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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Rudy Agus Santoso
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Pengembangan Sistem Prediksi Hama Wereng Berdasarkan Data Cuaca Sensor Dan Cuaca Online Menggunakan Metode Naive Bayes Rudy Agus Santoso; Dahnial Syauqy; Mochammad Hannats Hanafi Ichsan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Rice (Oryza sativa L) is an important commodity in agriculture because the needs for rice as the primary food of the Indonesian people. Brown planthopper (Nilaparvata lugens) is one of the obstacles that causes rice plants dry so that impact on the decreasing yields. This research will be developing a system to predict pests of planthopper based on parameters of temperature, humidity and rainfall using Naive Bayes method. Temperature and humidity features using test data that obtained from the DHT11 sensor while rainfall data was obtained online from an online weather site provider via ESP8266 module. The Data training of pest and weather conditions are obtained from institutions associated with Brown Grasshopper attack, and weather data information. The test data and training data will be processed by Arduino Uno Microcontroller to predict the presence of Brown Grasshopper using Naive Bayes method. In the test data taken by DHT11 sensor, the temperature and humidity feature readings have a good accuracy value with an error value of 2.14% for temperature and 1.71% on humidity. In testing of rainfall data readings, there is difference of value between weather site and BMKG with error value equal to 22,51%. To test the accuracy of Naive Bayes classification, obtained an accuracy of 83.33% with 6 test data from 17 data train. In testing the accuracy with the test data from the sensor obtained an accuracy of 85.71% with 19 training data and 21 times the data collection test.
Co-Authors Dahnial Syauqy Mochammad Hannats Hanafi Ichsan