PENERAPAN TRANFORMASI LINEAR DALAM RUANG LINGKUP BAHASA PADA ERA DIGITAL
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Abdul Majid, A., Noliza Bakar, N., & Yanita. 2019. “Sifat-Sifat Matriks Ortogonal Dan Transformasi Ortogonal”. Jurnal Matematika UNAND.Vol. VIII (2), pp: 7 – 14.
Aeni, S. N. 2022. Menilik Sejarah Media Sosial, Manfaat, dan Contohnya - Teknologi Katadata.co.id. [Online] Tersedia: https://katadata.co.id/sitinuraeni/digital/6246823429ac2/menilik-sejarah-media-sosial-manfaat-dan-contohnya
Alipour, G., Bagherzadeh Mohasefi, J., & Feizi-Derakhshi, M. R. 2022. “Learning Bilingual Word Embedding Mappings with Similar Words in Related Languages Using GAN”. Applied Artificial Intelligence. Vol. 36(1).
Annur, C. M. 2021. Imbas Pandemi Covid-19, Pendapatan Zoom Meroket 191% pada Kuartal I-2021. [Online] Tersedia: https://databoks.katadata.co.id/datapublish/2021/07/14/imbas-pandemi-covid-19-pendapatan-zoom-meroket-191-pada-kuartal-i-2021
Anton, H., & Rorres, C. 2013. Elementary Linear Algebra: Applications Version, 11th Edition. Dalam John Wiley & Sons Incorporated (11th ed.). https://doi.org/10.1201/b17671-9
Artetxe, M., Labaka, G., Agirre, E., & Cho, K. 2017. Unsupervised Neural Machine Translation. http://arxiv.org/abs/1710.11041
Bollegala, D., Hayashi, K., & Kawarabayashi, K.-I. 2017. “Learning linear transformations between counting-based and prediction-based word embeddings”. https://doi.org/10.1371/journal.pone.0184544
Brownlee, J. 2019. What Are Word Embeddings for Text? Machine Learning Mastery. [Online] Tersedia: https://machinelearningmastery.com/what-are-word-embeddings/
Brychcín, T. 2020. “Linear transformations for cross-lingual semantic textual similarity”. Knowledge-Based Systems, 187. https://doi.org/10.1016/j.knosys.2019.06.027
Conneau, A., Lample, G., Ranzato, M., Denoyer, L., & Jégou, H. 2017. Word Translation Without Parallel Data. [Online] Tersedia: http://arxiv.org/abs/1710.04087
Curry, D. 2023.. Most Popular Apps (2023) - Business of Apps. [Online] Tersedia: https://www.businessofapps.com/data/most-popular-apps/
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. 2018. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. [Online] Tersedia: http://arxiv.org/abs/1810.04805
Dewantara, A. 2021.. Bekerja dari Rumah (Work From Home/WFH) : Menghadapi COVID-19 pada PPKM Level 4. [Online] Tersedia: https://www.djkn.kemenkeu.go.id/kpknl-palu/baca-artikel/14156/Bekerja-dari-Rumah-Work-From-HomeWFH-Menghadapi-COVID-19-pada-PPKM-Level-4.html
Doval, Y., Camacho-Collados, J., Espinosa-Anke, L., & Schockaert, S. 2018. Improving Cross-Lingual Word Embeddings by Meeting in the Middle. [Online] Tersedia: http://arxiv.org/abs/1808.08780
Hewitt, J., & Manning, C. D. 2019. A Structural Probe for Finding Syntax in Word Representations. [Online] Tersedia: https://github.com/john-hewitt/structural-probes.
Himayati, A. I. A. 2020. “Regulatritas dan Relasi Green pada Semigrup Transformasi Linear Parsial Injektif dengan Restriksi Range”. Jurnal Karya Pendidikan Matematika. Vol. 7 (2), pp: 73 -80.
