Classification of Rock Mineral in Field X based on Spectral Data (SWIR & TIR) using Supervised Machine Learning Methods

S. A. Pane, F. M.H. Sihombing

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)


The massive development of science and technology in the industrial era 4.0 includes artificial intelligence, which is purposed to produce research output in the field of geology more accurately and can be completed in a short time using large amounts of data. Machine learning is a part of artificial intelligence that can provide learning processes on computers independently without explicit programming. The process of identifying rocks through classification can be done using machine learning. The study area is in the Manjimup region, Western Australia which consists of Volcanogenic Massive Sulphide (VMS) deposits. This study purposed to determine the classification of rock minerals using accuracy values from the evaluation of models generated using supervised machine learning based on spectral data, namely Short-Wavelength Infrared (SWIR), and Mid or Thermal Infrared (TIR) acquired from electromagnetic spectrum measurements to identify rock mineral features. The spectral data comes from five rock drilling data in the study area. The supervised machine learning method used to determine the best accuracy consists of 5 types of methods, which are K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Multi-layer Perceptron (MLP). Machine learning is completed by supervised method because the research data contains information about label data, which is the type of rock mineral so that it can produce a classification based on the level of accuracy for each type of rock mineral data. The SVM method produces the best accuracy on SWIR data with 82.5% accuracy and the MLP method produces the best accuracy on TIR data with 82% accuracy for rock mineral classification.

Original languageEnglish
Article number012042
JournalIOP Conference Series: Earth and Environmental Science
Issue number1
Publication statusPublished - 4 Oct 2021
Event5th International Conference on Science, Infrastructure Technology and Regional Development 2020, ICoSITeR 2020 - South Lampung, Virtual, Indonesia
Duration: 23 Oct 202025 Oct 2020


  • Classification
  • Machine Learning
  • Mineral
  • Spectral Data
  • Spectral Geology
  • Supervised Learning


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