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Auliya, Ahmad Himam
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Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Implementasi Metode Forward Chaining pada Sistem Pakar Diagnosis Penyakit Tanaman Tembakau Auliya, Ahmad Himam; Rahmasari, Nagita; Widyassari, Adhika Pramita
SIMETRIS Vol 18 No 2 (2024): SIMETRIS
Publisher : Sekolah Tinggi Teknologi Ronggolawe Cepu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51901/simetris.v18i2.489

Abstract

Tanaman tembakau memiliki nilai ekonomi tinggi, tetapi produktivitasnya sering terancam oleh berbagai penyakit yang dapat merugikan petani. Kurangnya pengetahuan dalam mendiagnosis penyakit secara akurat dan terbatasnya akses terhadap pakar pertanian menjadi kendala utama dalam pengendalian penyakit. Untuk mengatasi masalah ini, dikembangkan sistem pakar berbasis metode forward chaining yang mampu mendiagnosis penyakit tanaman tembakau berdasarkan gejala yang diamati. Sistem ini mencocokkan gejala dengan aturan dalam basis pengetahuan untuk menghasilkan diagnosis yang akurat. Hasil pengujian menunjukkan bahwa sistem memiliki akurasi 100% pada lima dataset tanaman tembakau yang diuji. Keberhasilan ini menunjukkan potensi sistem pakar sebagai alat bantu efektif untuk meningkatkan produktivitas tanaman tembakau dan mempermudah pengambilan keputusan di lapangan.
Implementasi Metode Forward Chaining pada Sistem Pakar Diagnosis Penyakit Tanaman Tembakau Auliya, Ahmad Himam; Rahmasari, Nagita; Widyassari, Adhika Pramita
SIMETRIS Vol 18 No 2 (2024): SIMETRIS
Publisher : Sekolah Tinggi Teknologi Ronggolawe Cepu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51901/simetris.v18i2.489

Abstract

Tobacco plants have high economic value, but their productivity is often threatened by various diseases that can harm farmers. Lack of knowledge in diagnosing diseases accurately and limited access to agricultural experts are major obstacles in controlling diseases. To overcome this problem, an expert system based on the forward chaining method was developed that is able to diagnose tobacco plant diseases based on observed symptoms. This system matches symptoms with rules in the knowledge base to produce an accurate diagnosis. The test results showed that the system had 100% accuracy on the five tobacco plant data sets tested. This success shows the potential of expert systems as an effective tool to increase tobacco plant productivity and facilitate decision making in the field.
Co-Authors Adhika Pramita Widyassari Rahmasari, Nagita