Implementasi Data Augmentation untuk Klasifikasi Sampah Organik dan Non Organik Menggunakan Inception-V3
DOI:
https://doi.org/10.14421/jiska.2024.9.3.192-204Keywords:
Classification, Transfer Learning, Convolutional Neural Network, InceptionV3, GarbageAbstract
The surge in global waste, particularly in Indonesia, with a total of 36.218 million tons per year, has become an urgent issue. Challenges in waste management are increasingly complex due to the lack of public understanding and awareness in classifying types of waste. One systemic approach to address waste classification issues involves the use of machine learning technology to categorize waste into two main types: organic and non-organic. The data used in this study comes from a Kaggle website dataset comprising 25,500 entries. This research employs a transfer learning approach with the Inception-V3 architecture and data augmentation implementation. Transfer learning is chosen for its proven performance in image data classification, while data augmentation is implemented to introduce diversity to the dataset. The research stages include business understanding, data preprocessing, data augmentation, data modelling, and evaluation. The results show that the use of transfer learning with the Inception-V3 approach and data augmentation implementation achieves an accuracy rate of 94%, which falls into the excellent category.
References
Ahmed, M., Afreen, N., Ahmed, M., Sameer, M., & Ahamed, J. (2023). An inception V3 approach for malware classification using machine learning and transfer learning. International Journal of Intelligent Networks, 4, 11–18. https://doi.org/10.1016/j.ijin.2022.11.005
Fadillah, I., A, L., Kamil, F. El, Shalahuddin, M., Setiawan, I., N, A., M, H., A, N., S, R., & Fikri, K. (2019). Perubahan Pola Pikir Masyarakat tentang Sampah melalui Sosialisasi Pengolahan Sampah Organik dan Non Organik di Dusun Pondok, Kecamatan Gedangsari, Kab. Gunungkidul. Prosiding Konferensi Pengabdian Masyarakat , 1, 239–242. https://sunankalijaga.org/prosiding/index.php/abdimas/article/view/201
Fan, Y., Li, J., Bhatti, U. A., Shao, C., Gong, C., Cheng, J., & Chen, Y. (2023). A Multi-Watermarking Algorithm for Medical Images Using Inception V3 and DCT. Computers, Materials & Continua, 74(1), 1279–1302. https://doi.org/10.32604/cmc.2023.031445
Hidayati, N., Suntoro, J., & Setiaji, G. G. (2021). Perbandingan Algoritma Klasifikasi untuk Prediksi Cacat Software dengan Pendekatan CRISP-DM. Jurnal Sains Dan Informatika, 7(2), 117–126. https://doi.org/10.34128/jsi.v7i2.313
Ibnul Rasidi, A., Pasaribu, Y. A. H., Ziqri, A., & Adhinata, F. D. (2022). Klasifikasi Sampah Organik dan Non-Organik Menggunakan Convolutional Neural Network. Jurnal Teknik Informatika Dan Sistem Informasi, 8(1), 142-149–142 – 149. https://doi.org/10.28932/jutisi.v8i1.4314
InceptionV3. (2015). Google. https://keras.io/api/applications/inceptionv3/
Kartiko, Prima Yudha, A., Dimas Aryanto, N., & Arya Farabi, M. (2022). Klasifikasi Sampah di Saluran Air Menggunakan Algortima CNN. Indonesian Journal of Data and Science, 3(2), 72–81. https://doi.org/10.56705/ijodas.v3i2.33
Kementerian Lingkungan Hidup dan Kehutanan. (2022). SIPSN - Sistem Informasi Pengelolaan Sampah Nasional. Kementerian Lingkungan Hidup Dan Kehutanan. https://sipsn.menlhk.go.id/sipsn/
Lebreton, L., & Andrady, A. (2019). Future scenarios of global plastic waste generation and disposal. Palgrave Communications, 5(1), 6. https://doi.org/10.1057/s41599-018-0212-7
Lin, C., Li, L., Luo, W., Wang, K. C. P., & Guo, J. (2018). Transfer Learning Based Traffic Sign Recognition Using Inception-v3 Model. Periodica Polytechnica Transportation Engineering, 47(3), 242–250. https://doi.org/10.3311/PPtr.11480
Luke, J. J., Joseph, R., & Balaji, M. (2019). IMPACT OF IMAGE SIZE ON ACCURACY AND GENERALIZATION OF CONVOLUTIONAL NEURAL NETWORKS. International Journal of Research and Analytical Reviews, 6(1), 70–80. www.ijrar.org
