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Tiani Wahyu Utami
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Sekretariat Jurnal Statistika Universitas Muhammadiyah Semarang Program Studi Statistika FMIPA Universitas Muhammadiyah Semarang
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Jurnal Statistika Universitas Muhammadiyah Semarang
Published by Universitas Muhammadiyah Semarang
ISSN : 23383216     EISSN : 25281070     DOI : -
Core Subject : Science,
Decision Sciences, Operations Research & Management
Focus and Scope a. Statistika Teori, Statistika Komputasi, Statistika terapan b. Matematika Teori dan Aplikasi c. Design of Experiment
Arjuna Subject : Matematika - Pemodelan dan Simulasi
Articles 5 Documents
 1 
Search results for , issue "Vol 11, No 1 (2023): Jurnal Statistika Universitas Muhammadiyah Semarang" : 5 Documents clear
AUXILIARY INFORMATION BASED GENERALLY WEIGHTED MOVING AVERAGE FOR PROCESS MEAN Istin Fitriana Aziza; Wirajaya Kusuma; Siti Soraya
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 1 (2023): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.11.1.2023.10-21

Abstract

The univariate mean process monitoring is only used the information from the study variable. One of the univariate control chart that used to monitor the mean process is GWMA control chart. But, in this research, we need to monitor process mean using the information from the study variable and information on the adding or auxiliary variable. The enhanced control chart in this research named AIB-GWMA control chart. In this research, we also made a comparison between the GWMA and AIB-GWMA to know the sensitivity and effectiveness of  these control chart. The comparison is used to know the effect of the auxiliary variable in process monitoring. The performance of these control chart is evaluated using Average Run Length with help of Monte Carlo simulation. The result of this study is AIB-GWMA has a smaller ARL than the GWMA control chart. It showed that AIB-GWMA is faster to detect a shift in mean process. In further study, we recommended to enhance the performance of the AIB-GWMA by extending the current work to the AIB-MaxGWMA, so it is possible to monitor process mean and variance simultaneously.
FORECASTING THE NUMBER OF PASSENGER AT JENDERAL AHMAD YANI SEMARANG INTERNATIONAL AIRPORT USING HYBRID SINGULAR SPECTRUM ANALYSIS-NEURAL NETWORK (SSA-NN) METHOD Tresiani Yunitasari; M. Al Haris; Prizka Rismawati Arum
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 1 (2023): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.11.1.2023.22-33

Abstract

Transportation was an important sector of supporting the economic growth of a country. The impact of the Covid-19 2020 pandemic at Achmad Yani International Airport in Semarang resulted in the movement of the number of passengers decreasing quite drastically, but in mid-2020 the movement of the number of passengers had slowly increased. Forecasting was done to determine the flow of movement of the number of passengers in the future using the Hybrid Singular Spectrum Analysis (SSA)-Neural Network (NN) method. The SSA method was expected to be able to decompose various patterns in the data into trend, seasonality and noise. Furthermore, the NN method was used to analyze nonlinear patterns in the data. The results showed that the best method was a combination of the SSA method with a window length of 40 and the NN method with a 6-8-1 network architecture (6 input neurons, 8 hidden neurons and 1 output neuron) for the trend component, 11-15-1 (11 neurons input, 15 hidden neurons and 1 output neuron) for the seasonal component, and 10-15-1 (10 input neurons, 15 hidden neurons and 1 output neuron) for the noise component. The model produces a prediction error based on a MAPE value of 0.54% or an accuracy rate of 99.46%.
ANALYSIS OF THE EFFECT TOURISM SECTOR AND OPEN UNEMPLOYMENT ON ECONOMIC GROWTH IN BALI PROVINCE Layla Fickri Amalia; Putu Gita Suari Miranti
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 1 (2023): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.11.1.2023.34-44

Abstract

Bali is one of the most popular tourist destinations by domestic and foreign tourists in Indonesia. Because many   tourists visit, many Balinese people are looking for a livelihood in the tourism sector such as being a tour guide, working in the  hospitality sector, culinary, tourist trips etc.  During the COVID-19 pandemic, many workers in the tourism sector lost their jobs, increasing  the open unemployment rate in  Bali Province.  With a high unemployment rate, people's welfare decreases so that it  can affect economic growth in   Bali Province. This study aims to see the Effect of the Tourism Sector and Open Unemployment on Economic  Growth in  Bali Province. The variables of the independent of this study are the number of  tourists visiting, the number of hotel, the  number of travel agencies and the open unemployment rate.  Meanwhile, the dependent variable used is the economic growth of Bali province. The analysis tool used is Panel Data Regression, from the test obtained the value of the coefficient of determination R2 of 65.80%, this shows the magnitude of the influence of independent variables on dependent variables. The results of the study concluded that simultaneously the number of tourists, the number of restaurants, the number of tourist travel agencies, and the unemployment rate influenced economic  growth. This is seen from the prob value of F-statistics of 0.0000. Meanwhile, the results of the t test show that the results are influential and significant for each independent variable against the dependent variable.
MODELING COUNT DATA WITH OVER-DISPERSION USING GENERALIZED POISSON REGRESSION: A CASE STUDY OF LOW BIRTH WEIGHT IN INDONESIA M. Fathurahman
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 1 (2023): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.11.1.2023.45-60

Abstract

Poisson regression is commonly used in modeling count data in various research fields. An essential assumption must be met when using Poisson regression, which is that the count data of the response has the mean and variance must be equal, namely equi-dispersion. This assumption is often unmet because many data for the response that the variance is greater than the mean, called over-dispersion. If the Poisson regression model contains the over-dispersion, then will be produced an invalid model can under-estimate standard errors and misleading inference for regression parameters. Therefore, an approach is needed to overcome the over-dispersion problem in Poisson regression. The generalized Poisson regression can handle the over-dispersion in Poisson regression. This study aims to obtain the generalized Poisson regression model and the factors affecting the low birth weight in Indonesia in 2021. The result shows that the factors affecting the low birth weight in Indonesia based on the generalized Poisson regression model were: poverty rate, percentage of households with access to appropriate sanitation, percentage of pregnant women at risk of chronic energy deficiency receiving additional food, percentage of pregnant women who received blood-boosting tablets, and percentage of antenatal care.
FOURIER SERIES APPLICATION FOR MODELING “CHOCOLATE” KEYWORD SEARCH TRENDS IN GOOGLE TRENDS DATA Andrea Tri Rian Dani; Fachrian Bimantoro Putra; Muhammad Aldani Zen; Vita Ratnasari; Qonita Qurrota A'yun
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 1 (2023): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.11.1.2023.1-9

Abstract

In some cases of regression modeling, it is very common to find a repeating pattern. To model this, of course, the approach used must be in accordance with the characteristics of the data. The Fourier series is one of the proposed approaches, because it has advantages in modeling relationship patterns that tend to repeat, such as cosine sine waves. The Fourier series is a subset of nonparametric regression, which has good flexibility in modeling. In this study, the Fourier series approach was applied to model search trend data for the keyword "Chocolate" sourced from Google Trends. Generalized Cross-Validation (GCV) is used as model evaluation criteria. Based on the results of the analysis, the best Fourier series nonparametric regression model is obtained with the number of oscillations of five, which is indicated by the minimum GCV value.

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