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All Journal POSITIF eProceedings of Engineering
Dwi Putro Wicaksono, Aditya
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Journal : POSITIF

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The KLASIFIKASI PENYAKIT PADA TANAMAN DAUN SINGKONG MENGGUNAKAN VISION TRANSFORMER: KLASIFIKASI PENYAKIT PADA TANAMAN DAUN SINGKONG MENGGUNAKAN VISION TRANSFORMER Rakan Nabila, Luthfi; Dwi Putro Wicaksono, Aditya
POSITIF : Jurnal Sistem dan Teknologi Informasi Vol 11 No 2 (2025): Positif : Jurnal Sistem dan Teknologi Informasi
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/positif.v11i2.14953

Abstract

This research develops a cassava leaf disease classification model using Vision Transformer (ViT) to identify four types of diseases and healthy leaves. With a dataset from Kaggle (3,000 images/class), the TinyViT model was tested through parameter variations to achieve optimal performance. Results showed that the combination of SGD, 50 epochs, and batch size 32 gave the highest validation accuracy (83.16%), outperforming Adam/AdamW. Despite overfitting (100% training accuracy), the model showed good generalization with 81% precision and recall. These findings confirm the potential of ViT in plant disease detection, while highlighting the need to address overfitting through further regularization. Future research can explore dataset expansion and fine-tuning for accuracy improvement.
Klasifikasi Kekeringan dan Penyakit pada Daun Padi Berdasarkan Ekstraksi Ciri Warna dan Tekstur Menggunakan CSPDarknet: Klasifikasi Kekeringan dan Penyakit pada Daun Padi Berdasarkan Ekstraksi Ciri Warna dan Tekstur Menggunakan CSPDarknet Abdul Jabbar Robbani; Dwi Putro Wicaksono, Aditya; Dedy Agung Prabowo
POSITIF : Jurnal Sistem dan Teknologi Informasi Vol 11 No 1 (2025): Positif : Jurnal Sistem dan Teknologi Informasi
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/positif.v11i1.14964

Abstract

The decline in rice productivity in Indonesia is often caused by drought and leaf diseases that are difficult to detect early. This condition requires a technology-based classification system that is able to provide fast and accurate diagnosis as support for decision making in the agricultural sector. This study aims to develop a rice leaf image classification model using the CSPDarknet architecture, with a color and texture feature extraction approach. The dataset used is the result of primary documentation that has gone through an augmentation process to increase the diversity of training data. The model architecture consists of a CSPDarknet backbone combined with a Cross-stage Partial Bottleneck with two Convolutions (C2f) block, Spatial Pyramid Pooling - Fast (SPPF), Global Average Pooling, and dropout to improve model generalization. Training was carried out using the Stratified 5-Fold Cross-Validation method and three optimizer variations, namely Stochastic Gradient Descent (SGD), Adam, and AdamW. The experimental results showed that the best model combination was achieved with the AdamW optimizer, with an average accuracy value of 99.72%, precision of 99.73%, recall of 99.72%, and F1-score of 99.72%. These findings indicate that the proposed classification approach is able to effectively distinguish healthy, diseased, and drought-affected leaves. In the future, this model has the potential to be further developed through the integration of Raspberry Pi-based Internet of Things (IoT) devices for real-time monitoring of plant conditions in the field.
Co-Authors Abdul Jabbar Robbani Dedy Agung Prabowo Deni Romadan, Muhamad Rakan Nabila, Luthfi Yohani Setiya Rafika Nur Yunus Dani Saputra, Muhammad