Saputra, Hendra Dwi (2026) Klasifikasi Jenis Obat Pada Computer Vision Berbasis Android Menggunakan Metode Convolutional Neural Network. Undergraduate thesis, Politeknik Negeri Jember.
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Abstract
The widespread circulation of drugs with look-alike packaging designs frequently triggers medication errors among the general public. To address this critical issue, this research proposes an Android-based computer vision application that combines a CNN MobileNetV2 architecture for visual feature recognition and Google ML Kit OCR for textual character extraction. Evaluation in direct field testing demonstrated a robust operational accuracy of 96.67%. This minor performance deviation indicates that the artificial intelligence model remains slightly susceptible to physical environmental dynamics, particularly visual distortions caused by light glare on the packaging surface. Moreover, black box validation proved that the software operates seamlessly, executing the dual-validation logic with high precision while maintaining real-time synchronization with the Firebase database. Ultimately, this intelligent prototype functions effectively as an independent drug verification instrument for partner patients. For future improvements, this study recommends a deeper exploration of advanced data augmentation techniques to enhance visual classification resilience
| Item Type: | Thesis (Undergraduate) | ||||||
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| Uncontrolled Keywords: | Convolutional Neural Network, Optical Character Recognition, Computer Vision, Android, Drug Classification | ||||||
| Subjects: | 410 - Rumpun Ilmu Teknik > 450 - Teknik Elektro dan Informatika > 458 - Teknik Informatika 410 - Rumpun Ilmu Teknik > 450 - Teknik Elektro dan Informatika > 462 - Teknologi Informasi |
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| Divisions: | Jurusan Teknologi Informasi > Prodi D4 Teknik Informatika (Sidoarjo) > Tugas Akhir | ||||||
| Depositing User: | Hendra Dwi Saputra | ||||||
| Date Deposited: | 30 Jul 2026 07:02 | ||||||
| Last Modified: | 30 Jul 2026 07:02 | ||||||
| URI: | https://sipora.polije.ac.id/id/eprint/58924 |
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