Pamanto, Mohammad Angga Tri (2025) KLASIFIKASI GAMBAR SAMPAH RUMAH TANGGA MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK UNTUK MENDUKUNG PROGRAM DAUR ULANG. S1 thesis, Universitas PGRI Madiun.
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Abstract
The issue of household waste management is becoming increasingly complex, particularly in waste segregation by type. Low public awareness and lack of technological support hinder recycling programs. This study aims to develop a web-based household waste image classification system using the Convolutional Neural Network (CNN) method with the ResNet101V2 model. The dataset, sourced from Kaggle, contains 19,762 images across 10 waste categories. Image preprocessing, data augmentation, and model training were conducted in two phases: initial training and fine-tuning. The model's performance was evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. Testing results showed that the system achieved 95% classification accuracy. The system was implemented in a web application called EcoDetect, allowing users to classify waste images instantly via uploads or camera input. This research is expected to serve as a technological solution that supports digital education and recycling practices.
| Item Type: | Thesis/Skripsi/Tugas Akhir (S1) |
|---|---|
| Kata Kunci: | waste classification, CNN, ResNet101V2, deep learning, recycling, image processing. |
| Subjects: | T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Fakultas Teknik > Teknik Informatika |
| Depositing User: | TRI ANGGA MOHAMMAD |
| Date Deposited: | 05 Aug 2025 03:42 |
| Last Modified: | 05 Aug 2025 03:42 |
| URI: | http://eprint.unipma.ac.id/id/eprint/3777 |
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