Klasifikasi Citra Sampah Menggunakan Support Vector Machine dengan Ekstraksi Fitur Gray Level Co-Occurrence Matrix dan Color Moments

Nisa, Iffa Zainan; Endah, Sukmawati Nur; Sasongko, Priyo Sidik; Kusumaningrum, Retno; Khadijah, Khadijah; Rismiyati, Rismiyati

doi:10.25126/jtiik.2022954868

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…The assessment using the best model yielded an accuracy of 70%. This research has the potential to improve efficiency in waste management thanks to the application of appropriate and accurate machine learning technology (Nisa et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Valuation of Svm Kernel Performance in Organic and Non-Organic Waste Classification

Dahyoung Yenuargo,

Muhamad Fatchan,

Wahyu Hadikristanto

2024

IJIST

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In an era of increasing concern for environmental sustainability, waste management remains an important global issue. Efficient waste classification, in particular distinguishing between organic and recyclable materials, is essential for reducing environmental impact. Traditional manual classification methods are often error-prone and inefficient. This research evaluates the performance of SVM models with RBF and Polynomial kernels for waste classification, using SqueezeNet for feature extraction. Datasets from Kaggle were preprocessed and augmented to improve model training. The experimental results show that the SVM model with RBF kernel outperforms the Polynomial kernel in classifying organic and recyclable waste, with an accuracy of 97.9% compared to 97.3% for the Polynomial kernel. This finding underscores the importance of kernel selection and parameter tuning in optimising SVM models for non-linear classification tasks. This research contributes to the development of more efficient and accurate waste classification technologies, promoting better waste management practices. Further research is recommended to explore advanced feature extraction methods and expand the scope of classification to cover a wider range of waste categories.

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Section: Literature Reviewmentioning

confidence: 99%

Valuation of Svm Kernel Performance in Organic and Non-Organic Waste Classification

Dahyoung Yenuargo,

Muhamad Fatchan,

Wahyu Hadikristanto

2024

IJIST

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“…Komponen biota terpenting yang mendominasi pada ekosistem ini adalah hewan karang yang tergolong kedalam ordo Scleractinia yang memiliki kerangka yang terbuat dari kapur [2].…”

Section: Pendahuluanunclassified

Rehabilitasi Ekosistem Terumbu Karang Menggunakan Metode Rangka Spider-Web

Firihu,

Variani,

Sudarsono

et al. 2022

jpm

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Ekosistem terumbu karang ini sangat rentan mengalami kerusakan, ada dua penyebab kerusakan terumbu karang, yaitu akibat dari kegiatan manusia dan pengaruh dari alam. Salah satu permasalahan yang dihadapi oleh mitra adalah adanya kerusakan ekosistem terumbu karang akibat faktor antropogenik. Lokasi pengabdian ini adalah Desa Mekar yang dihuni oleh masyarakat etnis bajo dimana 80% aktifitas masyarakatnya adalah nelayan dan pembudidaya ikan. Program pengabdian ini dilaksanakan terintegrasi dengan program Kuliah Kerja Nyata (KKN) Tematik dimana program utamanya adalah rehabilitasi terumbu karang menggunakan kerangka spider-web. Dalam pelaksanaannya, mahasiswa bersama-sama dengan Dosen Pendamping Lapangan (DPL) dan mitra kelompok nelayan Miara Sama Mandiri bersama-sama menyiapkan peralatan dan bahan serta menurunkan unit transplantasi karang ke lokasi. Tidak kurang dari 30 unit media transpantasi dengan jumlah anakan karang mencapai 570 anakan karang berhasil ditransplantasikan di perairan sekitar Pulau Bokori.

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“…The photos from the shooting were then analyzed using computer software to obtain quantitative data (Image J and CPCe). (Malinda et al, 2020;Sauri et al, 2019).…”

Section: Data Retrievalmentioning

confidence: 99%

Relationship Coral Reef Cover With Reef Fish Abundance in the Waters of Miang Island, Sangkulirang, Kutai East, East Kalimantan

Luthfi

2021

JEEST

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Miang Island is located at coordinates 118° 0' 20" E -0° 44' 0" N, which is administratively in Sangkulirang District, East Kutai Regency, East Kalimantan. Coral reefs on Miang Besar Island have an area of ± 218.8 ha, with the best coral cover in the waters of Sangkulirang District and one of the best in East Kutai Regency. Reef fish have an active role in coral reef ecosystems because they can be indicators of the health of coral reef ecosystems and can measure the level of suitability of their habitats. If the condition of the coral is damaged, the number of reef fish that inhabit it will decrease because the habitat does not meet the availability of food ingredients and the loss of breeding grounds. Coral reef data retrieval using the UPT method and reef fish using the UVC method. The percentage of coral cover on Miang Island is in the moderate to a bad category where the average percentage of cover is 21% which is in poor condition (<25%). The total abundance of fish at station 1 was 0.45 ind./m 3 , station 2 was 0.10 ind./m 3 , station 3 was 0.06 ind./m 3 , and station 4 was 0.09 ind./m 3 . The genus Acropora is the dominant coral genus at all stations, and the Pomacentridae family is the dominant family of reef fish at all stations. The relationship between the percentage of coral cover and the abundance of reef fish is very strong, with an R 2 value of 72.13%.

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Klasifikasi Citra Sampah Menggunakan Support Vector Machine dengan Ekstraksi Fitur Gray Level Co-Occurrence Matrix dan Color Moments

Cited by 4 publications

References 7 publications

Valuation of Svm Kernel Performance in Organic and Non-Organic Waste Classification

Valuation of Svm Kernel Performance in Organic and Non-Organic Waste Classification

Rehabilitasi Ekosistem Terumbu Karang Menggunakan Metode Rangka Spider-Web

Relationship Coral Reef Cover With Reef Fish Abundance in the Waters of Miang Island, Sangkulirang, Kutai East, East Kalimantan

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