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Joko Sungkono
Universitas Widya Dharma Klaten

Published : 10 Documents
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### Found 3 Documents Search Journal : ABSIS : Mathematics Education Journal

PEMBELAJARAN TEORI PROBABILITAS MENGGUNAKAN R Joko Sungkono; Kriswianti Nugrahaningsih
Absis: Mathematics Education Journal Vol 2, No 1 (2020): Mei 2020
Publisher : Universitas Veteran Bangun Nusantara

#### Abstract

In learning probability theory, if a series of statistical experiments is carried out several times, identifying the possible samples produced is not an easy thing. If this happens, then a significant probability problem will arise. The objective of this study is how to learn probability theory using R software. Based on the simulation results it can be concluded that by combining the syntax in R can be used to solve probability problems such as identifying sample points from experiments, events, event operations, probabilities and conditional probabilities. This will help students learning in understanding of the probability theory material. The use of R will be very pronounced for experiments with a large enough scale that results in a large sample probability.
Efektivitas Penggunaan Worksheet R Dalam Pembelajaran Teori Probabilitas Joko Sungkono; Andhika Ayu Wulandari
Absis: Mathematics Education Journal Vol 3, No 2 (2021): November 2021
Publisher : Universitas Veteran Bangun Nusantara

#### Abstract

Experiments that are often used to understand the theory of probability include the toss of currency, rolling of the dice, taking the ball randomly from a box, and drawing a bridge card. If this series of experiments is carried out several times, identifying possible samples produced is not an easy task. Learning probability theory material requires a new breakthrough in order to make it easier for students to understand according to their era. This research applies the use of R-based student worksheets (R worksheets) in learning probability theory. This paper discusses the effectiveness of using the R worksheet in learning probability theory. This research involved the experimental class and the control class. The experimental class used students in the Mathematics Statistics I course for Mathematics Education Study Program, Widya Dharma University, Klaten, while the control class used students in the Mathematics Statistics I course for Mathematics Education Study Program, Bangun Nusantara University, Sukoharjo. The experimental class used the R worksheet with R software-based learning, while the control class used the conventional method. From the pre-test result data, it is known that the 2 sample classes have the same initial ability. Based on the data analysis of the post-test scores, it is known that the experimental class scores are better than the control class. This means that the use of the R worksheet is effective in learning probability theory.
Pembelajaran Teorema Limit Pusat Melalui Simulasi Joko Sungkono; Andhika Ayu Wulandari
Absis: Mathematics Education Journal Vol 4, No 2 (2022): November 2022
Publisher : Universitas Veteran Bangun Nusantara

#### Abstract

The mathematical learning of the central limit theorem has been widely discussed in scientific writings by researchers through various versions of proofs. The discussion of the central limit theorem in case application has also been carried out with many different cases. However, students need to be given an overview of the truth of the central limit theorem through a general application. The truth and accuracy of the central limit theorem can be studied through a simulation study. Through simulation with R software, students can perform parameter variations such as variations in the population distribution, variations in the sample size used, as well as the number of repetitions or replications in studying the central limit theorem. The accuracy of the central limit theorem through simulation is determined by looking at the trend of the sampling distribution of the mean sample in the form of a histogram. The simulation results state that, in general, the larger the sample size used, the closer the sampling distribution to the mean sample is to the normal distribution. For samples taken from a population that has a distribution that is closer to symmetrical, then for a sample size that is not too large, the distribution of the mean sample is closer to a normal distribution. However, for samples originating from an asymmetric distribution, a larger sample size is required to obtain a sample mean that is close to the normal distribution