERNEL Icha Puspitasari; Suparti Suparti; Yuciana Wilandari
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 (558.432 KB) | DOI: 10.14710/j.gauss.v1i1.577

Abstract

Saham merupakaninvestasi yang banyak dipilih para investor, salah satu indikator yang menunjukkan pergerakan harga saham adalah Indeks Harga Saham Gabungan (IHSG). IHSG merupakan data runtun waktu sehingga untuk menganalisisnya dapat menggunakan metode runtun waktu klasik. Namun dengan metode tersebut banyak asumsi yang harus dipenuhi, sehingga diperlukan metode alternatif salah satunya metode regresi nonparametrik karena dalam model regresi nonparametrik tidak ada asumsi khusus sehingga model ini merupakan metode alternatif yang dapat digunakan dalam analisis IHSG. Dalam makalah ini dibandingkan nilai MSE yang dihasilkan dari analisis runtun waktu klasik, regresi parametrik linier sederhana dan regresi nonparametrik kernel. Data IHSG yang digunakan adalah  periode minggu pertama Januari 2011 sampai dengan minggu ke empat Februari 2012. Data tersebut merupakan data closing price saham mingguan pada periode perdagangan terakhir. Hasil perbandingan nilai MSE dari dataIHSG yang sering fluktuatif pada tiga analisis didapatkan nilai MSE terkecil adalah pada analisis menggunakan regresi nonparametrik kernel dengan fungsi triangle dan badwidth h sebesar 58.2 dengan nilai MSE = 6987.787. Model terbaik tersebut dapat digunakan untuk memprediksikan nilai IHSG selanjutnya.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (534.304 KB) | DOI: 10.14710/j.gauss.v1i1.519

Abstract

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
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.
PENENTUAN CADANGAN DISESUAIKAN DENGAN METODE ILLINOIS PADA ASURANSI JIWA ENDOWMEN SEMIKONTINU Marlia Aide Revani; Yuciana Wilandari; Dwi Ispriyanti
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 (542.478 KB) | DOI: 10.14710/j.gauss.v1i1.903

Abstract

Semicontinuous endowment insurance is a kind of insurance with a periodic premium payments which gives two benefits, payment of death benefit at the moment of death if the insured dies during a certain period of years or payment of living benefit if the insured survives to the end of the period. The insurer’s obligation of insured’s premium payments, provides net level premium reserves for benefit payment in the future. The insurer needs expenses for it’s operate and in fact, the first year expenses usually exceed the loading. This means that an insurance company have to find funds to cover the first year expenses. The funds can be obtained by modified reserve system. To get information of modified reserve value for semicontinuous life insurance, the study of determination of modified reserve value using Illinois method has been done. The full net level reserves are lesser than the reserves under the Illinois method before the end of min(n, 20) years and both of these reserves will be equal at the the end of min(n, 20) years, with n is premium period.
PEMODELAN REGRESI ZERO-INFLATED NEGATIVE BINOMIAL (ZINB) UNTUK DATA RESPON DISKRIT DENGAN EXCESS ZEROS Bayu Ariawan; Suparti Suparti; Sudarno Sudarno
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 (813.87 KB) | DOI: 10.14710/j.gauss.v1i1.573

Abstract

Zero-Inflated Negative Binomial (ZINB) regression is one of the methods used in troubleshooting overdispersion due to excessive zero values ​​in the response variable (excess zeros). ZINB regression model was based on the negative binomial distribution resulting from a mixture distribution between Poisson distribution  withis value of random variable which gamma distributed. ZINB regression parameter estimation can be performed by using Maximum Likelihood Estimation (MLE) method then is followed by the EM algorithm (Expectation maximization) procedure and Newton Rhapson. Test the suitability of the model simultaneously performed using Likelihood Ratio test and significance testing parameters individually performed with Wald test statistics. The model is applied to the case of car insurance obtained PT. Insurance of Sinar Mas Semarang Branch in 2010 in the form of data many policyholders filed claims to the PT. Sinar Mas Semarang Branch Insurance. Response variable is the number of claims submitted to the PT. Insurance of Sinar Mas Semarang Branch, while the predictor  variable is the age car and the type of coverage that consists of All Risk, Total Lost Only (TLO), and the joint between All Risk and Total Lost Only (TLO). From the analytical result obtained the conclution that the age of the car and the type of coverage affects number of claims filed by the policyholder to the PT. Insurance of Sinar Mas Semarang Branch in 2010.
PENDEKATAN SISTEM PERSAMAAN SIMULTAN DALAM PEMODELAN PRODUK DOMESTIK REGIONAL BRUTO (PDRB) PROPINSI JAWA TENGAH TAHUN 2000-2010 Rizky Oky Ari Satrio; Tatik Widiharih; Abdul Hoyyi
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 (548.617 KB) | DOI: 10.14710/j.gauss.v1i1.913

