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 733 Documents
 18   19   20   21   22 
PENGUKURAN VALUE AT RISK MENGGUNAKAN PROSEDUR VOLATILITY UPDATING HULL AND WHITE BERDASARKAN EXPONENTIALLY WEIGHTED MOVING AVERAGE (EWMA) (Studi Kasus pada Portofolio Dua Saham) Putri, Nurissalma Alivia; Hoyyi, Abdul; Safitri, Diah
Jurnal Gaussian Vol 2, No 4 (2013): 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 (562.586 KB) | DOI: 10.14710/j.gauss.v2i4.3809

Abstract

Investment is an effort to get profits for individual or institution. But the investment policy is always faced with market risk as the effect of financial instruments movement such as stock price movements. Market risk measurement tool commonly used is Value at Risk (VaR), which measures the amount of loss at a certain confidence level. VaR measurement by Hull and White volatility updating procedure is a modification of the historical simulation involving information of volatility change calculated by Exponentially Weighted Moving Average (EWMA). This procedure is fit to financial data such as stock returns that are generally not normally distributed and are heteroskedastic. VaR calculation applied to the portfolio between Kalbe Farma Tbk (KLBF) stock and Lippo Karawaci Tbk (LPKR) stock from 3 January 2011 to 19 April 2013 were selected based on the largest trading volume at the end of the observation for LQ45 stocks listed in the Indonesia Stock Exchange (IDX) . The data used is the return calculated from the closing price of stocks. The validity of VaR was tested through a back test by Kupiec test, and concluded that the 95% VaR and 99% VaR are valid.
KAPABILITAS PROSES DENGAN ESTIMASI FUNGSI DENSITAS KERNEL PADA PRODUKSI DENIM DI PT APAC INTI CORPORA Puput Ramadhani; Dwi Ispriyanti; Diah Safitri
Jurnal Gaussian Vol 7, No 3 (2018): 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 (494.12 KB) | DOI: 10.14710/j.gauss.v7i3.26665

Abstract

The quality of production becomes one of the basic factors of consumer decisions in choosing a product. Quality control is needed to control the production process. Control chart is a tool used in performing statistical quality control. One of the alternatives used when the data obtained is not known distribution is analyzed by nonparametric approach based on estimation of kernel density function. The most important thing in estimating kernel density function is optimal bandwidth selection (h) which minimizes Cross Validation (CV) value. Some of the kernel functions used in this research are Rectangular, Epanechnikov, Triangular, Biweight, and Gaussian. If the process control chart is statistically controlled, a process capability analysis can be calculated using the process conformity index to determine the nature of the process capability. In this research, the kernel control chart and process conformity index were used to analyze the slope shift of Akira-F style fabric and Corvus-SI style on the production of denim fabric at PT Apac Inti Corpora. The results of the analysis show that the production process for Akira-F style is statistically controlled, but Ypk > Yp is 0.889823 > 0,508059 indicating that the process is still not in accordance with the specified limits set by the company, while for Corvus- SI is statistically controlled and Ypk < Yp is 0.637742 < 0.638776 which indicates that the process is in accordance with the specification limits specified by the company. Keywords:     kernel density function estimation, Cross Validation, kernel control chart, denim fabric, process capability
PENERAPAN PENGENDALIAN KUALITAS JENIS VARIABEL PADA PRODUKSI MAKANAN (Studi Kasus pada Pabrik Wingko Babat Cap “Moel” Semarang) Dewiga, Pramestiara; Sudarno, Sudarno; Prahutama, Alan
Jurnal Gaussian Vol 4, No 3 (2015): 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 (859.313 KB) | DOI: 10.14710/j.gauss.v4i3.9487

Abstract

Wingko is a typical product from Semarang that growing and evolving because of the increase in tourism of Semarang City. Competition between each producer requires them to improve product quality. This study aims to minimize defective products and to monitor the distribution of the product to be worthy. Factors that are used as the benchmarks a wingko production process are the net weight and oven temperature for acceptance sampling plan. The R,  dan s control charts are used to monitor the production process and estimated capability process is used to minimize process defects. While acceptance sampling plans are used to determine the feasible product to distribute or not. Based on the analyze result that the production process is controlled after eliminating the 1st and the 28th sample number. Estimated capability process of 1.2508 indicates that it is a little defect product produced and DPMO value of 180 means that there are 180 defects per one million productions. While the acceptance sampling plan according to single specification limit either form 1 and form 2 indicates that wingko was acceptable (can be distributed). Keywords: Wingko, Net Weight, Quality Control, Capability Process
OPTIMALISASI JUMLAH BATU BATA YANG PECAH MENGGUNAKAN DESAIN EKSPERIMEN TAGUCHI (Studi Kasus: Usaha Batu Bata Bapak Kholil Ds. Bulak Karangawen) Cakra Kurniawan; Hasbi Yasin; Sugito Sugito
Jurnal Gaussian Vol 3, No 2 (2014): 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 (471.544 KB) | DOI: 10.14710/j.gauss.v3i2.5907

