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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK)
Jevania, Jevania
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Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Languange, Linguistic, Communication & Media Mathematics Other

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STUDI KORELASI KETERAMPILAN ARGUMENTASI ILMIAH SISWA KELAS VIII SMP NEGERI DI KOTA PONTIANAK Jevania, Jevania; Tenriawaru, Andi Besse
Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) Vol 12, No 12 (2023): Desember 2023
Publisher : Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jppk.v12i12.72418

Abstract

This research refers to Toulmin's Argument Pattern (TAP) that have components including claims, data, evidence, support, qualifications, and disclaimers. This research is conducted with the objective of understanding the relationship or correlation of scientific argumentation skills with each indicator that refers to Toulmin's theory. This research was conducted by distributing test questions based on the Toulmin indicator, carried out through 3 stages, such as preparation, to prepare scientific argumentation questions, the implementation test questions with class VIII students in Pontianak City and reporting to find out the relationship between results of scientific arguments and each Toulmin indicators. The data analysis method employed is a descriptive quantitative analysis approach, which involves presenting the data through score distributions and predefined rating scale categories. Furthermore, the analysis utilizes the SPSS 21 software to determine the correlation between scientific skills and individual indicators. The findings indicated that the relationship among the results of scientific argumentation on the data indicators was 0.028, the relationship with the qualification indicator was 0.191, the relationship with the claims indicator was 0.002, the relationship with the support indicator was 0.000, the relationship with the refutation indicator was 0.003, and relationship with the evidence indicator was 0.029. Based on the value of the relationship on each indicator. It can be reduced that there exists a distinct correlation between each indicator and the outcomes of scientific arguments.
Transformer and text augmentation for tourism aspect-based sentiment analysis Situmeang, Samuel; Tambunan, Sarah Rosdiana; Jevania, Jevania; Simanjuntak, Mastawila Febryanti; Sinaga, Sandro
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4614-4622

Abstract

The 36.98% growth in the quantity of electronic word of mouth (e-WOM) over the past five years presents opportunities for the tourism industry to understand tourists' needs and desires better when analyzed effectively. Aspect-based sentiment analysis (ABSA) is proposed as a solution, as it can identify the sentiment at a more detailed aspect level. Prior research revealed two issues in ABSA: imbalanced datasets and poor performance in representing implicit aspects and opinions. The authors proposed a combination of the bidirectional and auto-regressive transformer (BART) and bidirectional encoder representations from transformers (BERT) models. Leveraging BART capability in modeling context and BERT expertise in modeling text semantics and nuances, the author proposed an ABSA model that combines BART and BERT using the ensemble method. The experimental results reveal that combining these models significantly enhances the performance of the ABSA model, with an F1-score reaching 70%. Furthermore, text augmentation and preprocessing did not bring improvements in ABSA performance.
Co-Authors Sarah Rosdiana Tambunan Simanjuntak, Mastawila Febryanti Sinaga, Sandro Situmeang, Samuel Tenriawaru, Andi Besse