KECERDASAN BUATAN DAN LITERASI DIGITAL: Dasar, Etika, dan Praktik
Kata Kunci:
KECERDASAN BUATAN, LITERASI DIGITALSinopsis
Buku ini merupakan panduan praktis yang dirancang untuk membantu pembaca memahami konsep dasar Kecerdasan Buatan (Al) serta menerapkannya dalam kehidupan sehari-hari. Disusun dengan bahasa yang mudah dipahami, buku ini cocok bagi pemula, khususnya pelajar dan siapa pun yang ingin mengenal dan memanfaatkan teknologi Al secara tepat dan bertanggung jawab.
Melalui penjelasan konsep yang sederhana, contoh aplikatif, dan latihan praktik, pembaca akan diajak untuk mengeksplorasi berbagai alat Al yang dapat mendukung kegiatan belajar, kreativitas, pengembangan ide, serta pemecahan masalah di lingkungan sekitar.
• Memahami konsep dasar Al secara mudah dan menyenangkan.
• Mengenal berbagai alat Al dan cara penggunaannya.
• Meningkatkan kreativitas dan produktivitas dengan Al.
• Membangun keterampilan berpikir kritis, inovatif, dan kolaboratif di era digital.
Buku ini diharapkan menjadi langkah awal dalam membentuk generasi muda yang siap menghadapi tantangan masa depan dan mampu memanfaatkan teknologi Al untuk menciptakan perubahan positif bagi diri sendiri dan masyarakat.
Bab
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KATA PENGANTAR
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UCAPAN TERIMA KASIH
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PERNYATAAN
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DAFTAR ISI
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PENDAHULUAN
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BAB 01. MENGENAL KECERDASAN BUATAN
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BAB 02. KONSEP DASAR DAN CARA KERJA AI
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BAB 03. PENERAPAN AI DI BERBAGAI SEKTOR
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BAB 04. PANDUAN PENGGUNAAN ALAT AI
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BAB 05. MEMBANGUN LITERASI AI
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Bab 06. KESIMPULAN
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DAFTAR PUSTAKA
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TENTANG PENULIS
Unduhan
Referensi
[1] A. Kaplan and M. Haenlein, “Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of Artificial Intelligence,” Bus. Horiz., vol. 62, no. 1, pp. 15–25, Jan. 2019, doi: 10.1016/j.bushor.2018.08.004.
[2] A. M. Turing, “Computing Machinery And Intelligence,” Mind, Vol. 49, Pp. 433–460, 1950.
[3] J. McCarthy, N. S. Minsky, Marvin L. Rochester, and C. E, A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. 1955.
[4] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, Oct. 1986, doi: 10.1038/323533a0.
[5] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015, doi: 10.1038/nature14539.
[6] A. Vaswani et al., “Attention Is All You Need,” Aug. 2017, [Online]. Available: http://arxiv.org/abs/1706.03762
[7] T. B. Brown et al., “Language Models are Few-Shot Learners,” Jul. 2020, [Online]. Available: http://arxiv.org/abs/2005.14165
[8] T. M. Mitchel, Machine Learning. McGraw-Hill Science, 1997.
[9] D. Jurafsky and J. H. Martin, Speech and Language Processing (2Nd Edition). Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 2009.
[10] R. Szeliski, Computer Vision: Algorithms and Applications. Springer, 2022.
[11] S. Feuerriegel, J. Hartmann, C. Janiesch, and P. Zschech, “Generative AI,” Sep. 2023, doi: 10.1007/s12599-023-00834-7.
[12] T. Gebru et al., “Datasheets for Datasets,” Dec. 2021, [Online]. Available: http://arxiv.org/abs/1803.09010
[13] D. Bergmann, “What are Machine Learning algorithms?” [Online]. Available: https://www.ibm.com/think/ topics/machine-learning-algorithms
[14] J. Sevilla, L. Heim, A. Ho, T. Besiroglu, M. Hobbhahn, and P. Villalobos, “Compute Trends Across Three Eras of Machine Learning,” in 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, Jul. 2022, pp. 1–8. doi: 10.1109/IJCNN55064.2022.9891914.
[15] M. Steidl, M. Felderer, and R. Ramler, “The pipeline for the continuous development of Artificial Intelligence models—Current state of research and practice,” J. Syst. Softw., vol. 199, p. 111615, May 2023, doi: 10.1016/j.jss.2023.111615.
[16] A. D. Barocas, Solon. Selbst, “Big Data’s Disparate Impact,” Calif. Law Rev., vol. 104, no. No. 3, pp. 671–732, 2016.
[17] A. Jobin, M. Ienca, and E. Vayena, “The global landscape of AI ethics guidelines,” Nat. Mach. Intell., vol. 1, no. 9, pp. 389–399, Sep. 2019, doi: 10.1038/s42256-019-0088-2.
[18] OECD, AI, Data Governance and Privacy Synergies and Areas of International Co-Operation, OECD Artificial Intelligence Papers. 2024.
[19] P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing,” ACM Comput. Surv., vol. 55, no. 9, pp. 1–35, Sep. 2023, doi: 10.1145/3560815.
[20] Z. Ji et al., “Survey of Hallucination in Natural Language Generation,” ACM Comput. Surv., vol. 55, no. 12, pp. 1–38, Dec. 2023, doi: 10.1145/3571730.
[21] OECD, Empowering Learners for the Age of AI, An AI Literacy Framework for Primary and Secondary Education. OECD, 2025.
[22] D. Long and B. Magerko, “What is AI Literacy? Competencies and Design Considerations,” in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, New York, NY, USA: ACM, Apr. 2020, pp. 1–16. doi: 10.1145/3313831.3376727.
