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Sciencestatistics: Journal of Statistics, Probability, and Its Application
Published by Universitas Muhammadiyah Metro
ISSN : 29642884     EISSN : 29639875     DOI : https://doi.org/10.24127
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics
Sciencestatistics: Journal of Statistics, Probability, and Its Application is an Open Access journal in the field of statistical inference, experimental design and analysis, survey methods and analysis, research operations, data mining, statistical modeling, statistical updating, time series and econometrics, multivariate analysis, statistics education, simulation and modeling, numerical analysis, algebra, combinatorics, and applied mathematics.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 29 Documents
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Sampling Survey Design Presidential Election Quick Count Sumatera Island Dewi, Wardhani Utami; Warsono, Warsono; Nisa, Khoirin
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 1 No. 1 (2023): JANUARY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v1i1.3162

Abstract

The number of TPS on the island of Sumatra is very large, in order to save time and money in conducting surveys, a sampling survey design was created. The purpose of this study is to predict the results of the presidential election on the island of Sumatra. The TPS sample frame was obtained in four stages where each stage used a sampling technique, namely the first and second stages used stratified random sampling, the third stage used systematic random sampling, and the last used clusters. The results obtained are with different TPS sample sizes showing the same results. The victory in the presidential election on the island of Sumatra was won by candidate pair number two. Then compared with the overall TPS population in Sumatra. Based on the population, the second candidate pair is also superior. So it can be concluded that the use of a survey sampling design in this study is appropriate in predicting the results of the elected president election.
Analysis of Linear Log Models on Covid-19 Data in Indonesia Suciati, Indah; Warsono, Warsono; Usman, Mustofa
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 1 No. 1 (2023): JANUARY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v1i1.3163

Abstract

Covid-19 is still a concern of the world, including Indonesia. The transmission of Covid-19 is very fast and has a wide impact on all people around the world, especially Indonesia. In everyday life, we find a lot of data that looks into a certain category. Categorical analysis of data can be done using the log linear model. The log linear model is used to analyze the relationship between categorical variables that form a contingency table of arbitrary dimensions. The analysis used in this study is to make descriptive statistics and three-way contingency tables, then perform the analysis with the help of SPSS 25.0 software where the goodness of fit test is used to see which models can be used or suitable. The purpose of this study is to analyze a log linear model, so that a log linear model is obtained that is suitable for Covid-19 data based on gender, province, and age group. The conclusion of this study is that of the 9 models used, the model is the most suitable model to be used, with a value of 18,885 and the equation of the log linear model is , which means that there is a relationship between the two factors for the variables gender and province, gender and age group and province and age group in Covid-19 cases in Covid-19 in Indonesia by gender, province, and age group.
Perbandingan Model Gompertz, Logistic, dan Weibull pada Data Kasus Meniggal Pasien Covid-19 di Indonesia Irfan, Miftahul; Warsono, Warsono
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 1 No. 1 (2023): JANUARY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v1i1.3179

Abstract

Covid-19 merupakan pandemic yang sudah melanda dunia sejak akhir tahun 2019. Covid-19 mulai melanda Indonesia pada awal tahun 2020. Pada awal periode, pertumbuhan pandemic covid-19 ini sangat cepat. Bahkan rasio kasus meninggal akibat covid-19 ini tergolong cukup tinggi. Jika dilihat dari kurva pertumbuhan kasus meninggalnya, nampak bahwa kurvanya melandai pada awal namun menaik signifikan setelahnya. Oleh karena itu artikel ini mengimplementasikan model non-linear yang dalam hal ini menggunakan model gompertz, model logistic, dan model Weibull pada pertumbuhan kasus meninggal akibat covid-19 di Indonesia pada awal kemunculannya yaitu periode April 2020 sampai Maret 2021. Kemudian dari ketiga model itu akan dipilih model terbaik dengan membandingkan nilai R-Square dari masing-masing model. Model yang memiliki R-Square terbesar menandakan model yang paling baik digunakan. Setelah dilakukan pemilihan ternyata model gompertz memiliki nilai R-Square terbesar yaitu 0,9987, sehingga model yang paling cocok untuk data covid-19 ini adalah model gompertz.
Kurva Pertumbuhan Nonlinier (Gompertz, Logistic, dan Weibull) Juanda, Juanda; Warsono, Warsono
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 1 No. 1 (2023): JANUARY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v1i1.3180

