Sulistyarso, Harry Budiharjo and Irawati, Dyah Ayu and Pamungkas, Joko and Widiyaningsih, Indah (2021) Hasil Uji Similaritas_Modeling of Crude Oil Types Classification Using the Naive Bayes Classifier Method. LPPM UPN “Veteran” Yogyakarta Conference Series Proceeding on Engineering and Science Series (ESS).
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Abstract
This research is part of previous research regarding the prediction of petroleum's physical
properties to help the user get the prediction value of crude oil's physical properties from field
test data, which was carried out from Enhanced Oil Recovery Research Laboratory,
Petroleum Engineering UPN “Veteran” Yogyakarta. The field data that is measured in the
laboratory that has been done is by adding biosurfactants and increasing the temperature.
Various steps have been taken to reduce crude oil's viscosity value so that it can be diluted
could flow. It is necessary to calculate the viscosity of crude oil in this process to determine
the extent of the viscosity level as expected by adding biosurfactants or increasing the
temperature that has been carried out in the EOR process. Naïve Bayes Classifier is used to
classify oil data into three categories: light oil, medium oil, and heavy oil, based on the
prediction of the viscosity value. The Naïve Bayes Classifier is a robust algorithm for
performing machine learning-based predictive modeling that applies the Bayes theorem. This
predictive modeling for the physical properties of crude oil was built using the Python
programming language and the PyQt5 library to build desktop-based applications. The
classification of oil has arrived at labeling the prediction results of crude oil's viscosity into
three categories, namely light oil, medium oil, and heavy oil. The test results with testing data
produce accurate data; the predicted value is within the specified range.
Item Type: | Other |
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Subjek: | T Technology > TA Engineering (General). Civil engineering (General) |
Divisions: | x. Faculty of Engineering, Science and Mathematics > School of Engineering Sciences |
Depositing User: | Nurul Alifah Rahmawati |
Date Deposited: | 18 Oct 2021 05:22 |
Last Modified: | 18 Oct 2021 05:22 |
URI: | http://eprints.upnyk.ac.id/id/eprint/26883 |
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