Truth in the Perspective of Philosophy of Science and Implementation in Data Science and Machine Leaning


  • Mohamad Idris Institut Teknologi Bandung
  • Riza Ibnu Adam Institut Teknologi Bandung
  • Yulrio Brianorman Institut Teknologi Bandung
  • Rinaldi Munir Institut Teknologi Bandung
  • Dimitri Mahayana Institut Teknologi Bandung


falsifikasi, demarkasi, machine learning, data science, pseudoscience


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.

Author Biographies

Mohamad Idris, Institut Teknologi Bandung

Sekolah Tinggi Elektro dan Informatika

Riza Ibnu Adam, Institut Teknologi Bandung

Sekolah Tinggi Elektro dan Informatika

Yulrio Brianorman, Institut Teknologi Bandung

Sekolah Tinggi Elektro dan Informatika

Rinaldi Munir, Institut Teknologi Bandung

Sekolah Tinggi Elektro dan Informatika

Dimitri Mahayana, Institut Teknologi Bandung

Sekolah Tinggi Elektro dan Informatika


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