Skripsi

SISTEM PENGENALAN EKSPRESI CIRI WAJAH MATA DAN HIDUNG MENGGUNAKAN METODE WEIGHTLESS NEURAL NETWORK IMMEDIATE SCAN PADA MOBILE ROBOTIC

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Penilaian

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Facial expression recognition is a branch of digital image processing widely applied in various fields, such as security, human-robot interaction, and artificial intelligence systems. However, the main challenge in implementing facial recognition systems on embedded and mobile robotic devices lies in computational power and memory constraints. Therefore, this study develops a facial expression recognition system using the Weightless Neural Network (WNN) Immediate Scan method, designed for high efficiency on resource-limited devices. The system is built using Raspberry Pi 4 and Arduino Mega 2560, with a Pi Camera as the image acquisition sensor. The image processing workflow consists of several key steps: face detection using Haar Cascade Classifier, feature extraction of the eyes and nose, image conversion into a binary representation, and pattern recognition with the Weightless Neural Network Face Recognition Algorithm (WNN-FRA). The training dataset comprises 75 images with three different expressions (neutral, happy, surprised), and system evaluation is conducted using a random split method to compare its performance. Experimental results indicate that the system achieves an average accuracy of 93,16%, with the highest performance observed for neutral expressions 97,58%, highest happy expressions at 96,62%, and highest surprised expressions at 96,97%. The implementation of the WNN Immediate Scan method enables facial expression recognition with lower power consumption and computational time than conventional methods such as Convolutional Neural Networks (CNN). This study concludes that the Weightless Neural Network Immediate Scan method can be effectively applied to facial expression recognition systems for mobile robotic devices with limited computational resources. Future research may explore hybrid approaches combining WNN with deep learning techniques, expanding the dataset with additional facial expressions, and optimizing image processing parameters to enhance recognition accuracy. Keywords: Facial Expression Recognition, Weightless Neural Network, Immediate Scan, Mobile Robotic, Raspberry Pi, Arduino Mega.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507002872	T173477	T1734772025	Central Library (REFERENCE)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1734772025

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xvii, 127 hlm.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Sistem komputer

Specific Detail Info

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Statement of Responsibility

TUTI

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