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 10 Documents

Search results for , issue "Vol 7, No 3 (2018): Jurnal Gaussian" : 10 Documents clear

PEMODELAN INDEKS HARGA SAHAM GABUNGAN MENGGUNAKAN REGRESI SPLINE MULTIVARIABEL Ihdayani Banun Afa; Suparti Suparti; Rita Rahmawati
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The Composite Stock Price Index (CSPI) is a composite index of all types of shares listed on the stock exchange and their movements indicate the conditions occurring in the stock market. CSPI movement is an important indicator for investors to determine whether they will sell, hold, or buy a stock. One of the factors that influence the movement of CSPI is Inflation (X1), Exchange Rate (X2) and SBI rate (X3). This study aims to obtain the best CSPI model using a multivariable nonparametric spline regression approach. The approach is done by nonparametric regression because the regression curve obtained does not show a certain relationship pattern. Spline is very dependent on the order and location of the knot point. The best spline model is the model that has the minimum MSE (Mean Square Error) value. In this study, the best spline regression model is when X1 is 4 order, X2 is 2 order and X3 is 2 order. The number of knots on X1 is 1 knot at 8.22, X2 is 2 knots at 13066.82 and 13781.75 While X3 is 2 knots at 6.6 and 6.67 with value of MSE equal to 6686.85.Keywords: Composite Stock Price Index, Multivariable Spline Regression, MSE

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

KETAHANAN HIDUP PASIEN GAGAL GINJAL DENGAN METODE KAPLAN MEIER (Studi Kasus di Rumah Sakit Umum Daerah dr. R. Soedjati Soemodiarjo Purwodadi) Immawati Ainun Habibah; Tatik Widiharih; Suparti Suparti
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Chronic Kidney Disease (CKD) is a failure of kidney function that which get slowly and can not recover. Most of the patients CKD get death sudden becuse of cardiovascular complications (related to the heart and blood vessels) however only minor part can reach terminal phase (CKD stage 5) which need replacement therapy of Kidney. Replacement therapy of Kidney are hemodialysis, peritoneal dialysis, and Kidney transplant. Because of that, the importance to study how long the patient opportunity is life endurance analysis. Survival analysis methods to life depend from the life time and status of individual life time. Survival analysis uses Kaplan-Meier method. During the observation process, there is different observations so censor type III is choosen. Censor type III is censoring type which research is done to individual in and out for determine time, because of that estimation value of survival can be caunted using Kaplan Meier method with censor type III. This research uses medical records data from the patients with kidney failure period 1 January 2014 until 30 November 2017 in RSUD dr.R. Soedjati Soemodiarjo Purwodadi Grobogan Regency. The results of the analysis and discussion are known that if hemodialysis getting longer done, estimation value of survival. With an average estimate of survival is 776 days. Keywords: Chronic Kidney Disease, Survival Analysis, Kaplan Meier

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

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

GENERALIZED PARETO DISTRIBUTION UNTUK PENGUKURAN VALUE AT RISK PADA PORTOFOLIO SAHAM SYARIAH DAN APLIKASINYA MENGGUNAKAN GUI MATLAB Desi Nur Rahma; Di Asih I Maruddani; Tarno Tarno
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The capital market is one of long-term investment alternative. One of the traded products is stock, including sharia stock. The risk measurement is an important thing for investor in other that can decrease investment loss. One of the popular methods now is Value at Risk (VaR). There are many financial data that have heavy tailed, because of extreme values, so Value at Risk Generalized Pareto Distribution is used for this case. This research also result a Matlab GUI programming application that can help users to measure the VaR. The purpose of this research is to analyze VaR with GPD approach with GUI Matlab for helping the computation in sharia stock. The data that is used in this case are PT XL Axiata Tbk, PT Waskita Karya (Persero) Tbk, dan PT Charoen Pokphand Indonesia Tbk on January, 2nd 2017 until May, 31st 2017. The results of VaRGPD are: EXCL single stock VaR 8,76% of investment, WSKT single stock VaR 4% of investment, CPIN single stock VaR 5,86% of investment, 2 assets portfolio (EXCL and WSKT) 4,09% of investment, 2 assets portfolio (EXCL and CPIN) 5,28% of investment, 2 assets portfolio (WSKT and CPIN) 3,68% of investment, and 3 assets portfolio (EXCL, WSKT, and CPIN) 3,75% of investment. It can be concluded that the portfolios more and more, the risk is smaller. It is because the possibility of all stocks of the company dropped together is small. Keywords: Generalized Pareto Distribution, Value at Risk, Graphical User Interface, sharia stock

