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

36 37 38 39 40

ANALISIS LAMA KAMBUH PASIEN HIPERTENSI DENGAN SENSOR TIPE III MENGGUNAKAN REGRESI COX KEGAGALAN PROPORSIONAL (Studi Kasus di RSUD Kartini Jepara) Ishlahul Kamal; Triastuti Wuryandari; Hasbi Yasin
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Hypertension is a disease that silently kills the patients because they do not realize that they get hypertension until they check their blood pressure. It is important for hypertensive patients to know the factors that lead to the relapse time. To determine the relationship between the relapse time on hypertensive patients with the influencing factors is using regression analysis, the dependent variable is the failure time so to determine the relationship is using regression Cox proportional hazard. This research uses the medical records of hypertensive patients in period January to December 2014 in RSUD Kartini Jepara. The results indicate that the factors which affect relapse time of hypertension are kidney disease and stroke. The hypertensive patients that also suffer from kidney disease have relapse time sooner than patients who do not suffer from kidney disease. The hypertensive patients that also suffer from stroke have relapse time sooner than patients who do not suffer from a stroke. Keywords: Hypertension, Survival Analysis, Regression Cox Proportional Hazards

OPTIMASI VALUE AT RISK RETURN ASET TUNGGAL DAN PORTOFOLIO MENGGUNAKAN SIMULASI MONTE CARLO DILENGKAPI GUI MATLAB Astuti, Nur Indah Yuli; Tarno, Tarno; Yasin, Hasbi
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Value at Risk (VaR) is a scale that can measure the maximum loss that may happen for a specified period of time in the normal market conditions at a certain level of confidence. The most important thing in the VaR is to determine the type of methodology and assuming appropriate with the distribution of the return. One of the methods in calculating the VaR is Monte Carlo simulation. VaR with Monte Carlo simulation method assumes that the return value is normal distribution simulated using the appropriate parameters and portfolio return is linier towards its single asset return. From the results and analysis research conducted use GUI Matlab, VaR single asset of value risk on the stock of United Tractors Tbk (UNTR) is greater than Bank Rakyat Indonesia (Persero) Tbk (BBRI), Astra International Tbk (ASII), and Bank Negara Indonesia Tbk ( BBNI), VaR value of portfolio consisting of two assets, the three assets, and four assets have lower value than the sum of its single asset of the value of VaR. Matlab (Matrix Laboratory) is an interactive programming system with the basic elements of array database which dimensions do not need to be stated in particular, while the GUI is the submenu of Matlab. In this research, determining the level of trust and specified time period is very important to count of VaR value because it can describe how much investors bear the risk. Keywords: Value at Risk, time period, confidence level, Monte Carlo simulation

KETEPATAN KLASIFIKASI STATUS KERJA DI KOTA TEGAL MENGGUNAKAN ALGORITMA C4.5 DAN FUZZY K-NEAREST NEIGHBOR IN EVERY CLASS (FK-NNC) Atika Elsadining Tyas; Dwi Ispriyanti; Sudarno Sudarno
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Unemployment is a very crucial problem that always deal a developing country and affected a national foundation. It used two methods for classifying a employment status on productive society in Tegal City on August 2014, the methods are C4.5 Algorithm and Fuzzy K-Nearest Neighbor in every Class (FK-NNC). C4.5 Algorithm is a way of classifying methods from data mining that use to construct a decision tree. FK-NNC is another classification technique that predict using the amount of closest neighbor of K in every class from a testing data. The predictor variables that used on classifying an employment status are neighborhood status, sex, age, marriage status, education, and a work training. To evaluate the result of classification use APER calculation. Based on this analysis, classification of employment status using C4.5 Algorithm obtained APER = 28,3784% and 71,6216% of accuracy, while FK-NNC methods obtained APER = 21,62% and 78,38% of accuracy. So, it can be concluded that FK-NNC is better than C4.5 Algorithm. Keywords: Classification, C4.5 Algorithm, Fuzzy K-Nearest Neighbor in every Class (FK-NNC), APER

