Skripsi

KLASIFIKASI SAMPAH MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN)

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Penilaian

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Effective waste management is a major challenge in maintaining environmental cleanliness and sustainability. One important step is waste separation, such as distinguishing between organic and inorganic waste. Organic waste, such as food scraps and dry leaves, can be processed into compost or environmentally friendly energy. Meanwhile, inorganic waste, such as plastic, glass, and metal, can be recycled into new products. Proper waste separation allows for more efficient waste processing and has a positive impact on the environment. This study proposes an automated waste classification system based on digital images using deep learning technology with Convolutional Neural Network (CNN) architecture. The three CNN models used are VGG-16, ResNet-50, and Xception. The dataset consists of two main classes: organic and inorganic waste. The study was conducted under 12 testing scenarios with variations in learning rate and batch size. Evaluation metrics include accuracy, precision, recall, and F1-score. The best result was achieved by the VGG-16 model with a learning rate of 1e-4 and batch size of 64, reaching 94.34% accuracy, 90.58% precision, 95.05% recall, and 92.76% F1-score.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507004523	T180146	T1801462025	Central Library (REFERENCE)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1801462025

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xiii, 104 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Teknik Informatika

Specific Detail Info

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Statement of Responsibility

TUTI

Other version/related

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