Klasifikasi Citra Sampah Menggunakan Support Vector Machine dengan Ekstraksi Fitur Gray Level Co-Occurrence Matrix dan Color Moments
DOI:
https://doi.org/10.25126/jtiik.2022954868Abstrak
Sampah merupakan salah satu permasalahan global yang dihadapi seluruh dunia termasuk Indonesia. Apabila tidak dikelola dengan baik, jenis dan volume sampah yang semakin meningkat dapat berdampak buruk pada lingkungan dan kesehatan manusia. Pemilahan sampah merupakan langkah awal dalam melakukan berbagai jenis pengolahan sampah. Pemilahan sampah secara manual tidak mudah dilakukan mengingat jumlahnya yang amat besar, sehingga otomatisasi pemilahan sampah diperlukan. Penelitian ini mengusulkan klasifikasi citra sampah menggunakan Support Vector Machine (SVM) dengan ekstraksi fitur Gray Level Co-Occurrence Matrix (GLCM) dan Color Moments serta mengoptimalkan kinerja terbaik dalam proses klasifikasinya. Dataset TrashNet digunakan untuk mengevaluasi metode yang diusulkan. Beberapa parameter penting yang digunakan dalam penelitian ini adalah orientasi sudut GLCM, parameter C (soft margin) pada SVM, dan parameter ???? pada Radial Basis Kernel (RBF). Pembagian data dilakukan menggunakan 10-Fold Cross Validation. Hasil penelitian menunjukkan bahwa kombinasi fitur GLCM dengan orientasi sudut 45° dan Color Moments memberikan rata-rata akurasi terbaik sebesar 78,87% dengan menggunakan parameter C bernilai 32 dan parameter γ bernilai 4. Hasil pengujian terbaik diperoleh pada fold ke-3 dengan akurasi sebesar 85,43% yang digunakan sebagai skenario pengujian data baru. Pengujian terhadap 30 citra sampah baru menggunakan model terbaik memperoleh akurasi sebesar 70%.
Abstract
Waste is one of the global problems faced by the whole world, including Indonesia. Improper waste management can harm the environment and interfere with health. Waste management involved several steps in handling waste, the first one being waste sorting. In Indonesia, waste sorting is still performed manually. Manual waste sorting is not easy to do because the waste amount is very large. Therefore, automatic waste detection technology is needed to support more optimal waste sorting. This study proposes waste image classification using Support Vector Machine (SVM) with Gray Level Co-Occurrence Matrix (GLCM) and Color Moments as the features. The TrashNet dataset is used to evaluate the proposed method. In addition, 30 additional waste image outside trashnet is used as testing data. Some of the important parameters that are tuned in this study are the angle orientation of the GLCM, C (soft margin) parameter on the SVM, and ???? parameter on the Radial Base Kernel (RBF). Data splitting is done using 10-Fold Cross Validation. The results showed that the combination of GLCM features with 45° angle orientation and Color Moments gave the best average accuracy of 78.87% using C parameter with a value of 32 and γ parameter with a value of 4. The best test results were obtained in the third fold with an accuracy of 85, 43%. This result is used to test the 30 test image outside the TrashNet dataset, and achieve accuracy of 70%.
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