Jaringan Saraf Tiruan

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Jaringan Saraf Tiruan. Supervised Learning Process dengan Backpropagation of Error. Andre Tampubolon – 1204000114 Arawinda D. – 1205000169 Bernadia Puspasari – 1205000223 Bobby Alexander – 1205000231 Clara Vania – 1205000266 Dyta Anggraeni - 1205000304. Latar Belakang. - PowerPoint PPT Presentation

Text of Jaringan Saraf Tiruan

  • Jaringan Saraf TiruanSupervised Learning Process dengan Backpropagation of ErrorAndre Tampubolon 1204000114Arawinda D. 1205000169Bernadia Puspasari 1205000223Bobby Alexander 1205000231Clara Vania 1205000266Dyta Anggraeni - 1205000304

  • Latar BelakangKebutuhan akan teknologi pengenalan wajah untuk mendukung kegiatan sehari-hari manusia, misalnya dalam hal keamanan dan identifikasi.

  • TujuanUmumMelakukan pembelajaran agar suatu input citra dapat dikenali.

    KhususMenghitung error dari hasil keluaran dan target untuk digunakan sebagai bahan pembelajaran agar dapat mencapai hasil yang mendekati target

  • Experimental Design

    Layer dibagi menjadi tiga, yaitu input layer, hidden layer, dan output layer. Input layer terdiri dari 900 neuron dari sebuah data yang berukuran 30x30 pixel. Hidden layer terdiri dari 100 neuron, dan output layer terdiri dari 5 neuron sebagai target.

    Langkah-langkah yang dilakukan:Inisialisasi bobot dengan metode Nguyen-Widron.Selama nilai error belum mencapai batas toleransi error atau epoch belum mencapai 104, lakukan proses Feedforward dan Backpropagation.

  • FeedforwardSetiap input yang diterima dikirimkan ke semua unit layer di atasnya (hidden layer).Pada setiap unit hidden, 1. Semua sinyal input dengan bobotnya dihitung sebagai input dari hidden layer.2. Nilai aktivasi sebagai output unit hidden dihitung.3. Nilai aktivasi dikirim sebagai input untuk unit output.Pada setiap unit output,1. Semua sinyal input dengan bobotnya dihitung sebagai input dari output layer.2. Nilai aktivasi sebagai output jaringan dihitung.

  • BackpropagationPada setiap unit output:1. Menerima pola target yang bersesuaian dengan pola input.2. Menghitung informasi error.3. Menghitung besarnya koreksi bobot unit output.4. Menghitung besarnya koreksi bias output.5. Mengirimkan informasi error ke unit-unit yang ada pada layer di bawahnya.

  • BackpropagationPada setiap unit hidden:1. Menghitung semua koreksi error.2. Menghitung nilai aktivasi koreksi error.3. Menghitung koreksi bobot unit hidden.4. Menghitung koreksi error bias unit hidden.Setelah itu, setiap unit output dan hidden masing-masing meng-update bobot dan biasnya.

  • Experimental Result

    Dari hasil penelitian yang kelompok kami lakukan, error paling minimum didapatkan ketika dipilih alpha dan momentum yang paling kecil, yaitu masing-masing bernilai 0.1.

    Sebanyak 10.000 iterasi dilakukan dan kemudian didapatkan error yang turun naik, namun akhirnya konvergen menurun.

  • Experimental Result

    Chart1

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