Implementation of CNN Algorithm for Classification of Organic and Inorganic Waste Images

Ardiansyah Putra

doi:10.59934/jaiea.v5i1.1546

Authors

Ardiansyah Putra STMIK Kaputama

DOI:

https://doi.org/10.59934/jaiea.v5i1.1546

Keywords:

Transfer learning, image classification, organic waste, inorganic waste, CNN, MobileNetV2, fine-tuning

Abstract

The increasing waste problem necessitates efficient solutions, one of which is automatic classification based on artificial intelligence. This study develops a Convolutional Neural Network (CNN) model for classifying organic and inorganic waste images using a transfer learning approach with the MobileNetV2 architecture. The model was trained in two stages, namely feature extraction and fine-tuning, using a dataset of 25,077 images from a public Kaggle repository. The results show that the model after fine-tuning achieved an accuracy of 92.28%, with a precision of 89.6% for the organic category and 96.4% for inorganic. High recall and F1-score values were also achieved, demonstrating that transfer learning with fine-tuning effectively improves waste image classification accuracy and has potential for implementation in automatic waste sorting systems.

Downloads

Download data is not yet available.

References

W. M. Yusmaman, H. Widiyanto, S. N. Rohmah, M. Ali, S. Pascasarjana, and U. M. Surakarta, “Bahaya Lingkungan Pada Open Dumping Sampah Organik Perkotaan,” vol. 2, no. 2, pp. 85–101, 2023.

M. R. R. Hasibuan, “Manfaat Daur Ulang Sampah Organik Dan Anorganik Untuk Kesehatan Lingkungan,” J. Ilm. Lingkung. , vol. 2, no. 3, pp. 1–11, 2023.

A. Peryanto, A. Yudhana, and R. Umar, “Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation,” J. Appl. Informatics Comput., vol. 4, no. 1, pp. 45–51, 2020, doi: 10.30871/jaic.v4i1.2017.

F. Ramadhani and S. Rahardiantoro, “Acne Severity Classification Study Using Convolutional Neural Network Algorithm with MobileNetV2 Architecture,” vol. 8, no. 2, pp. 112–128, 2024.

Anggraini et al., “Implementasi Data Mining Menggunakan Algoritma c4.5 Pada Klasifikasi Penjualan Fashion Muslimah,” no. 2, pp. 217–227, 2024.

R. Amalia and P. Rosyani, “Klasifikasi Citra Menggunakan Metode Random Forest dan Sequential Minimal Optimization ( SMO ) Image Classification Using Random Forest Method and Sequential Minimal Optimazation ( SMO ),” vol. 09, no. 2, pp. 2020–2022, 2021, doi: 10.26418/justin.v9i2.44120.

Mt. Dr. Basuki Rahmat, S.Si and M. K. Budi Nugroho, S.Kom, Pemrograman Deep Learning Dengann Python. 2021.

F. N. Cahya, N. Hardi, D. Riana, and S. Hadianti, “Klasifikasi Penyakit Mata Menggunakan Convolutional Neural Network ( CNN ),” vol. 10, pp. 618–626, 2021.

R. Faturrahman, Y. S. U. N. Hariyani, and S. Hadiyoso, “Klasifikasi Jajanan Tradisional Indonesia berbasis Deep Learning dan Metode Transfer Learning,” vol. 11, no. 4, pp. 945–957, 2023.

W. Hastomo, R. Dalam, K. Baru, D. Learning, C. N. Network, and T. Brain, “Convolution Neural Network Arsitektur Mobilenet-V2 Untuk Mendeteksi Tumor Otak,” vol. 5, no. Gambar 1, 2021.

S. Mujilahwati, M. Sholihin, and R. Wardhani, “Optimasi Hyperparameter TensorFlow dengan Menggunakan Optuna di Python : Study Kasus Klasifikasi Dokumen Abstrak Skripsi,” vol. 5, pp. 1084–1089, 2021, doi: 10.30865/mib.v5i3.3090.