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All Journal Green Intelligent Systems and Applications Innovative: Journal Of Social Science Research
Pebrian, Hafizh
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Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Public Health Other

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Analisis Sentimen Opini Publik terhadap Dedi Mulyadi di Twitter Menggunakan Ekstraksi Fitur TF-IDF dan Klasifikasi Naive Bayes Pebrian, Hafizh; Kusuma, Aditya Ali; Pribadi, Muhammad Rizky
Innovative: Journal Of Social Science Research Vol. 6 No. 2 (2026): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

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Abstract

Pertumbuhan pesat media sosial, khususnya Twitter, telah membuka ruang yang luas bagi masyarakat untuk mengekspresikan pandangan mereka secara terbuka terhadap tokoh publik dan isu-isu politik. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap opini masyarakat mengenai Dedi Mulyadi, seorang tokoh politik di Indonesia, dengan memanfaatkan data yang diperoleh dari Twitter. Metodologi yang digunakan meliputi tahapan pengumpulan data tweet, praproses teks, ekstraksi fitur menggunakan pendekatan Term Frekuensi-Inverse Document Frekuensi (TF-IDF), serta proses klasifikasi sentimen melalui algoritma Naive Bayes. Sentimen yang diklasifikasikan terdiri dari tiga kategori, yaitu positif, negatif, dan netral. Hasil evaluasi menunjukkan bahwa kombinasi antara metode TF-IDF dan Naive Bayes mampu mengidentifikasi sentimen publik secara cukup efektif, dengan akurasi mencapai 68,0%. Temuan ini diharapkan dapat memberikan kontribusi dalam bidang analisis media sosial dan pemetaan opini masyarakat terhadap figur politik.
Potato Leaf Disease Classification Using MobileNetV3 Architecture With Adam and Stochastic Gradient Descent Optimizers Pebrian, Hafizh; Hartati, Ery
Green Intelligent Systems and Applications Volume 6 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i1.1063

Abstract

Potato leaf diseases such as Early Blight and Late Blight reduced productivity and could cause crop failure if they were not detected early. This study analyzed the comparative performance of the Adam and Stochastic Gradient Descent (SGD) optimizers using the MobileNetV3-Large architecture for potato leaf disease classification. The dataset consisted of three categories: healthy leaves, Early Blight, and Late Blight, with a total of 4,072 images. All images were processed through preprocessing stages, including resizing to 224 × 224 pixels and pixel value normalization. The data were divided into training, validation, and testing sets with a ratio of 70:20:10. Random undersampling and data augmentation techniques were applied to the training data to address class imbalance and improve the model’s generalization capability. The model training process was conducted using a transfer learning approach with the MobileNetV3-Large architecture through two stages: feature extraction and fine-tuning. Model performance evaluation was based on accuracy, precision, recall, and F1-score metrics. The results showed that the Adam optimizer achieved a test accuracy of 98.75% with an F1-score of 0.9875, while the SGD optimizer achieved a test accuracy of 96.56% with an F1-score of 0.9635. The Adam optimizer also demonstrated faster and more stable convergence during the training process. This study was expected to serve as a reference for determining an appropriate optimizer for deep learning applications in image classification, particularly in plant disease detection.
Co-Authors Hartati, Ery Kusuma, Aditya Ali Muhammad Rizky Pribadi