Article Per Year (5 Year)
p-Index From 2019 - 2024
0.408
P-Index
This Author published in this journals
All Journal Parameter: Journal of Statistics
Nur'eni
Tadulako University
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
SENTIMENT ANALYSIS OF REVIEW DATA OF THE RUANGGURU ONLINE LEARNING APPLICATION USING THE C5.0 ALGORITHM Nurul Izzah; Nur'eni; Rizka Pitri
Parameter: Journal of Statistics Vol. 3 No. 2 (2023)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2023.v3.i2.16919

Abstract

Sentiment analysis is process to determine the sentiment of a person that is manifested in the form of text. Internet users write their opinions and everything that concerns them in the google play store review column. Moreover, when the world of education could not carry out face-to-face learning due to the covid-19 pandemic, learning turned to e-learning applications. Through this innovation, many pros and cons flow from the community with the existence of Ruangguru online learning application in the world of education. This research was conducted with the aim of determining word cloud visualization and the accuracy of the results of sentiment analysis of review data on the Ruangguru application using the C5.0 algorithm. The word cloud visualization results are dominated by word such as “paham”, “bagus”, “mudah”, “suka”, “langganan”, “seru”, “nyaman”, “senang”, “menarik”, “keren”, “lancar”, “sukses”. This shows that Ruangguru Application is a good application because it is dominated by positive sentiment words which means that users find it helpful and easy to understand the learning material in Ruangguru. The results of the Confusion Matrix show that the model successfully classifies 0.8557 or 85.57% of the data correctly from all test data
REGRESSION ANALYSIS OF ROBUST ESTIMATION-S WITH TUKEY BISQUARE WEIGHTING ON POVERTY LEVEL ON SULAWESI ISLAND Sandra Saputri; Nur'eni; Sitti Masyitah Meliyana R
Parameter: Journal of Statistics Vol. 3 No. 2 (2023)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2023.v3.i2.16923

Abstract

Poverty is a situation where a person experiences difficulty in meeting basic needs. There are several factors that influence poverty, including population, unemployment, gross regional domestic product, human development index, average years of schooling and labor force participation rate. Therefore, it is necessary to carry out regression analysis to determine the relationship between one variable and other variables. One method for estimating regression parameters is the least squares method. Some classic assumptions are not met because there are outlier data. Outliers are data that do not follow the overall distribution pattern, so a method is used that can overcome outliers, namely the S-estimation robust regression method with the Tukey bisquare weighting function. The results of the research show that the best model was obtained from robust S-estimation regression with Tukey bisquare weighting, namely factors that influence the level of poverty on the island of Sulawesi, namely Population Number ), Human Development Index ( ), Average Years of Schooling ( ) and, Force Participation Level. Work .
Co-Authors Nurul Izzah Rizka Pitri Sandra Saputri Sitti Masyitah Meliyana R.