PEMODELAN PRODUKSI BAWANG MERAH DI JAWA TENGAH DENGAN MENGGUNAKAN HYBRID AUTOREGRESSIVE INTEGRATED MOVING AVERAGE – ADAPTIVE NEURO FUZZY INFERENCE SYSTEM Inas Husna Diarsih; Tarno Tarno; Agus Rusgiyono
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Red onion is one of the strategic horticulture commodities in Indonesia considering its function as the main ingredients of the basic ingredients of Indonesian cuisine. In an effort to increase production to supply national necessary, Central Java as the main center of red onion production should be able to predict the production of several periods ahead to maintain the balance of national production. The purpose of this research is to get the best model to forecast the production of red onion in Central Java by ARIMA, ANFIS, and hybrid ARIMA-ANFIS method. Model accuracy is measured by the smallest RMSE and AIC values. The results show that the best model to modeling red onion production in Central Java is obtained by hybrid ARIMA-ANFIS model which is a combination between SARIMA ([2], 1, [12]) and residual ARIMA using ANFIS model with input et,1, et,2 on the grid partition technique, gbell membership function, and membership number of 2 that produce RMSE 12033 and AIC 21.6634. While ARIMA model yield RMSE 13301,24 and AIC 21,89807 with violation of assumption. And the ANFIS model produces RMSE 14832 and AIC 22,0777. This shows that ARIMA-ANFIS hybrid method is better than ARIMA and ANFIS.Keywords: production of red onion, ARIMA, ANFIS, hybrid ARIMA-ANFIS

PERAMALAN EKSPOR NONMIGAS DENGAN VARIASI KALENDER ISLAM MENGGUNAKAN X-13-ARIMA-SEATS (Studi Kasus: Ekspor Nonmigas Periode Januari 2013 sampai Desember 2017) Eka Lestari; Tatik Widiharih; Rita Rahmawati
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Non-oil and gas exports are one of the largest foreign exchange earners for Indonesia. Non-oil and gas exports always experience a decline in the month of Eid Al-Fitr due to delays in the delivery of export goods because the loading and unloading of goods at the port is reduced during Eid Al-Fitr. The shift of the Eid Al-Fitr month on the data will form a pattern or season with an unequal period called the moving holiday effect. The time series forecasting method that usually used the ARIMA method. Because the ARIMA method only suitable for time series data with the same seasonal period and can’t handle the moving holiday effect, the X-13-ARIMA-SEATS method used two steps. First, regARIMA modeling is a linear regression between time series data and the weight of Eid Al-Fitr and the residuals follow the ARIMA process. The weighting is based on three conditions, namely pre_holiday, post_holiday, and multiple. Second, X-12-ARIMA decomposition method for seasonal adjustments that produces trend-cycle components, seasonal, and irregular. Based on the analysis carried out on the monthly non-oil and gas export data for the period January 2013 to December 2017, the X-13-ARIMA-SEATS (1,1,0) model was obtained in the post_holiday condition as the best model. The forecasting results in 2018 show the largest decline in non-oil and gas exports in June 2018 which coincided with the Eid Al-Fitr holiday. MAPE value of 10.90% is obtained which shows that the forecasting ability is good.Keywords: time series, non-oil and gas, X-13-ARIMA-SEATS, moving holiday