Abstract

Brick is a substansial element in building construction. The strength of building may depend on bricks, a solid construction uses the best quality brick’s, which is not crumbling and broken into two parts. There are two popular types of bricks in Semarang, Penggaron bricks and Welahan bricks, Penggaron bricks is the most desirable type in market, but the quality of Penggaron bricks is worse than Welahan bricks, because the Penggaron bricks broken pieces are much more than Welahan’s. So that Penggaron bricks were taken to do research in purpose of optimizing the number of brick’s broken pieces that occurred during the production process. The method being used was the "Taguchi Design of Experiments" using Smaller is Better as quality character. The outcome of pre-experimental study was 3 factors and 2 levels so that L4 Orthogonal Array was used. After analyzing and conducting confirmation experiment, the result was obtained as follow, at the initial conditions, there are 4.6% of broken bricks, the broken bricks became 1.8%, after the experiment. The 1.8% of broken bricks were still within the range of the predicted value 1% to 2%.
PERBANDINGAN METODE MOORA DAN TOPSIS DALAM PENENTUAN PENERIMAAN SISWA BARU DENGAN PEMBOBOTAN ROC MENGGUNAKAN GUI MATLAB Rafida Zahro Hasibuan; Alan Prahutama; Dwi Ispriyanti
Jurnal Gaussian Vol 8, No 4 (2019): 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 (881.296 KB) | DOI: 10.14710/j.gauss.v8i4.26726

Abstract

MAN Asahan is an educational institution that selects new students every year. MAN Asahan sets certain criteria in choosing new students so that selected students are of high quality. The criteria determined are the Al-Qur'an test scores, national exam scores, Academic Potential Test scores and achievement certificates. In selecting new students who were accepted as many as 271 of the 530 registrants the school still used the manual process so that it needed accuracy and a long time. In this study a decision support system was created that could be a solution to assist the selection process according to school criteria. The system will applied is MOORA (Multi-Objective Optimization on the Base of Ratio Analysis) method and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) with the weighting method of ROC (Rank Order Centroid). Then the sensitivity analysis is done to determine the appropriate method to be chosen to obtain optimal results. This research was conducted with the help of the MATLAB GUI as a computing tool. The GUI that is built can simplify and speed up the selection process. Based on the results of the study, the average percentage value of sensitivity for the MOORA method is -1.61% while the TOPSIS method is -7.96%. With the existence of sensitivity analysis it can be known the most appropriate method for this case is the MOORA method.Keywords: Students, MOORA, TOPSIS, ROC, Sensitivity, GUI Matlab
PERAMALAN TINGGI GELOMBANG BERDASARKAN KECEPATAN ANGIN DI PERAIRAN PESISIR SEMARANG MENGGUNAKAN MODEL FUNGSI TRANSFER (Studi Kasus Bulan Januari 2014 sampai dengan Desember 2014) Firda Megawati; Rita Rahmawati; Suparti Suparti
Jurnal Gaussian Vol 4, No 4 (2015): 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 (876.704 KB) | DOI: 10.14710/j.gauss.v4i4.10221

Abstract

Semarang coast is suitable to develop marine transport activities such as sailing because of its strategic location in the coastal region of Indonesia. The condition of sailing in Indonesia is said smoothly if wave height is in the normal range which is 1-2 meters. Therefore, there will be research to predict wave height in Semarang harbor by using transfer function. The data used is secondary data from BMKG of Semarang period January 2014 to December 2014 with the variable X as the average daily of wind speed and variable Y as average daily of wave height. Model that formed based on the input wind speed is ARIMA(2,1,0) while transfer function model that formed is b=0, s=0, and r=0 with noise model ARMA(1,1). The forecasting results for January 2015 show that the wave height tends to rise and the highest wave is on the third day with 0,9589 meters. Calculation accuracy of forecasting wave heights using transfer function model with MAPE produce a value of 18,7%. Keywords : Transfer Function, Wave Height, Wind Speed.
RANCANGAN D-OPTIMAL UNTUK REGRESI POLINOMIAL DUA FAKTOR DERAJAT DUA Rosmalia Safitri; Tatik Widiharih; Triastuti Wuryandari
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 (497.12 KB) | DOI: 10.14710/j.gauss.v1i1.914

