Optimizing Machine Learning Performance with The Naive Bayes Classifier Process in Smart Farming

Made Yosfin Saputra; I Wayan Santiyasa

doi:10.23887/janapati.v13i2.76926

Authors

Made Yosfin Saputra Universitas Udayana
I Wayan Santiyasa Universitas Udayana

DOI:

https://doi.org/10.23887/janapati.v13i2.76926

Keywords:

Agriculture, Global Warming, Machine Learning, Naive Bayes Classifier

Abstract

Indonesia is a country that relies heavily on the agricultural and plantation sectors to meet its needs for food and industrial raw materials. But farmers face challenges such as falling commodity prices and the negative impact of global warming, which has resulted in widespread drought. As a result, competition for water resources between the agricultural, industrial and household sectors is getting tighter, making it increasingly difficult for farmers to guarantee water supplies. The phenomenon of global warming has caused challenges in the current era. In Bali, although there is a method called “Subak” to manage rice field irrigation systems, it has not been fully implemented. To overcome this problem, a solution is needed that can automate water distribution based on soil moisture levels, temperature, light and air humidity. It uses machine learning techniques specifically using Naive Bayes Classifier to make real-time decisions regarding crop irrigation. The aim of this research is to increase the efficiency and effectiveness of crop irrigation in agriculture while reducing the impact of warming. The results of testing the scenario with orchid plants obtained an accuracy of around 80% and with general plants obtained around 80% which was tested every time 5 data were collected. Testing with a total of 84 training data and 26 test data. From the test results, an accuracy of 92.30769% was obtained.

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