MODEL REGRESI DATA TAHAN HIDUP TERSENSOR TIPE III BERDISTRIBUSI LOG-LOGISTIK Ibnu Athoillah; Triastuti Wuryandari; Sudarno Sudarno
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Lifetime T is the time from initial treatment until the first response is to be observed which can be death due to a particular disease, illness that recur after treatment or the emergence of new diseases. In research of survival testing the data term are censored and not censored. Censored observation occur if the survival time of the observed individual is not known with certainty while the observation not censored if the survival time of observation is known with certainty. There are three different types of censoring observation that are type I,type II, and type III. Censoring type III is an observation made to several individuals at different time within a certain period, this is because an individual entry into the observations at different times. Influence of other factros on the response variable that is survival time relation should be considered. One way to know relationship is through a regression model. Regression model of survival data with censoring type III of log-logistic distribution is made following the curve of the response variable. Estimation of parameters using maximum likelihood methods. Regression model was apllied to estimate the survival time of patients with lung cancer for factors of the infected cell and type of treatment.

PENGGUNAAN METODE PERAMALAN KOMBINASI TREND DETERMINISTIK DAN STOKASTIK PADA DATA JUMLAH PENUMPANG KERETA API (Studi Kasus : KA Argo Muria) Titis Nur Utami; Abdul Hoyyi; Agus Rusgiyono
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The amount of the data of KA Argo Muria indicates the improve in every year during Ied mubarak day. Ied Mubarak day follows the Hijriyah calender, this is inditates that there is case effect of variation on the calender. The aims of this research is to predict the amount of the KA Argo Mulia passanger of destination of Semarang – Jakarta for 12 periodes in the future by using forecasting time series model of variation calender. The data used mounthly amount data KA Argo Mulia at PT KAI DAOP IV Semarang in the periode of January 2014 until Desember 2015. The result of the data analysis shows significant variable toward the model is and the model of Autoregressive Integrated Moving Average (ARIMA) (1,0,0). Based on the result of forecasting out-sample data, is gained Mean Absolute Percentage Error (MAPE) is 1,8089 % which indicates that the result of forecasting is very good.Keywords: deterministic trend, calender variation, time series, stochastic model, dummy regression.

ANALISIS PELAYANAN SERVIS DI BENGKEL NASMOCO CABANG SOLO BARU DENGAN METODE ANTRIAN Fatma Septy Deviana; Sugito Sugito; Moch. Abdul Mukid
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

World automotive in Indonesia has grown and has a very tight competition. As a company that is in the automotive world and is one of sole agent Toyota in Indonesia to Central Java and Yogyakarta, Nasmoco Solo Baru branch have service and parts facility. As a service provider, Nasmoco Solo Baru branch seeks to serve customers well according to the arrival rate of each customer. Thus, the need to know the measure of system performance on each part on the system service advisor. Queuing system at Nasmoco Solo Baru contained in the Registration Service, Service Parts, and the Cashier Section. Based on the results obtained and the analysis of queuing models are on the Registration Service (M/G/7): (GD/∞/∞) for Monday-Saturday with the booking system and (G/G/7): (GD/∞/∞) for non-booking system, while on Sunday/Holiday booking system model is obtained (M/M/2): (GD/∞/∞) and (M/G/2): (GD/∞/∞) to non-booking system. The model obtained in the service for Monday-Saturday with the booking system and non-booking is (M/G/17): (GD /∞/∞), while on Sunday/Holiday booking system obtained with the model (M/G/9): (GD/∞/ ∞) and (M/M/9): (GD/∞/∞) to the non-booking system. At the cashier queue model for a Monday-Saturday have the same model with a Sunday/Holiday is (M/G/9): (GD/∞/∞). Keywords: Queuing Systems, Nasmoco Solo Baru Branch, Registration Services, Service Parts, Cashier Section.

PEMODELAN VARIABEL-VARIABEL PENGELUARAN RUMAH TANGGA UNTUK KONSUMSI TELUR ATAU SUSU DI KABUPATEN MAGELANG MENGGUNAKAN REGRESI TOBIT Viliyan Indaka Ardhi; Agus Rusgiyono; Alan Prahutama
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Censored data is the data on a dependent variable of which most of the observations are worth less than or equal to zero while others have a certain value or more than zero. Tobit regression model is a statistical model that can overcome the problems in which many independent variables is zero or called data censored. In this research, modeling eggs or milk consumption in Magelang is analyzed using tobit regression. The data used in this research is secondary data derived from Susenas Data Magelang regency 2013. The concluding results of the final modeling shows that the educational level of householder, the amount of expenditure for food in a month, the number of children in the household and the householder’s profession give significant effect on household expenditures for the consumption of eggs or milk with a coefficient determination of is 60,31%. While the remaining 39,69 % is effected by other variables is not examined in this study such as the appetite of consumers and health factors. Keywords: Consumption of Eggs or Milk, Tobit Regression, Censored Data

