Sistem Hitung dan Klasifikasi Objek dengan Metode Convolutional Neural Network
DOI:
https://doi.org/10.22441/jte.2020.v11i2.007Keywords:
Convolutional neural network, Python, Spyder 3Abstract
Sistem klasifikasi objek ini di rancang untuk melakukan klasifikasi dan perhitungan jumlah objek terdeteksi pada suatu gambar. 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 pada citra resolusi tinggi yang memiliki model distribusi nonparametrik. Kemudian gambar yang telah diterima dijalankan menggunakan Bahasa pemrograman python pada laptop operasional menggunakan platform open source spyder3. Input system ini adalah citra 2 dimensi dengan skala minimal 400 x 400 pixel dan skala maksimal 1600 x 1600 pixel. Setelah program dijalankan maka outputnya adalah sebuah citra yang dengan keterangan jumlah wajah terdeteksi dan keterangan framing terhadap pola wajah pada gambar output. Penelitian menggunakan tiga kelompok gambar percobaan, gambar kelompok pertama berisikan gambar dengan objek manusia, kelompok gambar kedua berisikan objek manusia asli yang di campur dengan karikatur, kelopok gambar ketiga berisi gambar kartun berperawakan manusia. Pada percobaan kelompok pertama hasil deteksi mencapai 80%, Dan kelopok kedua di dapat hasil deteksi mencapai 75%. Dan pada percobaan gambar kelompok ketiga system tidak mendeteksi adanya pola wajah manusia. Hasil penelitian ini menunjukkan bahwa penggunaan metode CNN berpotensi untuk pendekatan pengenalan objek secara otomatis dalam membedakan jenis pola wajah manusia sebagai bahan pertimbangan interpreter dalam menentukan objek pada citra.
Kata kunci: Convolutional neural network, Python, Spyder 3
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