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68 Institut Teknologi Nasional

DAFTAR PUSTAKA

Abu Ahmad. (2017). Mengenal Artificial Intelligence, Machine Learning, Neural Network, dan Deep Learning. Jurnal Teknologi Indonesia.

Adithya Rao, N. S. (2016). Actionable and Political Text Classification using Word Embeddings and LSTM. Computer Applications.

Aditiawarman, M. (2019). HOAX DAN HATE SPEECH DI DUNIA MAYA.

Lembaga Kajian Aset Budaya Indonesia.

Aini Suri Talita1, A. W. (2019). IMPLEMENTASI ALGORITMA LONG SHORT-TERM MEMORY (LSTM) UNTUK MENDETEKSI UJARAN KEBENCIAN (HATE SPEECH) PADA KASUS PILPRES 2019. Jurnal MATRIK.

Alessandro Bondielli a, b. ,. (2019). A survey on fake news and rumour detection techniques.

Anshar, T. (2019). PENGARUH HOAX BAGI KEHIDUPAN BERNEGARA.

https://www.acehprov.go.id/.

Arkaitz Zubiaga, A. A. (2018). Detection and Resolution of Rumours in Social Media: A Survey. ACM Computing Surveys (CSUR), 32.

Arman, A. A. (2004). Teknologi Pemrosesan Bahasa Alami sebagai Teknologi Kunci untuk Meningkatkan Cara Interaksi antara Manusia dengan Mesin1.

Astria Firman, H. F. (2016). Sistem Informasi Perpustakaan Online Berbasis Web.

E-journal Teknik Elektro dan Komputer.

Bajaj, S. (2017). “The Pope Has a New Baby!” Fake News Detection Using Deep Learning.

Britz, D. (2015). Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs.

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Institut Teknologi Nasional | 69 Cahyo Darujati1, A. B. (2012). PEMANFAATAN TEKNIK SUPERVISED

UNTUK. Jurnal Link.

Chu-Sing Yang, Y.-H. Y. (2017). Improved Local Binary Pattern for Real Scene Optical Character.

Costel-Sergiu Atodiresei*, A. T. (2018). Identifying Fake News and Fake Users on Twitter. International Conference on Knowledge Based and Intelligent Information and Engineering. Belgrade, Serbia: Elsevier Ltd.

Dan Li, J. Q. (2016). Text Sentiment Analysis Based on Long Short-Term Memory. International Conference on Computer Communication and the Internet.

Dea Herwinda Kalokasari, D. I. (2017). IMPLEMENTASI ALGORITMA MULTINOMIAL NAIVE BAYES CLASSIFIER PADA SISTEM

KLASIFIKASI SURAT KELUAR (Studi Kasus : DISKOMINFO Kabupaten Tangerang). JURNAL TEKNIK INFORMATIKA VOL.10 NO.2, 2017.

DERWIN SUHARTONO, S. M. (2016). Word Vector Representation: Word2Vec

& Glove. School of Computer Science BINUS UNIVERSITY.

Dutta, D. (2018). A Review of Different Word Embeddings for Sentiment Classification. School of Electronics Kalinga Institute of Industrial Technology.

Gang Liu, J. G. (2018). Bidirectional LSTM with attention mechanism and convolutional layer. Neurocomputing.

Geoffrey E. Hinton, S. O.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation.

GERS, F. (2001). Long Short-Term Memory in Recurrent Neural Network.

Gomez, A. (2016). Deriving Back Propagation on simple RNN/LSTM.

https://medium.com/@aidangomez/let-s-do-this-f9b699de31d9.

Hyejung Chung, K.-s. S. (2018). Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction.

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Institut Teknologi Nasional | 70 IbnuHabibie. (2018). Identifikasi Judul Berita Clickbait Berbahasa Indonesia

dengan Algoritma Long Short Term Memory (LSTM) Recurrent Neural Network. Repositori Institusi USU, Univsersitas Sumatera Utara.

James Pustejovsky, A. S. (2012). Natural Language Annotation for Machine Learning.

Jeffrey Pennington, R. S. (2014). GloVe: Global Vectors forWord Representation.

Computer Science Department, Stanford University, Stanford, CA, https://nlp.stanford.edu/projects/glove/.

Ji Young Lee, F. D. (2016). Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks. NAACL.

Kadam, S. (2017). Python | Word Embedding using Word2Vec.

https://www.geeksforgeeks.org/python-word-embedding-using-word2vec/.

Keras. (n.d.). Timeseries data preprocessing.

https://keras.io/api/preprocessing/timeseries/.

