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All Journal JSAI (Journal Scientific and Applied Informatics)
Firga Abel Astiawan
Universitas Sjakhyakirti
Computer Science & IT

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Journal : jsai journal scientific and applied informatics

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Klasifikasi Penyakit Tanaman Sawit Berbasis Hybrid Deep Learning Menggunakan U-Net dan ResNet- Fakhri Lambardo; Putri Maharani; Putri Andromeda; Firga Abel Astiawan
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.9385

Abstract

Oil palm (Elaeis guineensis) productivity was frequently constrained by foliar diseases, which were often difficult to detect at an early stage using conventional visual inspection methods. To address this challenge, the present study proposed a hybrid deep learning framework for automated oil palm leaf disease detection. A dataset comprising 1,200 oil palm leaf images, equally distributed across three disease classes (400 images per class), was utilized. The dataset was partitioned into training (70%), validation (15%), and testing (15%) subsets, with training and validation data obtained from public repositories, while testing data were collected directly to ensure model generalizability. The proposed hybrid architecture combined U-Net for precise leaf lesion segmentation, ResNet-50 as a deep feature extractor to capture high-level discriminative representations. U-Net segmentation enabled isolation of infected regions, while ResNet-50 provided robust feature embeddings that enhanced separability between visually similar disease classes. Experimental evaluation demonstrated that the baseline U-Net + SVM approach achieved an accuracy of 84.2%, precision of 82.5%, recall of 83.1%, and F1-score of 82.8%. In contrast, the hybrid U-Net + ResNet-50 + SVM method yielded superior results with 91.6% accuracy, 90.8% precision, 91.2% recall, and 91.0% F1-score, reflecting an improvement of approximately 7.4%.
Deteksi Kecurangan Akademik Berdasarkan Dokumen Tulisan Tangan Menggunakan Siamse ResNet Approach Azhar Andika Putra; Bakhtiar K; Firga Abel Astiawan; Muhammad Al Hapiz
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.9388

Abstract

Academic cheating, particularly involving the forgery of handwriting documents, has become a significant challenge in the field of education. One of the most difficult modes to detect is identity misuse, where an individual writes or completes tasks on behalf of someone else. This study aimed to develop an academic cheating detection model based on handwriting using the Siamese Network approach and cosine similarity. The experiment was conducted using an HP Z8 G5 device equipped with two NVIDIA RTX 6000 GPUs, and the dataset used came from Universitas Sjakhyakirti. The dataset consisted of 101,475 pairs of handwriting images, each labeled as 1 (similar) for pairs from the same writer and 0 (dissimilar) for pairs from different writers. The data was divided into 70% for training, 15% for validation, and 15% for testing. This research dataset was sourced from handwriting documents of 450 different students, consisting of 450 positive pairs (label = 1) and 101,025 negative pairs (label = 0). The model was evaluated using a cosine similarity threshold of 0.5, with training accuracy reaching 95.34%, validation accuracy at 84.12%, and testing accuracy at 83.87%. This study contributes to the development of a handwriting-based academic cheating detection system that can be implemented in higher education institutions.
Klasifikasi Spesies Ikan di Sumatera Selatan Berdasarkan Citra Bawah Air Menggunakan ResNet-50 Lemi Iryani; Nia Umilizah; Firga Abel Astiawan; Muhammad Al Hapiz
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.9389

Abstract

Fish species classification in underwater ecosystems posed a significant challenge, particularly due to poor lighting that affected the quality of underwater images and decreased the accuracy of species identification. This study aimed to improve the accuracy of fish species classification in South Sumatra based on underwater images by utilizing the Super-Resolution Generative Adversarial Network (SRGAN) to enhance image quality and ResNet-50 for species classification. The research employed a Dell XPS 13 9310 device with an Intel Core i7 processor and 16GB of RAM for model training. Fish image data were collected from Google Images and YouTube according to predefined fish species, including Oreochromis mossambicus (Mujair), Oreochromis niloticus (Nila), Johnius trachycephalus (Gulamah), Eleutheronema tetradactylum (Senangin), and Chanos chanos (Bandeng). The data was divided into 70% for training, 15% for validation, and 15% for testing. The experimental results showed that the developed model achieved a training accuracy of 94.10%, validation accuracy of 88.25%, and testing accuracy of 84.68%. This research contributed to the field of underwater image classification and can be applied to conservation and monitoring of fish species in aquatic ecosystems.
Co-Authors Azhar Andika Putra Bakhtiar K Lambardo, Fakhri Lemi Iryani Muhammad Al Hapiz Nia Umilizah Putri Andromeda Putri Maharani