Truth in the Perspective of Philosophy of Science and Implementation in Data Science and Machine Leaning
DOI:
https://doi.org/10.23887/jfi.v5i2.42207Keywords:
falsifikasi, demarkasi, machine learning, data science, pseudoscienceAbstract
The development of data science or machine learning is experiencing swift growth. This development can become a pseudoscience due to the process of collecting, using algorithms, and processing data that are not per research standards. Machine learning is one of numerous artificial intelligence (AI) techniques that allow a machine to learn independently. Several machine learning implementations include consulting robots, process optimization, credit scoring, security, and services. Data science often uses supervised learning algorithms, which can be trapped into
pseudoscience due to errors in the use and processing of data in research. The solution to avoid this is to apply the epistemological concept of Karl Popper, which is related to the falsification of
science to solve the problem of demarcation. To enhance it, you can also use the principles of the Four Theory of Truth, namely coherence, correspondence, pragmatism, and consensus.
References
Atabik, A. (2014). Teori Kebenaran Perspektif Filsafat Ilmu: Sebuah Kerangka Untuk Memahami Konstruksi Pengetahuan Agama. Fikrah, 2(1), 253-271.
Dochmie, M. R. (2018). Keilmiahan Ilmu-ilmu Islam Ditinjau dari Prinsip Falsifikasi Karl Popper. Prosiding Konferensi Integrasi Interkoneksi Islam dan Sain, 1, 145-150.
Greg Yangy, J. P.-D. (2019). A Mean Field Theory of Batch Normalization. A Conference Paper at ICLR 2019.
Jimy. (2019, 8 18). Medium. Retrieved 11 10, 2021, from https://bit.ly/3naHJSj
Krizhevsky, A. a. (2017). ImageNet Classification with Deep Convolutional Neural Networks. Association for Computing Machinery, 84-90.
Kumar, S., Dhanda, N., & Pandey, A. (2018). Data Science — Cosmic Infoset Mining, Modeling and Visualization. 2018 International Conference on Computational and Characterization Techniques in Engineering & Sciences (CCTES), 1-4. doi:10.1109/CCTES.2018.8674138
Lima, T. K. (2020, 10). KBBI Daring. (Badan Pengembangan dan Pembinaan Bahasa) Retrieved 11 13, 2021, from https://bit.ly/3Hj8Y5u
Mahayana, D. (2018). Catatan Kuliah Filsafat Ilmu Pengetahuan. Bandung: ITB Press.
Martínez-Plumed, F., Contreras-Ochando, L., Ferri, C., & Hernández-Orallo, J. (2021). CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories. IEEE Transactions on Knowledge and Data Engineering, 33(8), 3048-3061. doi:10.1109/TKDE.2019.2962680
Mohammad Rastegari, V. O. (2016). XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. European Conference on Computer Vision, 525-542.
Nielson, B. a. (2021). Induction, Popper, and machine learning.
Nur, M. (2012). Revivalisasi Epistemologi Falsifikasi. Jurnal Agama dan Hak Azazi Manusia, 2(1), 1-14.
Olavsrud, T. (2020, 10 22). 5 famous analytics and AI disasters. Retrieved 11 11, 2021, from CIO.com - Tech News, Analysis, Blogs, Video: https://bit.ly/31NT2Ys
Padli, M., & Mustofa, M. (2021). Kebenaran dalam Perspektif Filsafat Serta Aktualisasinya dalam Menyaring Berita. Jurnal Filsafat Indonesia, 4(1), 78-88.
Rahman, S. (2017). Relevansi Epistemologi Karl R. Popper. Komunike, Volume IX(2), 138-149.
Ray, S. (2019). A Quick Review of Machine Learning Algorithms. International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), 35-39. doi:10.1109/COMITCon.2019.8862451
Suyanto. (2018). Machine Learning. Bandung: Penerbit Informatika.
Team, M. o. (2020, 11 3). Aplikasi Machine Learning di Bidang Riset Keuangan. (Universitas Bina Nusantara) Retrieved 11 9, 2021, from https://bit.ly/30lYrFj
Warin, T. a. (2021). Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature. Journal of Risk and Financial Management, 14(7). doi:10.3390/jrfm14070302
Yanghao Li, N. W. (2017). Adaptive Batch Normalization for Practical Domain Adaptation. Pattern Recognition.
Yiwen Zhao, L. W. (2018). The Application of Convolution Neural Networks in Sign Language Recognition. 2018 Ninth International Conference on Intelligent Control and Information Processing (ICICIP), 269-272.
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Jurnal Filsafat Indonesia
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Jurnal Filsafat Indonesia Undiksha is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