COPULA FRANK PADA VALUE at RISK (VaR) PEMBENTUKAN PORTOFOLIO BIVARIAT (Studi Kasus : Saham-Saham Perusahaan yang Meraih Predikat The IDX Top Ten Blue Tahun 2017 dengan Periode Saham 20 Oktober 2014 – 28 Februari 2018) Juria Ayu Handini; Di Asih I Maruddani; Diah Safitri
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The capital market has an important role in society to invest in financial instruments. Investors can invest in the form of a portfolio that is by combining several shares to reduce the risk that will occur. Value at Risk (VaR) is a method for estimating the worst risk of an investment. GARCH (Generalized Autoregressive Conditional Heteroscedasticity) is used to model high-volatile stock data that causes residual variance is not constant. Copula theory is a powerful tool for modeling joint distributions because it does not require normality assumptions that are difficult to fulfill in financial data. Copula Frank has a feature that can identify positive and negative dependencies. This study aims to measure the value of VaR using the Frank-GARCH copula method using stock returns data of PT Bank Rakyat Indonesia, Tbk (BBRI), PT Telekomunikasi Indonesia, Tbk (TLKM), and PT. Unilever Indonesia, Tbk (UNVR) for the period 20 October 2014 - 28 February. Bivariate portfolio pairs obtained namely TLKM and UNVR shares because they have the highest Rho Spearman residual correlation value of ρ = 0.3204. Based on the generation of data using Monte Carlo simulations, the results of the calculation of Value at Risk (VaR) of 1.40% at the 90% confidence level, 1.89% at the 95% confidence level, and 2.79% at the 99% confidence level. Keywords: Value at Risk, Frank copula, GARCH, Monte Carlo

PENERAPAN METODE EXPONENTIALLY WEIGHTED MOVING AVERAGE (EWMA) DALAM PENGUKURAN RISIKO INEVSTASI SAHAM PORTOFOLIO UNTUK VOLATILITAS HETEROGEN Wulandari, Heni Dwi; Mustafid, Mustafid; Yasin, Hasbi
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Risk measurement is important in making an investment. One tool used in the measurement of investment risk is Value at Risk (VaR). VaR represents the greatest possible loss of investment with a given period and level of confidence. In the calculation of Value at Risk requires the assumption of normality and homogeneity. However, financial data rarely satisfies that assumption. Exponentially Weighted Moving Average is one method that can be used to overcome the existence of a heterogeneous variant. Daily volatility is calculated using the EWMA method by taking a decay factor of 0.94. VaR portfolio of ASII, BBNI and PTBA stocks is calculated using historical simulation method from the revised portfolio return with Hull and White volatility updating procedure. VaR values obtained are valid at a 99% confidence level based on the validity test of Kupiec PF and Basel rules. Keywords: Value at Risk (VaR), Portfolio, EWMA, Historical Simulation, Volatility Updating

ANALISIS ANTREAN BUS NONPATAS AKAP DAN AKDP JALUR TIMUR TERMINAL TIRTONADI KOTA SURAKARTA Sitomurang, Rosalina Aprilda; Sugito, Sugito; Mukid, Moch. Abdul
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The queuing system is a set of customers, services and a set of rules governing the arrival of its customers and services. Queue is a waiting phenomenon that is part of everyday human life. The queue is formed if the number of subscribers to be served exceeds the available service capacity. Queue phenomenon one of them seen in the queue nonpatas buses at Terminal Tirtonadi Surakarta. Nonpatas bus lanes studied include non-purpose buses Surabaya, Karanganyar, Wonogiri, Purwodadi and Pedesaan. The queue displant used is FIFO (First In First Out). For the five nonpatas bus lanes it meets steady state conditions because it has utility value less than 1. The selected model is a model that has the following 4 types of distributions: Erlang, Weibull, Gamma and Lognormal. The queue model generated for the five tracks (ERLA/ERLA/1):(GD/∞/∞) for Surabaya nonpatas buses, (ERLA/WEIB/1):(GD/∞/∞) for Karanganyar nonpatas buses, (GAMM/WEIB/1):(GD/∞/∞) for Wonogiri nonpatas buses, (ERLA/WEIB/1):(GD/∞/∞) for Purwodadi nonpatas buses, (WEIB/LOGN/1):(GD/∞/∞) for Pedesaan nonpatas buses. Based on the value of the system performance measure indicated that the five lines are queue system is good. Keywords: Beta, Erlang, FIFO, Gamma, Steady State Conditions, Lognormal, Queue Model, Queuing Systems, System Performance Measure, Weibull

Page 1 of 1 | Total Record : 10

Filter by Year

2018 2018

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