Abstract

Suatu penelitian dalam bidang kimia seringkali memerlukan suatu rancangan optimal untuk menentukan titik mana dari variabel prediktor yang akan dicobakan dengan tujuan memaksimalkan sejumlah informasi yang relevan sehingga terpenuhi kriteria yang diinginkan. Kriteria pemenuhan optimal didasarkan pada matriks rancangan dari model yang dipilih. Kriteria D-optimal digunakan untuk meminimalkan variansi dari estimasi parameter dengan cara memaksimalkan determinan matriks informasinya atau meminimalkan determinan matriks dispersinya. Pemilihan titik-titik dari variabel prediktor selain tergantung dari model yang dipilih juga tergantung dari banyaknya pengamatan yang diinginkan.Kriteria D-optimal diaplikasikan pada data simulasi untuk kasus pengukuran nilai persentase kelarutan enam reaksi kimia berdasarkan nilai suhu dan lama reaksinya. Diperoleh kesimpulan bahwa determinan matriks informasi maksimal terjadi pada saat iterasi keempat dengan nilainya sebesar 2.2070 x 109.
ANALISIS PEMBENTUKAN PORTOFOLIO PADA PERUSAHAAN YANG TERDAFTAR DI LQ45 DENGAN PENDEKATAN METODE MARKOWITZ MENGGUNAKAN GUI MATLAB Titin Afriana; Tarno Tarno; Sugito Sugito
Jurnal Gaussian Vol 6, No 2 (2017): 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 (618.283 KB) | DOI: 10.14710/j.gauss.v6i2.16954

Abstract

Portfolio is one of  ways  in investment activity that  undertaken by more than one asset with intent to determining the amount of proportion of investment  that to be made in a certain period of time. To determine optimal portofolio, one of  analysis model which can be played is Markowitz. Markowitz exressed through diversification concept (with  making of the optimal stock of  portfolio), investor can maximize the expected income from investments with specific risk level or seeking to minimize risk to target certain profit level. To simplify the calculation of the portfolio for  public, there is an application that made by using GUI in Matlab. Matlab (Matrix Laboratory) is an interactive programming system with  basic elements of array database which dimensions do not need to be stated in a particular way, while the GUI is the submenu of Matlab. Generally, Matlab GUI is  more easily learned and  used because  it worked without  need to know  the commandments and how the command works. The data used in this study consists of five types of assets in the LQ45 group, there are BBNI,  PWON, PTBA, INCO, dan KLBF. In determining the portfolio proportion used trial and error method and Lagrange method. Based on the portfolio proportion of both methods obtained the optimal portfolio is almost the same. Keywords: GUI Matlab, LQ45, Portfolio, Markowitz, Trial and Error, Lagrange
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEMISKINAN DI JAWA TENGAH MENGGUNAKAN MODEL GALAT SPASIAL Octafinnanda Ummu Fairuzdhiya; Rita Rahmawati; Agus Rusgiyono
Jurnal Gaussian Vol 3, No 4 (2014): 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 (652.239 KB) | DOI: 10.14710/j.gauss.v3i4.8089

Abstract

Poverty is one of problems in developing country like Indonesia. From year to year, poverty in Central Java has decreased. This study is aimed to know the poverty model in Central Java by using Spatial Error Model. This research uses data from the number of poor people in Central Java in 2012. Spatial Error Model is a spatial method that showed spatial autocorrelation in the error. In Spatial Error Model, there are spatial dependency effect and spatial heterogenity. The variables that significantly affect the number of poor people in Central Java through Spatial Error Model are the percentage of 10 years old–over population with the highest education is primary school ( X2) and the number of households that have access to reliable drinking water (X3). This Spatial Error Model results R2 are 75,39% with the AIC are 63,36. It is better than regression model of Ordinary Least Square (OLS) which produces 66,3% of R2 with AIC are 69,286. It showed the poverty model in Central Java by using Spatial Error Model is better than regression model of Ordinary Least Square (OLS) and in OLS assumption of homoskedasticity not significant. Keywords: Poverty, Regression, Ordinary Least Square, Spastial Error Model
ANALISIS KLASIFIKASI NASABAH KREDIT MENGGUNAKAN BOOTSTRAP AGGREGATING CLASSIFICATION AND REGRESSION TREES (BAGGING CART) Desy Ratnaningrum; Moch. Abdul Mukid; Triastuti Wuryandari
Jurnal Gaussian Vol 5, No 1 (2016): 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 (594.532 KB) | DOI: 10.14710/j.gauss.v5i1.11031

Abstract

Credit is one of the facilities provided by banks to lend money to someone or a business entity within the prescribed period. The smooth repayment of credit is essential for the bank because it influences the performance as well as its presence in daily life. Acceptance of prospective credit customers should be considered to minimize the occurrence of bad credit. Classification and Regression Trees (CART) is a statistical method that can be used to identify potency of credit customer status such as current credit and bad credit. The predictor variables used in this study are gender, age, marital status, number of children, occupation, income, tenor / period, and home ownership. To improve the stability and accuracy of the prediction were used the Bootstrap Aggregating Classification and Regression Trees (Bagging CART) method. The classification of credit customers using Bagging CART gives the classification accuracy 81,44%. Key words : Credit, Bootstrap Aggregating Classification and Regression Trees (Bagging CART), Classification Accuracy

Page 20 of 74 | Total Record : 733

 18   19   20   21   22 

Filter by Year

2012 2024


Reset
Filter By Issues
All Issue Vol 13, No 1 (2024): Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian 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