Analisis Kecanduan Pornography Berdasarkan Signal Electroencephalogram Menggunakan Algoritma Deep Learning
DOI:
https://doi.org/10.30865/json.v7i2.9326Keywords:
Electroencephalography, Deep Learning, Pornografi, GRU, Signal EEGAbstract
Deteksi objektif kecanduan pornografi masih menjadi tantangan signifikan, seringkali menghambat intervensi klinis yang efektif. Penelitian ini bertujuan mengembangkan model klasifikasi Deep Learning untuk mendeteksi kecanduan pornografi secara non-invasif berdasarkan sinyal Electroencephalography (EEG). Penelitian ini menerapkan arsitektur Gated Recurrent Unit (GRU) untuk menganalisis data EEG yang diperoleh dari 14 responden menggunakan 19 channel. Data mentah diproses melalui tahapan filtering, Independent Component Analysis (ICA) untuk artifact removal, dan normalisasi. Hasil pengujian menunjukkan model GRU yang diusulkan berhasil mencapai akurasi 94,14%, dengan nilai precision dan recall seimbang (94%), serta F1-score 0,94 untuk kedua kelas (kecanduan dan non-kecanduan). Temuan ini menunjukkan bahwa arsitektur GRU sangat efektif untuk membedakan pola aktivitas otak antara individu pecandu dan non-pecandu. Penelitian ini berkontribusi menyediakan landasan ilmiah bagi pengembangan alat bantu diagnosis otomatis yang lebih akurat untuk menunjang program diagnosis dan rehabilitasi.
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