IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol.14, No.4, October 2020, pp. 397~406 ISSN (print): 1978-1520, ISSN (online): 2460-7258 DOI: https://doi.org/10.22146/ijccs.60733  397 Received October 20 th ,2020; Revised October 27 th , 2020; Accepted October 30 th , 2020 Attention-Based BiLSTM For Negation Handling In Sentimen Analysis Riszki Wijayatun Pratiwi *1 , Yunita Sari 2 , Yohanes Suyanto 3 1 Master Program of Computer Science, FMIPA UGM, Yogyakarta, Indonesia 2,3 Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta, Indonesia e-mail: *1 riszkiwijayatun@mail.ugm.ac.id, 2 yunita.sari@ugm.ac.id, 3 yanto@ugm.ac.id Abstrak Penelitian tentang analisis sentimen beberapa tahun ini telah terjadi peningkatan. Akan tetapi pada penelitian analisis sentimen masih sedikit yang menggagas tentang penanganan negasi, salah satunya dalam kalimat Bahasa Indonesia. Hal ini mengakibatkan kalimat yang mengandung unsur kata negasi belum ditemukan polaritasnya secara tepat. Tujuan dari penelitian ini adalah menganalisis pengaruh kata negasi berbahasa Indonesia. Berdasarkan kelas positif, netral dan negatif, dengan menggunakan attention-based Long Short Term Memory dan Metode ekstraksi fitur word2vec dengan arsitektur Continuous bag-of-word (CBOW). Dataset yang digunakan berupa data dari Twitter. Performa model dilihat pada nilai akurasi. Penggunaan word2vec dengan arsitektur CBOW dan penambahan layer attention pada metode Long Short Term Memory (LSTM) dan Bidirectional Long short Term Memory (BiLSTM) memperoleh hasil akurasi 78.16% dan untuk BiLSTM menghasilkan akurasi 79.68%. sedangkan pada algoritma FSW 73.50% dan FWL 73.79%. Bisa disimpulkan attention based BiLSTM memiliki akurasi tertinggi, akan tetapi penambahan layer attention pada metode Long Short Term Memory tidak terlalu signifikan untuk penanganan negasi. karena pada penambahan layer attention tidak dapat menentukan kata yang ingin diperhatikan. Kata kunci— LSTM, attention-based LSTM, BiLSTM, Attention based BiLSTM Negasi, Analisis sentimen. 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. Keywords—LSTM, attention-based LSTM, BiLSTM, Attention based Negation BiLSTM, sentiment analysis