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All Journal International Journal of Evaluation and Research in Education (IJERE) Journal on Education
Albert Albert
Universitas Mercu Buana
Education

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Early tax education: Could it change the future compliance behavior? Albert Albert; Rien Agustin Fadjarenie
International Journal of Evaluation and Research in Education (IJERE) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v11i4.22241

Abstract

Many developing countries face the challenges of low tax compliance behavior due to the self-assessment system. Therefore, the purpose of this study was to obtain empirical evidence about the implication of early tax education toward the changes in future taxpayers’ compliance behavior. Previous research found that early tax education can develop the compliance paradigm in human behavior. This study employed a quantitative research method with a case study approach. Data was gathered through a survey of 719 students of junior and senior high school in Jakarta, Indonesia as respondents, and divided into two groups: those who receive tax learning at school and those who did not. The results revealed that early tax education positively affects taxpayers’ compliance behavior, especially for those who had received tax learning. This study reports preliminary findings in tax education and fills the gap of research in this area. The research contributes to learning and behavior studies and sheds light on implementing early tax education to resolve non-compliance tax behavior.
Analisis Topik dan Perbandingan Klasifikasi pada Kolom Komentar Video Youtube Edukasi Indonesia Menggunakan Pendekatan Latent Dirichlet Allocation Albert Albert
Journal on Education Vol 5 No 3 (2023): Journal on Education: Volume 5 Nomor 3 Tahun 2023
Publisher : Departement of Mathematics Education

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Abstract

There are numerous Indonesian YouTube channels with educational themes and a range of topics. However, a study based on the correlation between the comments column, the title, and the video was done to assess the channel's quality. The goal of this research is to determine whether the educational-themed channel that has been chosen as the study's subject has a relevant emphasis and whether it is connected to any current titles. Data from YouTube comments that were scraped using Python are used in this investigation. Data pre-processing was done to clean up the data obtained from audience remarks in order to speed up the procedure. We test predictions for sentiment analysis using the SVM and Decision Tree algorithms. A dependable technique for grouping or summarizing a huge text is Latent Dirichlet Allocation. The Bag of Words approach is then used to turn the comments into a corpus by using them as tokens and vectorizing them. The topic will show up following the creation of the LDA model. The amount of match between the word fragment and the generated subject was then calculated using the coherence value. The best combination of subjects from each channel is then determined in order to get a better coherence value. An even distribution of subjects is produced using LDA.
Co-Authors Agustin Fadjarenie