Image Processing Using Morphology on Support Vector Machine Classification Model for Waste Image
Sorting waste has always been an important part of managing waste. The primary issue with the waste sorting process has been the discomfort caused by prolonged contact with waste odor. A machinelearning method for identifying waste types was created to address this issue. The study’s goal was to create machine learning to solve waste management challenges by applying the most accurate categorization model available. The research approach was the quantitative analysis of the classification model accuracy. The Kaggle dataset was used to collect and curate data, which was subsequently preprocessed using the morphology approach. Based on picture sources, the data was trained and used to classify waste. The Support Vector Machine model was used in this investigation and feature extraction via the Convolutional Neural Network. The results showed that the system categorized waste successfully, with an accuracy of 99.30% and a loss of 2.47% across all categories. According to the findings of this study, SVM combined with morphological image processing functioned as a strong classification model, with a remarkable accuracy rate of 99.30%. This study’s outcomes contributed to waste management by giving an efficient and dependable waste classification solution compared to many previous studies.
Kecamatan Krembung, Kabupaten Sidoarjo,” Journal of Science and Social Development, vol. 1, no. 1, pp. 16–23,
 R. Setiadi, M. Nurhadi, and F. Prihantoro, “Idealisme dan Dualisme Daur Ulang Sampah di Indonesia: Studi Kasus Kota
Semarang,” Jurnal Ilmu Lingkungan, vol. 18, no. 1, pp. 48–57, 2020.
 D. D, A. M. Tenrigau, and M. Marsal, “Pemanfaatan Sampah Plastik untuk Dijadikan Bantal yang Berkualitas dan Bernilai
Ekonomis Di Desa Tolada Kecematan Malangke Kabupaten Luwu Utara,” To Maega — Jurnal Pengabdian Masyarakat, vol. 1,
no. 1, pp. 8–13, aug 2018.
 A. Pires and G. Martinho, “Waste hierarchy index for circular economy in waste management,” Waste Management, vol. 95, no.
July, pp. 298–305, jul 2019.
 R. Cioffi, M. Travaglioni, G. Piscitelli, A. Petrillo, and F. De Felice, “Artificial Intelligence and Machine Learning Applications
in Smart Production: Progress, Trends, and Directions,” Sustainability, vol. 12, no. 2, pp. 1–26, jan 2020.
 P. R. Sihombing and I. F. Yuliati, “Penerapan Metode Machine Learning dalam Klasifikasi Risiko Kejadian Berat Badan Lahir
Rendah di Indonesia,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 20, no. 2, pp. 417–426,
 L. Efrizoni, S. Defit, M. Tajuddin, and A. Anggrawan, “Komparasi Ekstraksi Fitur dalam Klasifikasi Teks Multilabel Menggunakan
Algoritma Machine Learning,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21,
no. 3, pp. 653–666, 2022.
 Y.Wang, D.Wang, and Y. Tang, “Clustered HybridWind Power Prediction Model Based on ARMA, PSO-SVM, and Clustering
Methods,” IEEE Access, vol. 8, pp. 17 071–17 079, 2020.
 B. K. Triwijoyo, “Model Fast Tansfer Learning pada Jaringan Syaraf Tiruan Konvolusional untuk Klasifikasi Gender
Berdasarkan Citra Wajah,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 18, no. 2, pp.
 L. Leonardo, Y. Yohannes, and E. Hartati, “Klasifikasi Sampah Daur Ulang Menggunakan Support Vector Machine dengan
Fitur Local Binary Pattern,” Jurnal Algoritme, vol. 1, no. 1, pp. 78–90, 2020.
 Y. Chu, C. Huang, X. Xie, B. Tan, S. Kamal, and X. Xiong, “Multilayer hybrid deep-learning method for waste classification
and recycling,” Computational Intelligence and Neuroscience, vol. 2018, 2018.
 I. Muhammad Nurdin and A. Fadlil, “Identification of Feasibility of Canned Based Foods Image Processing Techniques Using
Thresholding,” Buletin Ilmiah Sarjana Teknik Elektro, vol. 3, no. 1, pp. 10–20, jan 2021.
 A. S. Musliman, A. Fadlil, and A. Yudhana, “Identification of White Blood Cells Using Machine Learning Classification Based
on Feature Extraction,” Jurnal Online Informatika, vol. 6, no. 1, pp. 63–72, jun 2021.
 F. Fahmi and B. P. Lubis, “Identification and Sorting of Waste Using Artificial Intelligence Based on Convolutional Neural
Network,” in 2022 6th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM),
2022, pp. 222–226.
 C. Shi, C. Tan, T. Wang, and L. Wang, “A Waste Classification Method Based on a Multilayer Hybrid Convolution Neural
Network,” Applied Sciences, vol. 11, no. 18, pp. 1–19, sep 2021.
 D. O. Gomez, S. C. Agudelo, S. I. Cadavid, M. Toro, and ..., “A pipeline for Solid DomesticWaste classification using Computer
 J. Bobulski and M. Kubanek, “Waste Classification System Using Image Processing and Convolutional Neural Networks,” in
Advances in Computational Intelligence, I. Rojas, G. Joya, and A. Catala, Eds. Cham: Springer International Publishing,
2019, pp. 350–361.
 F. Anwar, A. Fadlil, and I. Riadi, “Analisa Keamanan Image JPEG File Upload Menggunakan Metadata dan GD Graphic
Library Pada Aplikasi Berbasis Web,” in Prosiding Semnastek 2019, 2019, pp. 479–487.
