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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 5 Documents
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Search results for , issue "Vol. 2 No. 1 (2024): JANUARY" : 5 Documents clear
Naive Bayes Classification of Bullying and Non-Bullying Comments in Instagram Social Media Posts Naflah Faulina
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 2 No. 1 (2024): JANUARY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v2i1.5527

Abstract

Machine learning is a type of artificial intelligence that provides computers with the ability to learn from data. There are three main branches of machine learning, namely supervised machine learning, unsupervised learning, and reinforcement learning. One of the categories in supervised machine learning is classification. Classification is the process of assessing a data object where the object is put into a certain class from the number of classes available. An example of a classification algorithm is Naive Bayes with classification using probability and statistical methods. This algorithm is used to classify Bullying and Non-bullying with a division of training data and testing data, namely 60:40, 70:30, and 80:20 resulting in the best accuracy value for training data and testing data 60:40 of 66%.
Loglinier Model on Immunization of Baduta Juanda, Juanda
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 2 No. 1 (2024): JANUARY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v2i1.5529

Abstract

This study aims to analyze the relationship between the type of immunization and the province of origin of children under two years of age (BADUTA) in Indonesia in 2020. This study uses a loglinear model approach to analyze DPT-HB-Hib4 and Measles/MR2 immunization data based on gender and home province. Data was obtained from the Directorate General of Disease Prevention and Control of the Indonesian Ministry of Health. Through statistical analysis using IBM SPSS Statistics 22 software, this research found that there is a relationship between the type of immunization and the child's province of origin. The chi-square test results show that there is a significant relationship between the type of immunization and the child's province of origin. Of the several loglinear models tested, the (J, VP) model is considered the best model based on Goodness-of-fit criteria. Thus, this study concludes that there is a relationship between the type of immunization and the province of origin of children under two years old in Indonesia in 2020.
Partial Derivatives of Gompertz, Logistic, and Weibull Non-Linear Growth Models on Confirmed COVID-19 Cases Utami Dewi, Wardhani; Warsono
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 2 No. 1 (2024): JANUARY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v2i1.5641

Abstract

. The epidemiological picture of COVID-19 is still unknown, and the number of confirmed cases of COVID-19 varies every day. Researchers have studied COVID-19 a lot, and many of them have used statistical models to estimate the growth of the outbreak. Non-linear statistical models can be used to describe growth behavior, as it varies in time. The aim of this research is to analyze, compare, and find the best model from the Gompertz, Logistic, and Weibull non-linear models. Daily cumulative data on confirmed COVID-19 viruses in Indonesia for 2020-2021 will be used in this research. The results obtained by the Logistic model proved to be very effective in describing the COVID-19 epidemic curve and estimating epidemiological parameters. The Logistic Model provides the best results compared to other growth models applied by Gompertz and Weibull. The R-Square of the logistic model is 0.9990, meaning that the model is able to explain or predict 99.90% of the data and 0.10% is explained by other factors. However, this research cannot explain the turning point of the curve, because there are many factors other than the model. One of them is the nature of the virus carrier from one place to another, then the behavior of the carrier who has not fully implemented the health protocol rules.
Factor Analysis on Possum Dataset to Simplify Many Independent Variables Into Fewer Factors Larasati, Ayu; Suminto
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 2 No. 1 (2024): JANUARY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v2i1.5649

Abstract

This research aims to apply factor analysis to possum data with the aim of simplifying many independent variables into fewer factors. The factor analysis steps begin by grouping the variables to be analyzed and compiling a correlation matrix using the Bartlett test and the Kaiser-Meyer-Olkin (KMO) test. From the test results, it was found that the variables had sufficient correlation to proceed to factor analysis. After that, factor extraction was carried out using three criteria, namely eigenvalues, diversity percentage, and scree plot, which concluded that the number of factors formed was two. Next, factor rotation was carried out using the varimax method to simplify interpretation. The results show that certain variables have high loadings on certain factors, making it easier to identify patterns. In conclusion, factor analysis succeeded in simplifying the relationship between variables into two factors that can be interpreted more easily.
Penerapan Metode Geographically Weighted Panel Regression Pada Indeks Pembangunan Manusia di Indonesia Tahun 2017-2022 Deta Erviana; Mustofa Usman; Widiarti; Khoirin Nisa
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 2 No. 1 (2024): JANUARY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v2i1.5669

Abstract

Regresi linier merupakan metode statistik untuk memeriksa hubungan antara variabel respons dan satu atau lebih variabel prediktor. Dalam sebuah penelitian, satu unit observasi harus diteliti selama beberapa periode waktu, karena mempelajari satu unit dalam satu periode waktu tidaklah cukup. Oleh karena itu, sebuah pendekatan statistik yang disebut analisis regresi panel diciptakan untuk mengintegrasikan data cross-section dan data time series. Namun pada kenyataannya, perbedaan kondisi antar lokasi dipengaruhi oleh efek spasial yang menyebabkan terjadinya heterogenitas spasial. Dikembangkanlah metode Geographically Weighted Regression (GWR) untuk mengatasi masalah heterogenitas spasial. Berdasarkan kelebihan kedua metode tersebut maka berkembanglah suatu metode yang menggabungkan antara regresi data panel dan GWR yaitu Geographically Weighted Panel Regression (GWPR). Tujuan dari penelitian ini adalah untuk mengetahui faktor-faktor yang mempengaruhi indeks pembangunan manusia (IPM) di Indonesia tahun 2017-2022 dan menentukan model terbaik dengan membandingkan model regresi global dan GWPR. Model GWPR dengan pembobot adaptive bisquare merupakan model terbaik dengan nilai AIC terkecil dan R^2 terbesar. Secara keseluruhan semua variabel prediktor yang digunakan dalam penelitian berpengaruh signifikan terhadap IPM pada taraf signifikansi α=0,05. Persamaan model dan variabel yang berpengaruh signifikan yang dihasilkan dalam pemodelan GWPR berbeda untuk setiap provinsi. Berdasarkan kesamaan variabel yang mempengaruhi IPM di provinsi yang letaknya berdekatan membentuk 8 kelompok.

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