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All Journal Jurnal Buana Informatika Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Journal of Telematics and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics TRANSISTOR Elektro dan Informatika Indonesian Journal of Community Services Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Indonesian Journal of Data and Science Jurnal Pengabdian kepada Masyarakat Nusantara JAST Prosiding Seminar Riset Mahasiswa Jurnal Rekayasa Sistem Informasi dan Teknologi International Journal of Artificial Intelligence in Medical Issues
Poetro, Bagus Satrio Waluyo
Universitas Islam Sultan Agung
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences

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Journal : Indonesian Journal of Data and Science

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Advancements in Agricultural Automation: SVM Classifier with Hu Moments for Vegetable Identification Waluyo Poetro, Bagus Satrio; Maria, ⁠⁠Eny; Zein, Hamada; Najwaini, Effan; Zulfikar, Dian Hafidh
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.123

Abstract

This study investigates the application of Support Vector Machine (SVM) classifiers in conjunction with Hu Moments for the purpose of classifying segmented images of vegetables, specifically Broccoli, Cabbage, and Cauliflower. Utilizing a dataset comprising segmented vegetable images, this research employs the Canny method for image segmentation and Hu Moments for feature extraction to prepare the data for classification. Through the implementation of a 5-fold cross-validation technique, the performance of the SVM classifier was thoroughly evaluated, revealing moderate accuracy, precision, recall, and F1-scores across all folds. The findings highlight the classifier's potential in distinguishing between different vegetable types, albeit with identified areas for improvement. This research contributes to the growing field of agricultural automation by demonstrating the feasibility of using SVM classifiers and image processing techniques for the task of vegetable identification. The moderate performance metrics emphasize the need for further optimization in feature extraction and classifier tuning to enhance classification accuracy. Future recommendations include exploring alternative machine learning algorithms, advanced feature extraction methods, and expanding the dataset to improve the classifier's robustness and applicability in agricultural settings. This study lays a foundation for future advancements in automated vegetable sorting and quality control, offering insights that could lead to more efficient agricultural practices.
Performance Comparison of CNN and ResNet50 for Skin Cancer Classification Using U-Net Segmented Images Aris Wahyu Murdiyanto; Zulfikar, Dian Hafidh; Waluyo Poetro, Bagus Satrio; Siregar, Alda Cendekia
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.200

Abstract

Skin cancer is a significant global health issue, with melanoma, basal cell carcinoma, and actinic keratosis being the most common types. Early and accurate detection is critical to improve survival rates and treatment outcomes. This study evaluates the performance of Convolutional Neural Networks (CNN) and ResNet50 in classifying segmented images of skin lesions. The dataset, sourced from Kaggle, was pre-processed using U-Net for lesion segmentation to enhance the quality of input data. Both models were trained and evaluated using accuracy, precision, recall, and F1-score metrics. The CNN model demonstrated a balanced performance across classes, with a weighted F1-score of 47%, but suffered from overfitting, as indicated by the divergence between training and validation losses. ResNet50 achieved better recall for basal cell carcinoma (100%) but failed to classify actinic keratosis and melanoma, resulting in a macro F1-score of 23%. The findings reveal that U-Net segmentation improved classification focus but was insufficient to address dataset imbalance and model-specific limitations. This study highlights the challenges of skin cancer classification using deep learning and underscores the importance of addressing data imbalance and overfitting. Future research should explore advanced techniques, such as ensemble methods, data augmentation, and transfer learning, to improve the generalization and clinical applicability of these models. The proposed framework serves as a foundation for further investigation into automated skin cancer detection systems.
Co-Authors Ahmad Ayudhawara Ahmad Saifurrohman Ahmat Yusuf Andika, Ardhi Dwi Angriani, Husni Arief Budiman Aris Wahyu Murdiyanto Dedy Kurniadi Dian Hafidh Zulfikar Effan Najwaini Fadhila Tangguh Admojo Hamada Zein Imam Much Ibnu Subroto izmy alwiah musdar Jiwani, Fatwa Akbar Maria, ⁠⁠Eny Meldyantono, Anggara Putra Moch Taufik Muhammad Qomaruddin Nur Muhammad Syaifuddin Nurul Hidayah Nuzulia Khoiriyah Putra, Allief Suryatama Jaya Sam Farisa Chaerul Haviana Sinra, A. Siregar, Alda Cendekia Sri Mulyono Sri Mulyono Suryani Alifah Taruk, Medi Vani Aulia Pramesti Widhya Nugroho Satrioajie