Artificial Intelligence For Banana's Ripeness Detection Using Conventional Neural Network Algorithm
DOI:
https://doi.org/10.22441/jte.2021.v12i2.005Keywords:
Convolutional Neural Network, Deep Learning, Machine Learning, PythonAbstract
Sistem pendeteksian tingkat kematangan buah pisang ini dirancang untuk melihat perbedaan buah yang layak dikonsumsi dan tidak layak dikonsumsi. Menggunakan metode Convolutional Neural Network yang telah dilatih, Metode CNN merupakan salah satu metode deep learning yang mampu melakukan proses pembelajaran mandiri untuk pengenalan objek, ekstraksi objek dan klasifikasi serta dapat diterapkan dapat diterapkan pada citra resolusi tinggi yang memiliki model distribusi nonparametrik. Kemudian gambar yang telah diterima dijalankan menggunakan Bahasa pemrograman python pada laptop operasional menggunakan platform google chrome. Setelah program dijalankan maka outputnya adalah sebuah citra yang dengan keterangan pisang mentah untuk buah pisang dengan kondisi yang mentah atau tidak layak di konsumsi, pisang matang dengan kondisi yang layak di konsumsi dan pisang busuk untuik buah pisang dengan kondisi tidak layak dikonsumsi. Penelitian menggunakan empat kelompok gambar percobaan dan menggunakan jarak, gambar kelompok pertama berisikan gambar dengan objek buah pisang dengan kondisi mentah, kelompok gambar kedua berisikan buah pisang dengan kondisi matang, kelompok gambar ketiga berisikan buah pisang dengan kondisi busuk dan kelompok gambar selain buah pisang. Pada percobaan kelompok pertama hasil deteksi mencapai 100%, Dan kelompok kedua di dapat hasil deteksi mencapai 78%, Dan kelompok ketiga di dapat hasil deteksi mencapai 89%. Dan pada percobaan gambar kelompok keempat system tidak mendeteksi adanya pola buah pisang. Hasil penelitian ini menunjukkan bahwa penggunaan metode CNN berpotensi untuk pendekatan pengenalan objek secara otomatis dalam membedakan jenis pola buah pisang bahan pertimbangan interpreter dalam menentukan objek pada citra
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