 R. A. Surya, A. Fadlil, and A. Yudhana, “Identification of Pekalongan Batik Images Using Backpropagation Method,” Journal
of Physics: Conference Series, vol. 1373, no. 1, 2019.
 V. K. Chauhan, K. Dahiya, and A. Sharma, “Problem formulations and solvers in linear SVM: a review,” Artificial Intelligence
Review, vol. 52, no. 2, pp. 803–855, 2019.
 S. Sekar, “Waste Classification data,” 2019.
 Y. Qian, M. Aghaabbasi, M. Ali, M. Alqurashi, B. Salah, R. Zainol, M. Moeinaddini, and E. E. Hussein, “Classification of
imbalanced travel mode choice to work data using adjustable svm model,” Applied Sciences (Switzerland), vol. 11, no. 24,
 R. N. Bharagava, G. Saxena, and S. I. Mulla, Bioremediation of Industrial Waste for Environmental Safety, G. Saxena and R. N.
Bharagava, Eds. Singapore: Springer Singapore, 2020.
 P. Lemenkova, “Processing Oceanographic Data By Python Libraries Numpy, Scipy And Pandas,” Aquatic Research, vol. 2,
no. 2, pp. 73–91, 2019.
 W. R. U. Fadilah, W. A. Kusuma, A. E. Minarno, and Y. Munarko, “Classification of Human Activity Recognition Utilizing
Smartphone Data of CNN-LSTM,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics,
and Control, vol. 4, no. 2020, 2021.
 S. A. Rosiva Srg, M. Zarlis, and W. Wanayumini, “Identifikasi Citra Daun dengan GLCM (Gray Level Co-Occurence) dan
K-NN (K-Nearest Neighbor),” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 2, pp.
 H. H. Xingyang Ni, Liang Fang, “Adaptive L2 Regularization in Person Re-Identification,” in 2020 25th International Conference
on Pattern Recognition (ICPR), 2021, pp. 9601–9607,.
 C. F. G. D. Santos and J. P. Papa, “Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural
Networks,” ACM Computing Surveys, vol. 54, no. 10, pp. 1–25, jan 2022.
 S. Saputra, A. Yudhana, and R. Umar, “Implementation of Na¨ıve Bayes for Fish Freshness Identification Based on Image
Processing,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 3, pp. 412–420, 2022.
 M. Rongbo, Zhu Yan, Information Engineering and Applications International Conference on Information Engineering and
Applications (IEA 2011), 2011.
 G. Franchi, A. Fehri, and A. Yao, “Deep morphological networks,” Pattern Recognition, vol. 102, pp. 1–33, 2020.
 Y. Jiang, N. Tan, T. Peng, and H. Zhang, “Retinal Vessels Segmentation Based on Dilated Multi-Scale Convolutional Neural
Network,” IEEE Access, vol. 7, pp. 76 342–76 352, 2019.
 G. Li, D. Jiang, Y. Zhou, G. Jiang, J. Kong, and G. Manogaran, “Human Lesion Detection Method Based on Image Information
and Brain Signal,” IEEE Access, vol. 7, pp. 11 533–11 542, 2019.
 R. I. Kurnia, “Classification of User Comment UsingWord2vec and SVM Classifier,” International Journal of Advanced Trends
in Computer Science and Engineering, vol. 9, no. 1, pp. 643–648, feb 2020.
 P. K. Soni, N. Rajpal, and R. Mehta, “Semiautomatic Road Extraction Framework Based on Shape Features and LS-SVM from
High-Resolution Images,” Journal of the Indian Society of Remote Sensing, vol. 48, no. 3, pp. 513–524, 2020.
 Q. Wu, T. Gao, Z. Lai, and D. Li, “Hybrid SVM-CNN classification technique for humanvehicle targets in an automotive
LFMCW radar,” Sensors (Switzerland), vol. 20, no. 12, pp. 1–18, 2020.
 B. Ghojogh and M. Crowley, “The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial,”
pp. 1–23, 2019.
 H. Zhang, L. Zhang, and Y. Jiang, “Overfitting and Underfitting Analysis for Deep Learning Based End-to-end Communication
Systems,” 2019 11th International Conference on Wireless Communications and Signal Processing, WCSP 2019, pp. 1–6, 2019.
 L. Yao, Y. Wan, H. Ni, and B. Xu, “Action unit classification for facial expression recognition using active learning and SVM,”
Multimedia Tools and Applications, vol. 80, no. 16, pp. 24 287–24 301, jul 2021.
 L. Bloch and C. M. Friedrich, Machine Learning Workflow to Explain Black-Box Models for Early Alzheimer’s Disease Classification
Evaluated for Multiple Datasets. Springer Nature Singapore, 2022, vol. 3, no. 6.
 C. J. Rameshbhai and J. Paulose, “Opinion mining on newspaper headlines using SVM and NLP,” International Journal of
Electrical and Computer Engineering (IJECE), vol. 9, no. 3, pp. 2152–2163, jun 2019.
 A. Jim´enez-Cordero, J. M. Morales, and S. Pineda, “A novel embedded min-max approach for feature selection in nonlinear
Support Vector Machine classification,” European Journal of Operational Research, vol. 293, no. 1, pp. 24–35, 2021.
 B. S. S. Pokuri, S. Ghosal, A. Kokate, S. Sarkar, and B. Ganapathysubramanian, “Interpretable deep learning for guided
microstructure-property explorations in photovoltaics,” npj Computational Materials, vol. 5, no. 1, p. 95, oct 2019.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.