Pengenalan Karakter Anak Untuk Mengenali Potensi Berdasarkan Sinyal Fisiologi Menggunakan K-Nearest Neighbors Classifier

Sukenda Sukenda, Eka Angga L, M. Kohar

Abstract


Perkembangan teknologi informasi mulai diterapkan di bidang psikologi, salah satu contohnya adalah aplikasi yang digunakan untuk pengenalan emosi. Pada dasarnya, proses pengenalan emosi dapat dilakukan melalui beberapa cara yaitu penulisan (text), sinyal fisiologi, ekspresi wajah, intonasi suara, dan gerak tubuh. Akan tetapi, ada kemungkinan ekspresi wajah, tulisan tangan, intonasi suara dan gerak tubuh bisa dimanipulasi, sehingga membuat pengenalan emosi menjadi kurang valid. Pengenalan emosi melalui sinyal fisiologi lebih representatif dan mampu memberikan hasil yang lebih objektif karena sinyal fisiologi tidak dapat dikontrol secara sadar oleh penggunanya sendiri. Sinyal fisiologi yang dapat digunakan untuk mengenali emosi adalah detak jantung dan respon dari konduktansi kulit. Untuk dapat melakukan pengenalan emosi berdasarkan sinyal fisiologi ini dilakukan dengan membangun sebuah sistem aplikasi. Sistem aplikasi ini, terdiri dari dua unit utama yaitu unit hardware dan unit software. Unit hardware terdiri dari dua sensor yaitu sensor pulse dan sensor GSR yang terintegrasi dengan microcontroller arduino, integrasi ini untuk melakukan pengukuran sinyal dari tubuh. Unit software berfungsi untuk mengolah data yang terdiri dari aplikasi user interface, sistem database, dan machine learning. Data yang diterima dari sensor, akan disimpan ke database yang kemudian dilakukan proses pre-processing data, feature scaling, dan klasifikasi data. Proses pre-processing data terdiri dari dua tahapan, yaitu filter data dan filter attribute. Kemudian feature scaling digunakan untuk proses normalisasi. Setelah melalui kedua proses tersebut, data diklasifikasikan menggunakan algoritma KNN, untuk melakukan proses prediksi emosi. Dari hasil penelitian, sistem aplikasi yang dibangun mampu melakukan proses pengukuran sinyal fisiologi dan klasifikasi emosi dengan rata-rata nilai akurasi, presisi, dan recall adalah 76%.

Kata kunci : Aplikasi, Emosi, Sinyal fisiologi, KNN.

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References


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DOI: https://doi.org/10.37531/sejaman.v6i2.5891

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