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 733 Documents

19 20 21 22 23

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEPUTUSAN PEMBELIAN DAN KEPUASAN KONSUMEN PADA NOTEBOOK MEREK ACER (Studi Kasus Mahasiswa Universitas Diponegoro) Koko Arie Bowo; Abdul Hoyyi; Moch. Abdul Hoyyi
Jurnal Gaussian Vol 2, No 1 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Consumer perception about notebook product is a variated, this condition based on consumer need is referred that will exploit existing facility at a notebook. Generally, consumer buys a notebook product based on some considerations for example price, brand and product quality. If the product that the of exceed its expectation, consumer will satisfied and possibility will submit the good things about the products to others people. This research aims to analyze the factors that have an effect on purchasing decisions and consumer satisfaction on Acer notebook. Data collecting in this research use questionnaire , that was distributed to 110 students from Diponegoro University that have a Acer notebook.Technique sample uses accidental sampling method. The data obtained are then analyzed using Structural Equation Modeling (SEM). Based on research result is obtained that brand image not has an effect on to purchasing decision Acer notebook, while the product quality and price have an effect on purchasing decision Acer notebook. Despitefully also, the product quality and purchasing decision Acer notebook have an effect on consumer satisfaction. Keywords: brand image, price, quality product, purchasing decision, consumer satisfaction.

PERAMALAN PRODUK DOMESTIK BRUTO (PDB) SEKTOR PERTANIAN, KEHUTANAN, DAN ‎PERIKANAN MENGGUNAKAN SINGULAR SPECTRUM ANALYSIS (SSA) Desy Tresnowati Hardi; Diah Safitri; Agus Rusgiyono
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Forecasting is the process of estimating conditions in the future by testing conditions from the past. One of the forecasting methods is Singular Spectrum Analysis (SSA) which aim of SSA is to make a decomposition of the original series into the sum of a small number of independent and interpretable components such as a slowly varying trend, oscillatory components and a structureless noise. Gross Domestic Product data in the agriculture, forestry, and fisheries sector are time series data with trend and seasonal pattern so that it can be processed using the SSA method. The forecasting process of SSA method uses the main parameter (L) of 21 obtained by the Blind Source Separation (BSS) method. From forecasting, acquired group of 3 groups. Forecasting resulted the value of Mean Absolute Percentage Error (MAPE) is 1.59% and the value of tracking signal is 2.50, which indicates that the results of forecasting is accurate. Keywords: Forecasting, Gross Domestic Product in the agriculture, forestry, and fisheries sector, Singular Spectrum Analysis (SSA)

IMPLEMENTASI METODE LEAN SIX SIGMA SEBAGAI UPAYA MEMINIMALISASI CACAT PRODUK KEMASAN CUP AIR MINERAL 240 ml (STUDI KASUS PERUSAHAAN AIR MINUM) Ari Fakhrus Sanny; Mustafid Mustafid; Abdul Hoyyi
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Efforts to increase productivity can not be said that the human factor is not the only factor which should be observed, studied, analyzed, and repaired in the effort to increase productivity, but also other factors such as machine, equipment, raw materials, factory buildings, etc. may also affect the productivity improvement efforts remain to be considered. Quality is the customer's main factor to decide products and services. Therefore, quality is a key factor which brings business success and growth, and improves competitive position. Lean six sigma method is a method to identify and eliminate waste or activities which are not value added and analyze defect rate product approaches zero defect products. This study aims to implement lean six sigma methods in quality control with case studies of product quality bottled water cup 240 ml at the quality control process produces eleven types of disabilities. Efforts should be made to improve the quality of products, one of them by monitoring the production process control diagram. The results obtained in this study is the value of DPMO on line 1 of 546 machines produce sigma level of 4.766 and a percentage of 99.95%, which means that in a million products cup 240 ml mineral water contained 0.05% units of a product that does not fit in production line machine 1. The DPMO values on line 2 of 291 machines produce sigma level of 4.932 and a percentage of 99.97%, which means that in a million products cup 240 ml mineral water contained 0.03% units of a product that does not fit in production line machine 2. Keywords : Quality, Quality Control, Lean Six Sigma

