PENERAPAN JARINGAN SARAF TIRUAN DENGAN ALGORITMA PERCEPTRON PADA DETEKSI PENYAKIT BAWANG MERAH
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Abstract
Penelitian ini difokuskan pada kemajuan sistem deteksi penyakit bawang merah (Allium ascalonicum) menggunakan Jaringan Saraf Tiruan (JST) dengan Algoritma Perceptron. Bawang merupakan komoditas hortikultura penting dengan kepentingan ekonomi yang substansif di Indonesia. Namun, produksi bawang merah sering terhambat oleh berbagai penyakit yang dapat menyebabkan kerugian signifikan. Identifikasi tepat waktu dan pengelolaan yang efektif terhadap penyakit tersebut sangat penting untuk mengurangi dampak buruknya. Sistem pakar yang memanfaatkan kecerdasan buatan, khususnya JST, telah terbukti efektif dalam deteksi penyakit tanaman. JST meniru cara kerja otak manusia dalam mengenali pola dan membuat keputusan berdasarkan data yang diperoleh. Penelitian ini menggunakan data gejala penyakit bawang merah yang terdiri dari 13 gejala dan 7 jenis penyakit. Setiap gejala dan penyakit dihubungkan dalam basis pengetahuan yang kemudian dianalisis menggunakan Algoritma Perceptron. Perceptron adalah algoritma pembelajaran terawasi yang digunakan untuk klasifikasi dan pengenalan pola. Proses pelatihan melibatkan inisialisasi bobot dan bias, perhitungan respon output, dan penyesuaian bobot jika terjadi kesalahan. Hasil akhir dari sistem ini adalah identifikasi jenis penyakit yang menyerang bawang merah berdasarkan gejala yang diamati. Implementasi sistem ini mencakup tampilan home, tampilan diagnosa, dan tampilan hasil diagnosa. Sistem ini memungkinkan pengguna untuk melakukan diagnosa penyakit bawang merah dengan menjawab pertanyaan terkait gejala yang diamati. Hasil diagnosa akan menampilkan jenis penyakit dan solusi yang disarankan. Penelitian ini menunjukkan bahwa Algoritma Perceptron dapat diterapkan secara efektif dalam sistem deteksi penyakit bawang merah, membantu petani dalam mengidentifikasi dan mengelola penyakit dengan lebih cepat dan akurat.
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