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Ikhlas, Ariza
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Agriculture, Biological Sciences & Forestry Computer Science & IT Electrical & Electronics Engineering

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MESIN PEMBELAJARAN ENSEMBLE UNTUK IDENTIFIKASI VARIETAS PADI Ikhlas, Ariza; Abdullah, Abdullah; Prasetyo, Dwi Yuli
Informatika Pertanian Vol 29, No 2 (2020): DESEMBER
Publisher : Sekretariat Badan Penelitian dan Pengembangan Pertanian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21082/ip.v29n2.2020.p123-130

Abstract

Setiap varietas padi memiliki karakter tertentu dengan anjuran tanam berbeda. Petani umumnya kesulitan memilih varietas padi yang cocok untuk ditanam di lahan mereka karena kurangnya kemampuan identifikasi. Algoritma klasifikasi merupakan solusi mengatasi masalah ini karena mampu mengidentifikasi varietas padi melalui citra digital. Tujuan penelitian ini adalah menerapkan dan mengevaluasi beberapa algoritma klasifikasi untuk mengidentifikasi varietas padi menggunakan fitur warna dan tekstur. Penelitian dilakukan di kabupaten Indagiri Hilir Riau pada tahun 2018. Mesin pembelajaran dibangun dengan cara menggabungkan beberapa algoritma klasifikasi (classifier), yaitu Support Vector Machine, k-Nearest Neighbors, Logistic Regression, dan Decision Tree. Varietas yang diteliti adalah IR42, Inpara-9. dan Batang Piaman. Berdasarkan tingkat ketelitian masing-masing algoritma, k-Nearest Neighbors memberikan hasil lebih baik dibanding algoritma lainnya, baik dengan maupun tanpa normalisasi data. Terdapat enam sampel Inpara-9 yang diprediksi benar (true positive) dan lima sampel diprediksi salah (false positive). Pada varietas Batang Piaman terdapat delapan sampel yang diprediksi benar (true positive). Pada IR42 terdapat lima sampel yang diprediksi benar.
Literature Review: A Comparative Study of Waste Classification using Deep Learning Algorithms Ikhlas, Ariza; Hendrik, Billy
Sistemasi: Jurnal Sistem Informasi Vol 14, No 3 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i3.5163

Abstract

Waste type classification remains a daily challenge in modern waste management. Proper waste classification contributes significantly to environmental protection and enhances the efficiency of the recycling process. Unfortunately, manual waste classification is rarely performed by individuals, resulting in mixed waste that is difficult to separate into recyclable and non-recyclable categories. This leads to increased waste accumulation, which becomes harder to process over time. Therefore, automating this procedure using computer vision is of critical importance. This study adopts a Systematic Literature Review (SLR) methodology to analyze existing research conducted by previous scholars. The main objectives are to identify the most appropriate algorithms for waste type classification, determine the most suitable model architectures, and examine the correlation between dataset size, number of classes, and classification accuracy. The results of the literature review show that the Convolutional Neural Network (CNN) algorithm is widely used and considered highly effective for computer vision tasks. Among the best-performing models are: A standard CNN architecture achieving 100% accuracy with 150 data points and 3 classes, CNN with ResNet50 model achieving 99.41% accuracy on 2,527 data points and 6 classes, A combination of ResNet, k-Nearest Neighbors (kNN), and Neighborhood Component Analysis (NCA) achieving 99.35% accuracy on 13,089 data points and 1,672 classes, CNN with CapSA ECOC + ANN model reaching 99.01% accuracy on 1,515 data points and 12 classes. These findings indicate that numerous prior studies have successfully developed high-accuracy models for waste classification, which can serve as a solid foundation for building computer vision systems to automate the waste sorting process.
Exploration of Data Augmentation in Xception for Waste Classification Ikhlas, Ariza; Arlis, Syafri
Sistemasi: Jurnal Sistem Informasi Vol 15, No 4 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i4.5709

Abstract

The increasing volume of waste worldwide has led to significant challenges related to pollution, waste management, and recycling. These issues require innovative solutions to enhance the waste management ecosystem, such as the implementation of Smart Waste Management, which leverages information technology and artificial intelligence. This study aims to implement the Xception Convolutional Neural Network (CNN) model for waste classification, explore various data augmentation techniques, and identify optimal model configurations for this task. The research methodology consists of several stages, including data preparation, model building and training, model adaptation for classification tasks, model evaluation, iterative experimentation, and saving and reloading the trained model. The dataset used in this study is the TrashNet dataset obtained from Kaggle, consisting of 2,527 images across several classes: cardboard, glass, metal, paper, plastic, and trash. Based on the optimization process, the selected hyperparameters include a batch size of 32, 64 convolutional filters, the Adam optimizer (learning rate = 0.0001), and a dropout rate of 0.25. After training for 100 epochs, the model achieved a training accuracy of 99% with a loss of 0.7%, and a validation accuracy of 87% with a validation loss of 52%. Evaluation on the test dataset yielded an accuracy of 76%, precision of 79%, recall of 75%, and an F1-score of 75%. The application of data augmentation techniques—such as scaling, translation, and color space transformation—resulted in performance improvements, increasing accuracy by 13%, precision by 11%, recall by 13%, and F1-score by 12%. This study contributes by implementing the Xception model on the TrashNet dataset for waste classification and proposing several data augmentation methods that provide empirical evidence to support or challenge existing approaches. The findings offer practical insights for the development of Smart Waste Management systems, enrich the literature through experimental results, and provide a comparative analysis of data augmentation techniques suitable for the TrashNet dataset.
Co-Authors Abdullah Abdullah Dwi Yuli Prasetyo Hendrik, Billy Syafri Arlis