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ANALISIS PENGENALAN POLA HURUF DAN ANGKA RUANGAN MENGGUNAKAN METODE WEIGHTLESS NEURAL NETWORK

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Pattern recognition is an essential aspect of artificial intelligence with various applications, including face, voice, and character recognition. In the healthcare field, this technology can support navigation systems for medicine delivery robots, reducing direct physical contact between medical staff and patients with infectious diseases. This research focuses on applying the Weightless Neural Network (WNN) method for character and number recognition using Raspberry Pi as the detector and binary data converter, and Nucleo F401RE microcontroller as the data processor. The objectives are to identify the implementation of WNN on hardware devices, analyze the effectiveness of Nucleo F401RE in binary data processing, and evaluate WNN’s performance in pattern recognition. The results show that the system successfully detected text using Tesseract OCR, which was processed into a 75 × 45 binary array and stored in 16 datasets. WNN achieved recognition accuracy of 95.83%. The Nucleo F401RE also demonstrated excellent processing speed, ranging from 0.01 to 0.02 seconds, supporting real-time implementation. Therefore, this system has strong potential to be applied in medicine delivery robots to enhance healthcare efficiency.

Availability
Inventory Code Barcode Call Number Location Status
2507006193T185479T1854792025Central Library (Reference)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1854792025
Publisher
Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xiii, 91 hlm.; ilus.; tab.; 29 cm.
Language
Indonesia
ISBN/ISSN
-
Classification
006.307
Content Type
Text
Media Type
unmediated
Carrier Type
other (computer)
Edition
-
Subject(s)
Prodi Sistem Komputer
Metode Weightless Neural Network
Specific Detail Info
-
Statement of Responsibility
Other version/related
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