SISTEM DETEKSI OBJEK, PENGENALAN WAJAH, DAN PENGENALAN EKSPRESI WAJAH MENGGUNAKAN METODE MODIFIKASI ALEXNET

PUTERI, SHALSABILA and Suprapto, Bhakti Yudho (2025) SISTEM DETEKSI OBJEK, PENGENALAN WAJAH, DAN PENGENALAN EKSPRESI WAJAH MENGGUNAKAN METODE MODIFIKASI ALEXNET. Undergraduate thesis, Sriwijaya University.

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Abstract

Service robots are one of the implementations of Industry 4.0 technology that require a computer vision system to detect objects, recognize faces, and identify facial expressions in real-time. However, most previous studies still separated these three functions into different systems, resulting in less efficient and less interactive robot performance. This study developed an integrated system based on deep learning by modifying the AlexNet architecture to enable simultaneous object detection, face recognition, and facial expression recognition. Image processing was performed using the Python programming language and tested directly on a service robot at the Control and Robotics Laboratory, Universitas Sriwijaya. Object detection was carried out using YOLOv8, while face and expression recognition were performed using the Modified AlexNet. Facial expressions were classified into five categories: happy, sad, angry, normal, and shocked. Based on the test results, the Modified AlexNet achieved a face detection accuracy of 78% and expression recognition accuracy of 74% after 50 epochs of training, significantly outperforming the standard AlexNet, which only achieved 32% and 40%, respectively. In real-time testing, the Modified AlexNet achieved 80% accuracy for face detection and 93% for expression recognition, with a distance estimation error of approximately ±1 cm. YOLOv8 demonstrated the highest accuracy in object detection at 82%, while Faster R-CNN showed poor performance with only 8% accuracy and failed to detect faces and expressions. The results indicate that the combination of YOLOv8 and Modified AlexNet offers an optimal and reliable solution to support intelligent and responsive service robot interactions.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Modifikasi AlexNet, deep learning, pengenalan wajah, ekspresi wajah, pengenalan objek, service robot, CNN.
Subjects:	T Technology > TJ Mechanical engineering and machinery > TJ1125-1345 Machine shops and machine shop practice > TJ1185 General works Machine tools and machining Including errors in workmanship, accuracy of fitting, etc. > TJ1185.S393 Ceramic materials--Machining--Automation. Robots, Industrial T Technology > TJ Mechanical engineering and machinery > TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK452-454.4 Electric apparatus and materials. Electric circuits. Electric networks
Divisions:	03-Faculty of Engineering > 20201-Electrical Engineering (S1)
Depositing User:	SHALSABILA PUTERI
Date Deposited:	09 Sep 2025 02:48
Last Modified:	09 Sep 2025 02:48
URI:	http://repository.unsri.ac.id/id/eprint/183724

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