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PENERIMAAN PESERTA DIDIK SMA NEGERI 2 SEMARANG MENGGUNAKAN METODE REGRESI LOGISTIK ORDINAL Galuh Riani Putri; Yuciana Wilandari; Triastuti Wuryandari
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Education can be used to determine the standard quality of life. One way to get an education is studying in the schools. In Semarang, there are several schools, one of which is SMAN 2 Semarang. In order to pass the admission selection of students at SMAN 2 Semarang, students must fulfill the requirements that had specified by the school. To determine the factors that affect the acceptance of students, the author uses ordinal logistic regression method. Ordinal logistic regression method is used to model the relationship between the response variable that consists of more than two categories and there are levels in that category with several independent variables that are categories or continuous. After doing research using ordinal logistic regression method, the result is that the factors that affect the acceptance of students of SMAN 2 Semarang is Indonesian scores, English scores, Mathematics scores, Science scores, Benefit scores, Achievement scores and also Rayon with the accuracy of the classification by 89, 63%. Keywords: Education, Admission of Students, Ordinal Logistic Regression

ANALISIS PASIEN RAWAT INAP BERDASARKAN KELAS PERAWATAN DI RSUP Dr. KARIADI SEMARANG DENGAN METODE ANTRIAN Friska Irnas Adiyani; Sugito Sugito; Triastuti Wuryandari
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Health is the right of everyone. RSUP Dr. Kariadi as one of the health service facilities has an obligation to provide service optimally to overcome the necessities and complaints of the patients. Nevertheless, the high number of patients that are not in balance with the amount of service facilities be constraints in achieving this purpose, so the patient must be entered the waiting-list or having a queuing situation. This situation happens in queuing system of the hospitalization patients at the place for registration of hospitalization patients (TPPRI) and at the care room installation of hospitalization A and B RSUP Dr. Kariadi Semarang. Therefore, it is necessary to determine the queuing system models that is appropriately with the conditions and characteristics of the queuing at TPPRI and care room that classified based on care class. So it can help in determining the decision to achieve the effective and efficient service. From the analysis result, the best queuing model for TPPRI is and for the care room that is classified based on care class are for the main class, for the first class, for second class, dan the last for third class.

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

ANALISIS KELOMPOK DENGAN ALGORITMA FUZZY C-MEANS DAN GUSTAFSON KESSEL CLUSTERING PADA INDEKS LQ45 Lailly Rahmatika; Suparti Suparti; Diah Safitri
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Clustering analysis is a data analysis aimed at determining a group of data based on common characteristics. Grouping method that’s being developed now is fuzzy clustering analysis. Fuzzy clustering algorithm that’s commonly used is the Fuzzy C-Means (FCM) algorithm and developed further by Gustafson Kessel Clustering (GK) which is able to detect groups with different shape than the FCM. This study examines the comparative application of FCM and GK clustering method in a case study, namely grouping in LQ45 based on the shares ratio of Earning Per Share (EPS) and Price Earning Ratio (PER). Determination of the optimal number of groups is done through calculation Xie and Beni validity index.In this research the algorithm FCM and GK will be made using MATLAB software, such as GUI-based application program which can help users to perform clustering analysis. In some cases, the research results showed that GK is better than FCM, specifically in generating the objective function and the standard deviation ratio of the minimum group. Based on the validity index Xie and Beni, it can be concluded that the optimal number of groups are divided into three.Keywords: Categories of Stocks, Fuzzy C-Means, Gustafson Kessel clustering, Xie and Beni index.

PENDEKATAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION UNTUK PEMODELAN PERTUMBUHAN EKONOMI MENURUT KABUPATEN/KOTA DI JAWA TENGAH Pratama Ganang Widayaka; Mustafid Mustafid; Rita Rahmawati
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Global regression models with a multitude of residual variance in each region causing non-homoskedastisitas assumptions are not met. The diversity of the geographic location factors causing spatial heterogeneity. Geographically Weighted Regression (GWR) is a development of linear regression by involving diverse factors geographical location, so that the parameters generated will be local. GWR model is not able to model the combination of local and global influences in a model. So the purpose of forming a GWR Mixed models are able to establish a model GWR with local and global influences simultaneously. GWR Mixed Model is used to estimate the model Gross Regional Domestic Product (GRDP). As independent variables that influence is revenue (PAD/X1), a variable amount of labor (JAK/X2), the human development index (HDI/X3), unemployment rate (TPT/X4) and the regional minimum wage (UMR/X5). Mixed GWR model the variables that are local and which are global variables. Methods for estimating model parameters MGWR using Weighted Least Square (WLS). Weights obtained the appropriate model to estimate the optimal bandwidth by using the reference method Cross Validation (CV) is a minumum. MGWR models with adaptive exponential kernel function weighting on Gross Domestic Product in the districts / cities in Central Java to produce variable JAK, IPM and TPT have the nature of the locality an area that is significant to the later model PAD have a global nature that sigbifikan against the model. To mengengetahui error rate value model is used Akaike Information Criterion (AIC). Keywords: Akaike Information Criterion, Bandwidth Cross Validation, Fungsi Kernel Gaussian, Mixed Geographically Weighted Regression, Weighted Least Square.

PERBANDINGAN KINERJA MUTUAL K-NEAREST NEIGHBOR (MKNN) DAN K-NEAREST NEIGHBOR (KNN) DALAM ANALISIS KLASIFIKASI KELAYAKAN KREDIT Annisa Sugesti; Moch. Abdul Mukid; Tarno Tarno
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Credit feasibility analysis is important for lenders to avoid the risk among the increasement of credit applications. This analysis can be carried out by the classification technique. Classification technique used in this research is instance-based classification. These techniques tend to be simple, but are very dependent on the determination of K values. K is number of nearest neighbor considered for class classification of new data. A small value of K is very sensitive to outliers. This weakness can be overcome using an algorithm that is able to handle outliers, one of them is Mutual K-Nearest Neighbor (MKNN). MKNN removes outliers first, then predicts new observation classes based on the majority class of their mutual nearest neighbors. The algorithm will be compared with KNN without outliers. The model is evaluated by 10-fold cross validation and the classification performance is measured by Gemoetric-Mean of sensitivity and specificity. Based on the analysis the optimal value of K is 9 for MKNN and 3 for KNN, with the highest G-Mean produced by KNN is equal to 0.718, meanwhile G-Mean produced by MKNN is 0.702. The best alternative to classifying credit feasibility in this study is K-Nearest Neighbor (KNN) algorithm with K=3.Keywords: Classification, Credit, MKNN, KNN, G-Mean.

PENGGUNAAN SIMULASI MONTE CARLO UNTUK PENGUKURAN VALUE AT RISK ASET TUNGGAL DAN PORTOFOLIO DENGAN PENDEKATAN CAPITAL ASSET PRICING MODEL SEBAGAI PENENTU PORTOFOLIO OPTIMAL (Studi Kasus: Index Saham Kelompok SMinfra18) Pradana, Danang Chandra; Maruddani, Di Asih I; Yasin, Hasbi
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In financial markets, a stock is a unit of account for various investments. It often means the stock of a corporation, but also used for collective investments such as mutual funds, limited partnerships, and real estate investment trusts. In this era, most investors establish a stock portfolio as one way to reduce the risk of loss or risk which may be obtained when investing in stocks. Formation of portfolio in this research, investors is used to calculate the weight of the investment using the Capital Asset Pricing Model (CAPM). Risks of investing often called Value at Risk (VaR), calculate the VaR using Monte Carlo simulation. From the results and analysis conducted on a group of SMInfra18 stocks, there are two stocks into the portfolio with an allocation of the largest given to the ISAT (PT. Indosat, Tbk) and the allocation of funds smallest given to stock TBIG (PT. Tower Bersama Infrastructure Tbk). While the losses or the estimated risk of the portfolio at 95% confidence level is IDR 18,860,237.00 of the initial capital of IDR 1,000,000,000.00 during the holding period 1 day after portfolio formation. Keywords: Stock, Portfolio, SMInfra18, CAPM, Monte Carlo

Page 21 of 74 | Total Record : 733

19 20 21 22 23

Filter by Year

2012 2024

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

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