Cahyo, Aryo Dwi Cahyo (2026) RANCANG BANGUN ALAT PENYORTIR CERDAS UNTUK MENENTUKAN JENIS SAYURAN MENGGUNAKAN MODEL YOLOV5n BERBASIS COMPUTER VISION. Diploma thesis, Politeknik Negeri Sriwijaya.
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Abstract
Penelitian ini merancang alat penyortir sayuran otomatis berbasis computer vision menggunakan Raspberry Pi CM4, kamera Web-CAM, load cell (HX711), dan LCD 16×2. Model YOLOv5n dilatih dengan 1.668 gambar (wortel, tomat, kentang) via Roboflow, 200 epoch di Google Colab, lalu dikonversi ke TFLite FP16 untuk edge computing. Hasil pelatihan: precision 80,4%, recall 82,2%, mAP50 84,7%, dengan tomat sebagai kelas terbaik. Pengujian real-time menunjukkan akurasi deteksi 80% pada kecepatan 3–5 FPS. Data otomatis terkirim ke Google Spreadsheet via N8N (latensi <3 detik) disertai notifikasi WhatsApp ke pemilik toko. Sistem terbukti mengurangi ketergantungan identifikasi manual serta meningkatkan efisiensi pencatatan transaksi di pasar tradisional.
| Item Type: | Thesis (Diploma) |
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| Uncontrolled Keywords: | Computer Vision, YOLOv5n, Raspberry Pi CM4, Penyortir Cerdas, Deteksi Sayuran, Load Cell, Edge Computing, Google Spreadsheet |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Electronic Engineering > Undergraduate Theses |
| Depositing User: | Pustaka Teknik Elektro |
| Date Deposited: | 29 Jul 2026 10:10 |
| Last Modified: | 29 Jul 2026 10:10 |
| URI: | http://eprints.polsri.ac.id/id/eprint/24014 |
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