Prediction system for pH measurement on Brassica oleraceae (Red Cabbage) using machine learning regression

Dian Wulan Hastuti, Marlina Harahap, Faizal Adila Ferdiansyah, Adhi Harmoko Saputro, Cuk Imawan

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)


pH is an important unit to represent the chemical condition of a liquid, solid substance, food nutrition, and microbial activity as well. pH value is also commonly used to detect the behaviour of chemical substances. Measurement of pH value can be shown by color changes of the substance based on the acidity condition from the measured environment. In this research, the colorimetric based on machine learning and pH value detected from the color information for point-of-care applications. For this investigation, we used the pH buffer solution and natural dyes derived from Brassica oleraceae (Red Cabbage) that shows colorimetric response gradually shifted from red to green along with the increasing of pH from 2.00 (acid) to 11.00 (alkaline). In this paper, we propose a method for predicting pH value based on Artificial Neural Network Regression (ANNR) and K-Nearest Neighbour Regression (KNNR) with RGB, HSV and LAB color space. As a result, the performance (99.83% ± 0.11) of this method could estimate the pH reasonably well for point-of-care application.

Original languageEnglish
Article number012050
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 9 Jun 2020
Event4th International Seminar on Sensors, Instrumentation, Measurement and Metrology, ISSIMM 2019 - Padang, West Sumatera, Indonesia
Duration: 14 Nov 201914 Nov 2019


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