Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal TEKNIK INFORMATIKA JOIN (Jurnal Online Informatika)
Sopian, Annisa Mufidah
Unknown Affiliation
Computer Science & IT

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

Found 2 Documents
Search

 1 
Voice Spoofing Classification Using Residual Bidirectional Long Short Term Memory Kasyidi, Fatan; Sukma, Rifaz Muhammad; Sopian, Annisa Mufidah; Anbiya, Dhika Rizki
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.43281

Abstract

Voice spoofing attacks are a major security concern for speech-based biometric systems. Detection and classification of spoofed voice are essential steps for preventing unauthorized accesses. This study proposes a novel approach to voice spoofing classification using a Residual Bidirectional Long Short Term Memory (R-BLSTM) network. The goal is to enhance the accuracy and robustness of voice spoofing detection using the power of deep learning and residual connections. The current proposed approach based on bidirectional LSTM with residual connections is designed to capture long-range dependencies and latent characteristics of speech signals. Experimental evidence that the R-BLSTM model is superior to classic ML techniques is also demonstrated by observing an accuracy of 95.6% on the ASVspoof 2019 collection. The designed system can be further utilized for enriching the security of speech-based biometrics modalities and making anti-voice spoofing attacks ineffective.
SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis Sopian, Annisa Mufidah; Ilyas, Ridwan; Kasyidi, Fatan; Hadiana, Asep Id
JOIN (Jurnal Online Informatika) Vol 9 No 1 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i1.1275

Abstract

Twitter is a popular social media in Indonesia, and sentiment analysis on Twitter has an important role in measuring public trust, especially in taxation issues. Aspect extraction is an important task in sentiment analysis. In this research, we propose SAER, a Syntactic Aspect-opinion Extraction and Rule prediction, that used language rule-based approach using syntactic features for aspect and opinion extraction, and we compare several algorithm for rule prediction such as Random Forest Regression, Decision Tree Regression, K-Nearest Neighbor Regression (KNN), Linear Regression, Support Vector Regression (SVR), and Extreme Gradient Boosting Regression (XGBoost) that can generate rules with a tree-based approach. By employing syntactic features and rule prediction, it has been able to explore important features in a sentence. In rule prediction, comparison results show that Support Vector Regression (SVR) was identified as the most effective model for aspects rule prediction, providing the best results with a Mean Squared Error (MSE) of 0.022, Root Mean Squared Error (RMSE) of 0.150, and Mean Absolute Error (MAE) of 0.123. While XGBoost was identified as the most effective model for opinions rule prediction, with MSE of 0.013, RMSE of 0.117, and MAE of 0.075. Since we used syntactic feature-based approaches and rule prediction in this work, it is expected to be implemented for other cases, with other domain datasets.
Co-Authors Anbiya, Dhika Rizki Hadiana, Asep Id Kasyidi, Fatan Ridwan Ilyas Sukma, Rifaz Muhammad