Fajar Rizki Ramadhan, . (2025) RANCANG BANGUN APLIKASI ANDROID KLASIFIKASI JENIS KAIN KATUN MENGGUNAKAN METODE WATERFALL DAN RESNET-50. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
This research developed an Android application called Klafka for automatically classifying five types of cotton-based fabrics: Combed, TC, CVC, Cotton Stretch, and Polyester. The system uses a ResNet-50 convolutional neural network model converted to TensorFlow Lite for offline inference on mobile devices. Fabric images were collected using a mobile microscope to capture texture details and processed through a TensorFlow-based pipeline. The application was developed using the Waterfall methodology and evaluated through confusion matrix metrics and User Acceptance Testing (UAT) involving 18 participants. The model achieved 97% accuracy with high precision, recall, and f1-score across all classes. UAT results showed strong user satisfaction in terms of ease of use, speed, and reliability. This application demonstrates practical potential for mobile-based fabric classification using deep learning.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 2110511029] [Pembimbing: Ridwan Raafi'udin] [Penguji 1: Ika Nurlaili Isnainiyah] [Penguji 2: Radinal Setyadinsa] |
| Uncontrolled Keywords: | Fabric classification, Deep Learning, ResNet-50, TensorFlow Lite, Android, User Acceptance Testing |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | FAJAR RIZKI RAMADHAN |
| Date Deposited: | 06 Aug 2025 07:23 |
| Last Modified: | 06 Aug 2025 07:23 |
| URI: | http://repository.upnvj.ac.id/id/eprint/37469 |
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