Yuda, Prawira (2026) Implementasi Internet Of Things dan Machine Learning Untuk Prediksi Kondisi Kesuburan Tanaman Kelapa Sawit. Other thesis, Universitas Islam Riau.

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Abstract

Oil palm is a strategic plantation commodity that plays an important role in the Indonesian economy. The productivity level and quality of oil palm plants are greatly influenced by soil fertility conditions, such as soil pH, temperature, humidity, and electrical conductivity (EC). However, the process of monitoring soil conditions in the field is still mostly done manually, so it is less efficient, takes a long time, and has the potential to produce less accurate data. Therefore, a monitoring and prediction system is needed that is able to work in real-time and support appropriate decision-making. This research aims to design and implement a system for predicting the fertility and health conditions of oil palm plants based on the Internet of Things (IoT) and Machine Learning. The system uses an ESP32 microcontroller connected to a 7 in 1 soil sensor to measure soil pH, temperature, humidity, and EC parameters in real-time. The measurement data is sent to the server and displayed through the website platform as a monitoring medium. The data is processed using Machine Learning algorithms to classify soil fertility conditions and predict the health of oil palm plants. The system development method used is the prototyping method, which includes needs analysis, system design, prototyping, and system testing and improvement. With the hope of helping farmers and plantation managers in making faster and more appropriate decisions regarding crop maintenance, so as to increase the productivity and efficiency of oil palm plantation management.

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