Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Engineering, Mathematics and Computer Science Journal (EMACS)
Purwandari, Kartika
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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

Found 2 Documents
Search

 1 
Sampling methods in handling imbalanced data for Indonesia health insurance dataset Kurniadi, Felix Indra; Purwandari, Kartika; Wulandari, Ajeng; Permai, Syarifah Diana
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp348-357

Abstract

Health insurance fraud is one of the most frequently occurring fraudulent acts and has become a concern for every insurance. According to data from The Indonesian General Insurance Association or Asosiasi Asuransi Umum Indonesia (AAUI), the private insurance industry suffered losses up to billions rupiah throughout 2018 due to the fraudulent acts commited by the perpetrators. The problem in with the number of frauds in Indonesia is that the current system is highly vulnerable and they is still done manually. The other problem from this detection is imbalance data which often occurs in fraudulent cases. In this research, we used a sampling methods using several machine learning as the baseline. The result shows that the instance hardness thresholding algorithm and extreme gradient boosting gives the best performance for all the case. It shows the method can reduced the bias and can achieve better generalization.
Comparison of IndoBERT and SVM Algorithm to Perform Aspect Based Sentiment Analysis using Hierarchical Dirichlet Process Octarini, Sheila Prima; Zakiyyah, Alfi Yusrotis; Purwandari, Kartika
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 3 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i3.13493

Abstract

Analyzing the performance of SVM and IndoBERT models for aspect-based sentiment analysis on fashion reviews in Tokopedia E-Commerce. This study employs the SMOTE technique due to the imbalance in the original data. Aspect determination using the Hierarchical Dirichlet Process (HDP) model yields satisfactory results with an adequate coherence score. The comparison between SVM and IndoBERT methods for aspect-based sentiment analysis shows that SVM is superior. IndoBERT achieved an accuracy of 87%, precision of 91%, recall of 93%, and F1-Score of 92%, while SVM attained an accuracy of 96%, precision of 100%, recall of 92%, and F1- Score of 96%. Therefore, the SVM model was chosen for implementation on a website that allows users to view aspect-based sentiment analysis on products in E-Commerce. The HDP model effectively grouped related terms into aspects such as “Material,” “Shipping,” and “Colour,” enhancing interpretability in sentiment classification. The resulting website enables users to analyze product sentiments interactively, providing actionable insights for both sellers and customers to assess product quality and service satisfaction more efficiently.
Co-Authors Ajeng Wulandari Kurniadi, Felix Indra Octarini, Sheila Prima Syarifah Diana Permai Zakiyyah, Alfi Yusrotis