Web-Based Rice Disease Diagnosis Expert System Using Fuzzy Tsukamoto Method and K-Nearest Neighbor Algorithm

Abstract

Technology today is growing rapidly from year to year, not least started to spread to the agricultural sector. With the information technology making society more easily in search of information via the internet from your smart device. The goal of this study was made to facilitate the community, especially farmers in helping to diagnose diseases and pests in rice plants. Rice plants can be attacked by a wide variety of diseases and pests with a wide variety of symptoms experienced in rice plants. To know the kind of disease on rice plants in the era of technology, it takes an expert system that can help detect the disease in rice plants. In this study, Expert System-Based Website using Tsukamoto Fuzzy method and the Algorithm of K-Nearest Neighbor whose purpose is to help people, especially farmers in diagnosing diseases and pests in rice plants by looking at the symptoms of the attack on the rice plant. Data was obtained from the Research and the Ministry of Agriculture then taken some sample data for testing done. The results of the testing data of this expert system is the result of late diagnosis in diseases of the rice with the symptoms that already exist based on the data that have been obtained with an accuracy rate of 92,88%.

Sistem Informasi Monitoring Lahan Pertanian dan Pengusiran Hama Berbasis Internet of Thing

“…Metode fuzzy sugeno memiliki derajat keanggotaan (µ[x]) dalam rentang 0 hingga 1 yang menunjukan sejauh mana suatu nilai benar dan sejauh mana suatu nilai itu salah [16]. Secara umum dalam logika fuzzy terdapat empat tahap, yaitu tahap basis kaidah (rule base), Tahap inferensi, Tahap fuzzifikasi (fuzzification), lalu tahap defuzzifikasi (defuzzification) [17].…”

Implementation Of K-Nearest Neighbor - Certainty Factor For Expert System Detection Of Idiopathic Thrombocytopenic Purpura

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