Abstract

Penyebaran COVID-19 sangat cepat bahkan diperkirakan tumbuh secara eksponensial, dikarenakan migrasi manusia antar daerah, negara, bahkan benua yang sangat massif. Oleh karena itu kurva pertumbuhan penderita COVID-19 yang tumbuh dari waktu ke waktu dapat di dekati dengan fungsi eksponensial selama beberapa peubah prediktor diketahui. Tujuan dari penelitian ini adalah memodelkan dan membandingkan kurva pertumbuhan dengan metode non-linear. Metode yang digunakan adalah metode non-linear dengan model gompertz, logistics, Weibull. Hasil yang diperoleh model gompertz memiliki nilai R-Sqaure yang lebih tinggi bandingkan dengan model logistic dan Weibull. Sehingga dapat disimpulkan bahwa model gompertz menjadi model non-linear yang terbaik dalam menginterpretasikan kurva pertumbuhan Covid-19.
Penerapan Analisis Regresi Robust dalam Penentuan Faktor Dominan Cuaca Terhadap Penyebaran Covid-19 di Jawa Barat Pradana, Aditya Putra; Nisa, Khoirin
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 1 No. 1 (2023): JANUARY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v1i1.3181

Abstract

Penyebaran Corona Virus Disease-2019 (COVID-19) semakin mengkhawatirkan di dunia, khususnya di provinsi Jawa Barat, Indonesia. Selain penularan dari manusia ke manusia, parameter meteorologi dianggap menjadi faktor efektif dalam penyebaran virus tersebut. Parameter meteorologi tersebut diantaranya terkait dengan cuaca dan iklim di suatu daerah. Melalui kajian Badan Meterologi, Klimatologi, dan Geofisika (BMKG) Indonesia ditemukan bahwa cuaca dan iklim merupakan faktor pendukung terjadinya wabah COVID-19 sehingga sangat cocok apabila dilakukan penelitian dan analisis mengenai faktor yang paling berpengaruh terhadap penyebaran COVID-19. Pada penelitian ini, bertujuan untuk mengetahui faktor-faktor dominan apa saja yang mempengaruhi penyebaran COVID-19 di provinsi Jawa Barat. Metode analisis yang akan digunakan yaitu metode S-estimator yang merupakan salah satu metode analisis regresi robust, sebab terdapat data yang merupakan pencilan sehingga distribusi dari residu tidak normal. Data yang digunakan diambil dari situs resmi Badan Meteorologi, Klimatologi, dan Geofisika Indonesia dan situs Kawal COVID-19 dengan variabel bebas yaitu suhu, kelembaban udara, lama penyinaran matahari, kecepatan angin, dan curah hujan, serta variabel terikatnya yaitu jumlah penderita positif COVID-19. Software dalam analisis tersebut menggunakan software IBM SPSS Statistic versi 25dan software R 3.4.3. Berdasarkan hasil penelitiannya, terlihat bahwa nilai R-square (adjusted) dengan regresi robust S-estimator sebesar 56,79% dan variabel bebas suhu dan lamanya penyinaran matahari mempengaruhi variabel jumlah penderita positif COVID-19 sehingga dapat disimpulkan bahwa kedua variabel bebas tersebut merupakan faktor dominan yang mempengaruhi penyebaran COVID-19 di Jawa Barat.
Goodness Of Fit Test In Structural Equation Modeling with Unweighted Least Square (ULS) Estimation Method Amanathi, Ani; Setiawan, Eri; Usman, Mustofa
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 1 No. 2 (2023): JULY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v1i2.5021

Abstract

Structural equation model (SEM) is a multivariate analysis method that is used to describe a linear relationship simultaneously between indicator variables and latent variables. There are several estimation methods in SEM, one of them is Unweighted Least Square (ULS). The method doesn‟t have specific assumptions about the distribution of variables. This study aims to estimate the model using the ULS method and see the influence of employee competency variables and library facilities on the quality of service at the University of Lampung library. Survey of quality of service in the library of Lampung University is used in the research. Based on the results of the study, it is found that from the three suitability tests, namely the overall model test, the structural model test and the measurement model test using ULS estimation give good results in explaining the compatibility between the model and observation results.
Log-Linear Model on Categorical Data of HIV Cases Dewi, Wardhani Utami; Warsono, Warsono
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 1 No. 2 (2023): JULY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v1i2.5022

