Desi Ratnasari, . (2025) DETEKSI JENIS SAMPAH BERBASIS COMPUTER VISION MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) DENGAN METODE CLAHE. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
The increasing waste problem requires technology-based solutions, one of which involves the application of Computer Vision. The primary objective of this study is to design a waste classification model using the Convolutional Neural Network (CNN) method combined with the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique to enhance the visual quality of images used during the training and evaluation stages. The dataset utilized consists of digital images of organic and inorganic waste, with two data split proportions: 56:14:30 and 64:16:20. Evaluation was conducted on four different models, both with and without the application of CLAHE. The experimental results indicate that the use of CLAHE improves detection accuracy in several tested models. The best-performing model was obtained using the 56:14:30 data split with CLAHE applied, achieving an accuracy of 94.57%. This study demonstrates that integrating CNN with CLAHE is effective in automatically detecting waste types. Therefore, the developed system has the potential to contribute to intelligent technology-based waste management solutions. Furthermore, this approach aims to enhance the effectiveness of waste sorting and recycling processes, thereby supporting the creation of a future environment that is clean, health-safe, and sustainable.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 2110511152] [Pembimbing 1: Ridwan Raafi’udin] [Pembimbing 2: Muhammad Adrezo] [Penguji 1: Musthofa Galih Pradana] [Penguji 2: Nurul Afifah Arifuddin] |
| Uncontrolled Keywords: | Computer Vision, CNN, CLAHE, Image Processing, Waste Detection |
| 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: | DESI RATNASARI |
| Date Deposited: | 09 Sep 2025 07:58 |
| Last Modified: | 10 Sep 2025 01:05 |
| URI: | http://repository.upnvj.ac.id/id/eprint/37532 |
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