Indonesia: smartphone users 2026 | Statista. 2023. Statista Research Department. [Online] Tersedia: https://www.statista.com/statistics/266729/smartphone-users-in-indonesia/
Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. 2016. Bag of Tricks for Efficient Text Classification. [Online] Tersedia: http://arxiv.org/abs/1607.01759
Kedem, D., Tyree, S., Weinberger, K. Q., Sha, F., & Lanckriet, G.2012. Non-linear Metric Learning.
Keerthi, S. S., Schnabel, T., & Khanna, R. 2015. Towards a Better Understanding of Predict and Count Models. [Online] Tersedia: http://arxiv.org/abs/1511.02024
Khandelwal, R. 2019. Word Embeddings for NLP. Understanding word embeddings and their… | by Renu Khandelwal | Towards Data Science. Towards Data Science. [Online] Tersedia: https://towardsdatascience.com/word-embeddings-for-nlp-5b72991e01d4
Levy, O., & Goldberg, Y. 2014. Neural Word Embedding as Implicit Matrix Factorization.
Li, X., Liu, S., Kautz, J., & Yang, M.-H. 2019. “Learning Linear Transformations for Fast Image and Video Style Transfer”. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2019.00393
Liddy, E. D. 2001. Natural Language Processing. [Online] Tersedia: https://surface.syr.edu/istpub
Lidwina, A. 2021.. Jumlah Unduhan Aplikasi Global Naik 8,7% pada Kuartal I-2021. Databoks. [Online] Tersedia: https://databoks.katadata.co.id/datapublish/2021/05/07/jumlah-unduhan-aplikasi-global-naik-87-pada-kuartal-i-2021
Mahdi, I. M. 2022. Zoom Kuasai Pangsa Platform Konferensi Video Dunia pada 2021. DataIndonesiai.d. [Online] Tersedia: https://dataindonesia.id/digital/detail/zoom-kuasai-pangsa-platform-konferensi-video-dunia-pada-2021
Mikolov, T., Chen, K., Corrado, G., & Dean, J. 2013. Efficient Estimation of Word Representations in Vector Space. [Online] Tersedia: http://arxiv.org/abs/1301.3781
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. 2013. Distributed Representations of Words and Phrases and their Compositionality. [Online] Tersedia: http://arxiv.org/abs/1310.4546
Nurdin, A., Anggo Seno Aji, B., Bustamin, A., & Abidin, Z. 2020. “Perbandingan Kinerja Word Embedding Word2Vec, Glove, Dan Fasttext Pada Klasifikasi Teks”. Jurnal Tekno Kompak. Vol. 14(2), pp:74. https://doi.org/10.33365/jtk.v14i2.732
Oktaviana, D., Noviani, E., & Fran, F. 2020. “Transformasi Givens dan Penerapannya”. Bimaster : Buletin Ilmiah Matematika, Statistika Dan Terapannya. Vol 9, pp: 213–222.
Pennington, J., Socher, R., & Manning, C. D. 2014. GloVe: Global Vectors for Word Representation. [Online} Tersedia: https://doi.org/10.3115/v1/D14-1162
Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. 2018. Deep contextualized word representations. [Online] Tersedia: http://arxiv.org/abs/1802.05365
Rika Ayu Febrilia, B., Chairun Nissa, I., Pujilestari, & Utami Setyawati, D. 2020. Analisis Keterlibatan Dan Respon Mahasiswa Dalam Pembelajaran Daring Menggunakan Google Classroom Di Masa Pandemi Covid-19. Fibonacci: Jurnal Pendidikan Matematika Dan Matematika. Vol. 6(2), pp: 175–184.
Susanti, R. D., & Effendi, M. M. 2022. Efektivitas Penggunaan Edmodo Dalam Pelaksanaan Ulangan Harian Matematika. Fibonacci: Jurnal Pendidikan Matematika Dan Matematika. Vol. 6(1), pp: 9–16.
Turner, A. (t.t.). 2023.How Many People Have Smartphones Worldwide. [Online] Tersedia: https://www.bankmycell.com/blog/how-many-phones-are-in-the-world
DOI: https://doi.org/10.24853/fbc.9.1.1-12
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