Kung-Hsiang, H. (. (2018). Word2Vec and FastText Word Embedding with Gensim. https://towardsdatascience.com/word-embedding-with-word2vec- and-fasttext-a209c1d3e12c.

Maaz Amajd, Z. K. (2017). Text Classification with Deep Neural Networks.

Marin Vuković, K. P. (2009). An Intelligent Automatic Hoax Detection System.

MathWorks. (2018). Introducing Deep Learning with MATLAB.

Mitchell, T. M. (1997). Machine Learning. McGraw-Hill Science/Engineering/Math.

Muh. Ibnu Choldun, K. S. (2018). KLASIFIKASI PENELITIAN DALAM DEEP LEARNING. Jurnal Ilmiah Manajemen Informatika, 25.

Nene, S. (2017). Deep Learning for Natural Language Processing. International Research Journal of Engineering and Technology.

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Institut Teknologi Nasional | 71 NITIN INDURKHYA, F. J. (2010). HANDBOOK OF NATURAL LANGUAGE

PROCESSING.

O. E. Taylor, P. S. (2020). Application of Supervised Machine Learning Algorithms to Detect Online Fake. International Journal of Computer Science and Mathematical Theory.

Olah, C. (2014). Deep Learning, NLP, and Representations.

http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/.

Olah, C. (2015). Understanding LSTM Networks.

https://colah.github.io/posts/2015-08-Understanding-LSTMs/.

Oluwaseun Ajao, D. B. (2018). Fake News Identification on Twitter with Hybrid CNN and RNN Models. roceedings of the 9th International Conference on Social Media and Society (pp. 226-230). Copenhagen, Denmark:

SMSociety '18 .

Pengfei Liu, X. Q. (2016). Recurrent Neural Network for Text Classification with Multi-Task Learning. Computation and Language.

Priansa, D. J. (2017). Perilaku Konsumen dalam Persaingan Bisnis Kontemporer.

Bandung.

PRIANSYA, S. (2017). NORMALISASI TEKS MEDIA SOSIAL

MENGGUNAKAN WORD2VEC, LEVENSHTEIN DISTANCE, DAN JARO-WINKLER DISTANCE.

Putra, J. W. (2019). Pengenalan Konsep Pembelajaran Mesin dan Deep Learning.

Rafal Jozefowicz, W. Z. (2015). An Empirical Exploration of Recurrent Network Architectures.

Rafal Jozefowicz, W. Z. (2015). An Empirical Exploration of Recurrent Network Architectures.

Rahadi, D. R. (2017). PERILAKU PENGGUNA DAN INFORMASI HOAX DI MEDIA SOSIAL.

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Institut Teknologi Nasional | 72 Rahadi1*, D. R. (2017). PERILAKU PENGGUNA DAN INFORMASI HOAX.

JMDK.

Ronan Collobert, J. W. (2008). A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning.

S.Ananth, D. D. (2019). Fake News Detection using Convolution. International Journal of Innovative Research in Computer and Communication Engineering.

Sahil Chopra, S. J. (2017). Towards Automatic Identification of Fake News:

Headline-Article Stance Detection with LSTM Attention Models.

Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks.

Sepp Hochreiter, J. S. (1997). Long Short-Term Memory.

Silitonga, Y. R. (2019). ANALISIS DAN PENERAPAN DATAMINING UNTUK MENDETEKSI BERITA PALSU (FAKE NEWS) PADA SOCIAL MEDIA DENGAN MEMANFAATKAN MODUL SCIKIT LEARN.

Silvin. (2017). ANALISIS SENTIMEN MEDIA TWITTER MENGGUNAKAN LONG SHOR-TERM MEMORY RECURRENT NEURAL NETWORK.

http://kc.umn.ac.id/.

Skymind. (2017). A Beginner's Guide to LSTMs and Recurrent Neural Networks.

Retrieved from pathmind: https://pathmind.com/wiki/lstm

Sutskever, I. (2013). TRAINING RECURRENT NEURAL NETWORKS.

Tomas Mikolov, K. C. (2013). Efficient Estimation of Word Representations in Vector Space. https://code.google.com/archive/p/word2vec/.

Verma, S. (2014). Understanding Input and Output shape in LSTM (Keras).

https://mc.ai/understanding-input-and-output-shape-in-lstm-keras/.

Yessivha Imanuela Claudy1, R. S. (2018). Klasifikasi Dokumen Twitter Untuk Mengetahui Karakter Calon Karyawan Menggunakan Algoritme K-

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Institut Teknologi Nasional | 73 Nearest Neighbor (KNN). Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer.

Yulius Denny Prabowo, T. L. (2019). Pembentukan Vector Space Model Bahasa Indonesia Menggunakan Metode Word to Vector.

Referensi

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