Abstract

Categorical data is widely used in social, health, educational and psychological research. A contingency table is a form of presenting this data. One of them is about cases of being infected with the HIV virus. The log-linear model is an alternative for analyzing categorical data. In this study, HIV cases will be analyzed using a log-linear model grouped by gender, age and province. Apart from that, several log-linear models will be formed and the best model will be selected based on the likelihood ratio (G^2) statistical test. According to the results of the analysis and consideration of model complexity, (JK*P, JK*U, P*U) is the best model and fits the data because the p-value = 0.517 is greater than the real level α = 0.05. This means that the interaction between gender, age and province is significant. Studies and explanations about the HIV virus show that individuals between the ages of 25-49 years are more at risk of being infected with the virus. Examined by gender group, women were most infected with the virus, namely 513 people. Apart from that, West Papua is the province with the highest number of HIV infections compared to Maluku and North Maluku
Bayesian Structural Time Series Model for Forecasting the Composite Stock Price Index in Indonesia Suciati, Indah; Usman, Mustofa
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 1 No. 2 (2023): JULY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v1i2.5023

Abstract

One of the models that can be used to predict time series data is the Bayesian Structural Time Series (BSTS) model. The BSTS model is a more modern model and can handle data movement better. In the BSTS model, the Markov Chain Monte Carlo (MCMC) sampling algorithm is used to simulate the posterior distribution, which smoothes the forecasting results over a large number of potential models using Bayesian averaging models. The purpose of this study was to obtain the best BSTS model for Composite Stock Price Index (CSPI) data in Indonesia based on the state component and the number of MCMC iterations, and obtain forecasting results for CSPI value in Indonesia for the next 24 months, namely the period July 2023 to June 2024. The results obtained are based on a comparison of the R-square values in the model, the BSTS model with local linear trend and seasonal state components, and the number of MCMC iterations n = 5 00 is the best BSTS model that can be used for forecasting the CSPI value in Indonesia with an R-square value of 99.96%. The results of forecasting the CSPI value in Indonesia for the period July 2023 to June 2024 range from 6589 to 6760, with the lowest forecasting value in October 2023 and the highest in March 2023.
Maximum Likelihood Estimation Approach using the CB-SEM Method: Case Study of Service Quality Oktavia, Putri Meyla; Setiawan, Eri; Nusyirwan, Nusyirwan; Herawati, Netti
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 1 No. 2 (2023): JULY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v1i2.5031

Abstract

The purpose of this study to analyze the service level, satisfaction and loyalty of Sariringgung Beach visitors.Covarian based approach to estimatemaximum likelihood method in the service levelto the satisfaction and loyalty of visitorstourism area of Sariringgung Beach is used. The results of this study indicate that the direct effect of service level to satisfaction is 77%, service level to loyalty is 75%. Whereas the indirect effect of service level on loyalty through satisfaction is 38,5%. Then the total effect of service level on customer loyalty through satisfaction is 73,5%.
Artificial Neural Network (ANN) Classification: Titanic Passenger Safety Juanda, Juanda; Nisa, Khoirin
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 1 No. 2 (2023): JULY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v1i2.5074

Abstract

Scientific and technological innovation has always been the main driver of economic growth and social progress. The rapid development of technology and advances in the internet have made it possible to disseminate information and interact more easily. With the rapid development of technology, a lot of information is shared every second, resulting in big data in terms of different, complex variables. ANN is the result of work in the computer field that is inspired by the capabilities of the human brain which consists of biological neural networks. In recent years, the use of artificial neural networks (ANN) has increased. The research carried out aims to analyze the survival capabilities of Titanic passengers who experienced an accident while sailing and sank. This research uses initial data of 1309 observations with 14 variables. From the research results, 2 hidden variables are the most accurate with an accuracy of 80.5%, compared to the number of hidden variables of 3 (79%) and 4 (79%). So it can be concluded that the number of hidden variables with the same number of hidden screens does not have a significant difference in accuracy

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