PENDEKATAN METODE SERVQUAL DAN KLASTER FUZZY K-MEANS UNTUK MENGANALISIS INDEKS KEPUASAN NASABAH BANK X Dewi Erliana; Mustafid Mustafid; Abdul Hoyyi
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Servqual (service quality) is a method for measuring the service quality of each dimension attribute. Servqual dimensions include tangibles, empathy, responsiveness, reliability, and assurance. Cluster analysis is a technique for classifying objects that have the same characteristics in a relatively homogeneous group. One method of cluster is Fuzzy k-Means (FKM). Classification object with FkM, the existence each data point in a cluster is determined by the degree of membership. Determination of the optimum number of clusters using the accuracy measure Xie Beni index (XB). XB index calculation depends on the objective function or distance data to each cluster center, the distance between the center of the cluster, and weighted (fuzzifier). Based on the optimum number of clusters, then will be calculated percentage of Customer Satisfaction Index (CSI). This research is based on a case Bank X customers through questionnaires of customer satisfaction based on the servqual dimension. Result of the research showed the optimum number of cluster 4 and weight 1.1. Customers in cluster 1 was 84.36 % very satisfied with most of the service quality Bank X. Customer in cluster 2 was 72.79 % satisfied, but they think there was nothing more conspicuous of service quality in Bank X. Customer in cluster 3 are satisfied with the value of 77.66 % satisfaction primarily to the ease in submitting a complaint.

PEMILIHAN HELM TERFAVORIT DENGAN MADM BERBASIS GUI MATLAB (Studi Kasus : Pemilihan Helm Terfavorit oleh Mahasiswa FSM Undip, Semarang) Nadya Kiki Aulia; Tatik Widiharih; Abdul Hoyyi
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Safety is an important factor that need to be considered in driving safety. One of important factor that need to be considered is the use of Indonesian National Standard (SNI) helmets. The large number of SNI helmets existance, make consumers especially students, have their own preferences in choosing a helmet. The methods that can be used to choose the most favorite helmet is ELECTRE and TOPSIS. Both of these methods are the methods used to solve MADM problems. There are 8 brands of helmets namely INK, KYT, BMC, HIU, VOG, BOGO, NHK, dan GM. For helmet selection criteria are safety helmets (helmet safety straps when worn), affordable helmet prices, easy found helmet, variety of helmet colors, various sizes available, helmets cover the entire face, comfortable helmet glass when worn, clear helmet glass, quality of the outside of the helmets, helmet foam quality, and resistance to impact. By using ELECTRE method, this research got result that the most favorite helm is INK helmet brand which has the number of row element as much 5. For TOPSIS method, the most favorite helmet is KYT helmet brand with preference value equal to 0.7146. Keywords: ELECTRE, TOPSIS, Helmet, favorite, GUI

PEMODELAN TINGKAT INFLASI INDONESIA MENGGUNAKAN MARKOV SWITCHING AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY Omy Wahyudi; Budi Warsito; Alan Prahutama
Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The financial sector often under conditions of fluctuating due to changes in monetary policy, the political instability even just a rumor. The linear model cannot capture changes in these conditions, so the model used is Markov Switching Autoregressive Conditional Heteroskedasticity (SWARCH). This model produces value of transition probability and the duration of each state. Filtering and smoothing process performed to determine probability of the observation data in each state. Modeling about the inflation data in Indonesia was done. The model used is SWARCH (2.1) with 240 data. The probability of inflation rate switch from non crisis state to crisis state is 0.016621, while the probability of inflation rate switch from crisis state to non crisis state is 0.195719. Expectation value of the length time in non crisis state is 60.16 days and the crisis state is 5.11 days.Keywords : filtering, smoothing, transition probability, SWARCH

Page 38 of 74 | Total Record : 733

36 37 38 39 40

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