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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Yunita Sari
Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
Computer Science & IT Control & Systems Engineering

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Attention-Based BiLSTM for Negation Handling in Sentimen Analysis Riszki Wijayatun Pratiwi; Yunita Sari; Yohanes Suyanto
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 4 (2020): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.60733

Abstract

Research on sentiment analysis in recent years has increased. However, in sentiment analysis research there are still few ideas about the handling of negation, one of which is in the Indonesian sentence. This results in sentences that contain elements of the word negation have not found the exact polarity.The purpose of this research is to analyze the effect of the negation word in Indonesian. Based on positive, neutral and negative classes, using attention-based Long Short Term Memory and word2vec feature extraction method with continuous bag-of-word (CBOW) architecture. The dataset used is data from Twitter. Model performance is seen in the accuracy value.The use of word2vec with CBOW architecture and the addition of layer attention to the Long Short Term Memory (LSTM) and Bidirectional Long Short Term Memory (BiLSTM) methods obtained an accuracy of 78.16% and for BiLSTM resulted in an accuracy of 79.68%. whereas in the FSW algorithm is 73.50% and FWL 73.79%. It can be concluded that attention based BiLSTM has the highest accuracy, but the addition of layer attention in the Long Short Term Memory method is not too significant for negation handling. because the addition of the attention layer cannot determine the words that you want to pay attention to.
Author Obfuscation on Indonesian News Articles Using Genetic Algorithms Rayhan Naufal Ramadhan; Yunita Sari; Aina Musdholifah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 2 (2021): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.64526

Abstract

Authorship attribution is a method for identifying the author of a text from a group of potential authors and can solve the anonymity of unknown authors. Such method threatens anyone’s privacy, especially those who wish to write anonymously. To address this issue, author obfuscation is proposed to modify a text to disguise its author.In this research, a genetic algorithm-based author obfuscation model was created to modify Indonesian news articles to avoid identification from authorship attribution while keeping its semantics. The model iteratively changed some words in the article using crossover and mutation techniques guided by a fitness function which involve identification probability and similarity to the original article.The model is evaluated based on safety, soundness, and sensibleness parameter. The model has good safety since it can reduce the given authorship attribution model's accuracy by 0.3018 but drops to 0.1179 when tested on different models. Its soundness is pretty good since the similarity of the modified to the original articles reaches 0.7817. The model obtained a score of 2.571 on a scale of 0 to 4 in terms of sensibleness which indicates that some articles are acceptable in terms of grammar, but not a few are messy.
The Effect of Text Summarization in Essay Scoring (Case Study: Teach on E-Learning) Sensa Gudya Sauma Syahra; Yunita Sari; Yohanes Suyanto
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 1 (2022): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.69906

Abstract

The development of automated essay scoring (AES) in the neural network (NN) approach has eliminated feature engineering. However, feature engineering is still needed, moreover, data with labels in the form of rubric scores, which are complementary to AES holistic scores, are still rarely found. In general, data without labels/scores is found more. However, unsupervised AES research has not progressed with the more common use of publicly labeled data. Based on the case studies adopted in the research, automatic text summarization (ATS) was used as a feature engineering model of AES and readability index as the definition of rubric values for data without labels.This research focuses on developing AES by implementing ATS results on SOM and HDBSCAN. The data used in this research are 403 documents of TEACH ON E-learning essays. Data is represented in the form of a combination of word vectors and a readability index. Based on the tests and measurements carried out, it was concluded that AES with ATS implementation had no good potential for the assessment of TEACH ON essays in increasing the silhouette score. The model produces the best silhouette score of 0.727286113 with original essay data.
Unsupervised Text Style Transfer for Authorship Obfuscation in Bahasa Indonesia Yunita Sari; Fadhlan Pasyah Al Faridzi
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 1 (2023): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.79623

Abstract

Authorship attribution is an NLP task to identify the author of a text based on stylometric analysis. On the other hand, authorship obfuscation aims to protect against authorship attribution by modifying a text’s style. The main challenge in authorship obfuscation is how to keep the content of the text despite the text modification. In this research, we are applying text style transfer methods for modifying the writing style while preserving the content of the input text. We implemented two unsupervised text style transfer: dictionary-based and back translation methods to change the formality level of the text. Experiment results shows that the back-translation method outperformed the dictionary-based method. The authorship attribution performance decreased up to 16.15% and 23.66% on F1-score for 3 and 10 authors respectively using back-translation. While for dictionary-based method the F1-score dropped up to 1.99% and 11.56% for 3 and 10 authors respectively. Evaluation on sensibleness and soundness factors show that the back-translation method can preserve the semantic of the obfuscated texts. Moreover, the modified texts are well-formed and inconspicuous.  
Co-Authors Aina Musdholifah Fadhlan Pasyah Al Faridzi Rayhan Naufal Ramadhan Riszki Wijayatun Pratiwi Sensa Gudya Sauma Syahra Yohanes Suyanto