Abstract

Gross Domestic Product (GDP) is general indicator used to identify the economical development in a region. The condition of economy in Central Java Province is categorized as stable condition since it has GDP value developed rapidly year by year. Refer to model used by Bappenas,the simultaneous equation model between GDP is influenced by number of employee and government spending.Identification of the model in this study using the ordercondition of indetification on the basis of the result of the overidentified taken the GDP of agriculture, mining, electricity, gas and water sector and trade. Therefore, the parameter evaluation used is 2SLS method (Two Stage Least Square). After fulfilled  assumption of independent, identical and normal distribution, the most valued toward model of GDP in Central Java Province is GDP sector of agriculture.
ESTIMASI PARAMETER DISTRIBUSI WEIBULL DUA PARAMETER MENGGUNAKAN METODE BAYES Indria Tsani Hazhiah; Sugito Sugito; Rita Rahmawati
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 (431.31 KB) | DOI: 10.14710/j.gauss.v1i1.578

Abstract

Interval estimation of a parameter is one part of statistical inference. One of the methods that used is the Bayes method. A Bayesian method is combine prior distribution and distribution of samples, so that the posterior distribution can be obtained. Interval estimation using a method Bayes called credibel interval estimation. In this thesis, the distribution of the sample is used a two-parameter Weibull distribution scale-shape-version of survival distribution (reliability). Data that used are data that is not censored data type and data type II censored if prior distribution using non-informative which of the produce distribution the resulting posterior distribution is gamma distribution. Parameters of the sample distribution that to find out is a parameter that  by the parameter c (shape parameter) known while the parameter b (scale parameter) had unknown.
SIMULASI STOKASTIK MENGGUNAKAN ALGORITMA GIBBS SAMPLING Anifa Anifa; Moch. Abdul Mukid; Agus Rusgiyono
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 (617.001 KB) | DOI: 10.14710/j.gauss.v1i1.569

Abstract

One way to get a random sample is using simulation. Simulation can be done directly or indirectly. Markov Chain Monte Carlo (MCMC) is an indirectly simulation method. MCMC method has some algorithms. In this thesis only discussed about Gibbs Sampling algorithm. Gibbs Sampling is introduced by Geman and Geman at 1984. This algorithm can be used if the conditional distribution of the target distribution is known. It has applied on two casses, these are generation of bivariate normal random data and parameters estimation using Bayesian method. The data used in this research are the death of pulmonary tuberculosis in ASEAN in 2007. The results obtained are  and with standard error for  and .
ANALISIS DATA RUNTUN WAKTU MENGGUNAKAN METODE WAVELET THRESHOLDING Yudi Ari Wibowo; Suparti Suparti; Tarno Tarno
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 (678.49 KB) | DOI: 10.14710/j.gauss.v1i1.918

Abstract

Latterly, wavelet is used in various application of statistics. Wavelet is a method without parameter which used in signal analysis, data compression, and time series analysis. Wavelet thresholding is a method which reconstructing the largest number of wavelet coefficients. Only the coefficients are greater than a specified value which taken and the rest coefficients are ignored, because considered null. Certain value is called the threshold value. The level of smoothness estimation are determined by some factor such as wavelet functions, the type of thresholding functions, level of resolutions and threshold parameters. But most dominant factor is threshold parameter. Because that was required to select the optimal threshold value. At the simulation study was analyzing of the stasioner, nonstasioner and nonlinier data. Wavelet thresholding method gives the value of Mean Square Error (MSE) which is smaller than the ARIMA. Wavelet thresholding is considered quite so well in the analysis of time series data.
RISIKO KREDIT PORTOFOLIO OBLIGASI DENGAN CREDIT METRICS DAN OPTIMALISASI PORTOFOLIO DENGAN METODE MEAN VARIANCE EFFICIENT PORTFOLIO (MVEP) Nurul Fauziah; Abdul Hoyyi; Di Asih I Maruddani
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 (579.223 KB) | DOI: 10.14710/j.gauss.v1i1.904

Abstract

Investing is a important thing in a capital market. Bond investment must be noticed the risk especially credit risk. From the information of credit risk, investor can choose the right investment. Credit Metrics is a reduced form model to estimate the risk. Credit Metrics is centered by the corporate rating. The risk not only occur when corporate rating be default but also if the rating upgrade or downgrade. For a bond portfolio, can calculate the optimal portfolio by Mean Variance Efficient Portfolio method. Empirical study can be used for two bonds, first bond is Obligasi Adira Dinamika Multi Finance V Tahun 2011 Seri A and second one is Obligasi BFI Finance Indonesia III Tahun 2011 Seri A. First bond has 127.01640 (Billion) of credit risk and the second one bonds has 18.33472 (Billion). For a portfolio of that two bonds, they have 179.82460 (Billion). For the optimal portfolio, first bond has propotion 66.39% and 33.61% for the second bond.

Page 1 of 3 | Total Record : 27

 1   2   3 

Filter by Year

2012 2012


Reset
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