Mikolajczyk, A., & Grochowski, M. (2018). Data augmentation for improving deep learning in image classification problem. 2018 International Interdisciplinary PhD Workshop (IIPhDW), 117–122. https://doi.org/10.1109/IIPHDW.2018.8388338
Minarno, A. E., Aripa, L., Azhar, Y., & Munarko, Y. (2023). Classification of Malaria Cell Image using Inception-V3 Architecture. JOIV : International Journal on Informatics Visualization, 7(2), 273. https://doi.org/10.30630/joiv.7.2.1301
Minarno, A. E., Hasanuddin, M. Y., & Azhar, Y. (2023). Batik Images Retrieval Using Pre-trained model and K-Nearest Neighbor. JOIV : International Journal on Informatics Visualization, 7(1), 115. https://doi.org/10.30630/joiv.7.1.1299
Nanmaran, R., Srimathi, S., Yamuna, G., Thanigaivel, S., Vickram, A. S., Priya, A. K., Karthick, A., Karpagam, J., Mohanavel, V., & Muhibbullah, M. (2022). Investigating the Role of Image Fusion in Brain Tumor Classification Models Based on Machine Learning Algorithm for Personalized Medicine. Computational and Mathematical Methods in Medicine, 2022(1), 1–13. https://doi.org/10.1155/2022/7137524
NG, K. (2019). Tuned Inception V3 for Recognizing States of Cooking Ingredients. State Recognition Symposium. https://doi.org/10.32555/2019.dl.009
Pei, X., Zhao, Y. hong, Chen, L., Guo, Q., Duan, Z., Pan, Y., & Hou, H. (2023). Robustness of machine learning to color, size change, normalization, and image enhancement on micrograph datasets with large sample differences. Materials & Design, 232, 112086. https://doi.org/10.1016/j.matdes.2023.112086
Rosiana, E., & Perdana, R. (2022). Rancang Bangun Sistem Robot Pemilah Sampah Anorganik dengan Inductive Proximity dan LDR Sebagai Sensor. Building of Informatics, Technology and Science (BITS), 4(2), 1001–1009. https://doi.org/10.47065/bits.v4i2.2017
Schröer, C., Kruse, F., & Gómez, J. M. (2021). A Systematic Literature Review on Applying CRISP-DM Process Model. Procedia Computer Science, 181, 526–534. https://doi.org/10.1016/j.procs.2021.01.199
Sekar, S. (2019). Waste Classification data. Kaggle. https://www.kaggle.com/datasets/techsash/waste-classification-data
Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6(1), 1–48. https://doi.org/10.1186/S40537-019-0197-0/FIGURES/33
Singh, D., & Singh, B. (2020). Investigating the impact of data normalization on classification performance. Applied Soft Computing, 97, 105524. https://doi.org/10.1016/j.asoc.2019.105524
Tripathi, M. (2021). Analysis of Convolutional Neural Network based Image Classification Techniques. Journal of Innovative Image Processing, 3(2), 100–117. https://doi.org/10.36548/jiip.2021.2.003
Vallez, N., Bueno, G., Deniz, O., & Blanco, S. (2022). Diffeomorphic transforms for data augmentation of highly variable shape and texture objects. Computer Methods and Programs in Biomedicine, 219, 106775. https://doi.org/10.1016/j.cmpb.2022.106775
Wang, Y. H., Ou, Y., Deng, X. D., Zhao, L. R., & Zhang, C. Y. (2019). The Ship Collision Accidents Based on Logistic Regression and Big Data. Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019, 4438–4440. https://doi.org/10.1109/CCDC.2019.8832686
Widodo, A. E., & Suleman, S. (2020). Otomatisasi Pemilah Sampah Berbasis Arduino Uno. Indonesian Journal on Software Engineering (IJSE), 6(1), 12–18. https://doi.org/10.31294/ijse.v6i1.7781
Zhang, Y. D., Dong, Z., Chen, X., Jia, W., Du, S., Muhammad, K., & Wang, S. H. (2019). Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation. Multimedia Tools and Applications, 78(3), 3613–3632. https://doi.org/10.1007/S11042-017-5243-3/METRICS
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Rahina Bintang, Yufis Azhar
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms as stated in http://creativecommons.org/licenses/by-nc/4.0
a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
