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All Journal International Journal of Electrical and Computer Engineering Techno.Com: Jurnal Teknologi Informasi Bulletin of Electrical Engineering and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTECOMS: Journal of Information Technology and Computer Science Jurnal Mantik Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Teknimedia: Teknologi Informasi dan Multimedia Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Bangkit Indonesia Jurnal Pendidikan Indonesia (Japendi)
Dhani Ariatmanto
Universitas Amikom Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice

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Deteksi Karakter Aksara Jawa Menggunakan YOLO11 Pendekatan Deep Learning untuk Pelestarian Warisan Budaya Digital Eko Rahmad Darmawan; Dhani Ariatmanto
Jurnal Bangkit Indonesia Vol 14 No 2 (2025): Bulan Oktober 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i2.450

Abstract

Javanese script represents a significant cultural heritage of the Indonesian archipelago that faces extinction threats due to Latin alphabet dominance and minimal users capable of writing with this traditional script. This research aims to develop a Javanese character detection system using You Only Look Once version 11 (YOLO11) algorithm to support cultural preservation efforts through efficient digitalization. The research methodology employs an experimental approach with deep learning, where the Javanese script dataset consisting of 20 basic characters plus background class was obtained from Kaggle and preprocessed using Roboflow with data augmentation techniques. The YOLO11 model was implemented with SGD optimizer, 640px image size, and trained for 500 epochs to achieve optimal convergence. YOLO11 architecture integrates advanced components such as C3K2 blocks, Spatial Pyramid Pooling-Fast (SPPF), and Cross-scale Pixel Spatial Attention (C2PSA) to enhance multiscale feature extraction capabilities. Model performance evaluation utilized confusion matrix with accuracy, precision, recall, and F1-score metrics. Research results demonstrate that the YOLO11 model achieved an overall accuracy of 81.00% with macro-averaged precision of 86.28%, macro-averaged recall of 87.25%, and macro-averaged F1-score of 86.41%. Model performance distribution shows 7 classes with high performance (F1-score ≥ 90%), 9 classes with medium performance (80-90%), and 4 classes with low performance (<80%). The "nga" class achieved perfect performance of 100%, while the "ha" class showed the lowest performance with an F1-score of 68.09%. This research successfully improved accuracy compared to previous methods using backpropagation neural networks (74%) and conventional backpropagation (59.5%), although challenges remain in detecting characters with similar shapes and handling background class. The main contribution is the first implementation of YOLO11 for Javanese script detection, opening opportunities for developing more efficient and accurate ancient literature digitalization systems.
Perbandingan Metode Ekstraksi Fitur LBP, GLCM, dan Canny dalam Klasifikasi Penyakit Daun Padi dengan KNN Roy Jordy; Dhani Ariatmanto
Jurnal Bangkit Indonesia Vol 14 No 2 (2025): Bulan Oktober 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i2.452

Abstract

Accurate and timely identification of rice leaf diseases plays a crucial role in supporting early disease control efforts in agriculture. This study aims to compare the performance of three image feature extraction methods—Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), and Canny Edge Detection—in classifying three types of rice leaf diseases: Bacterial leaf blight, Brown spot, and Leaf smut. Each method was evaluated based on its confusion matrix as well as key performance metrics, including accuracy, precision, recall, and F1-score. Experimental results show that LBP achieved the highest classification performance with an accuracy of 92.06%, followed by GLCM at 78.57% and Canny at 66.67%. In addition to accuracy, LBP also outperformed the other methods across all evaluation metrics. These findings indicate that the local texture features captured by LBP are more effective in distinguishing disease types compared to the global texture features from GLCM and edge-based features from Canny. Therefore, LBP is recommended as a superior feature extraction method for automated classification systems of rice leaf diseases based on digital imagery.
Analisis Perbandingan Kinerja Model YOLO11 dan YOLOv8 dalam Identifikasi Penyakit pada Daun Tomat Muhammad Arif Kholis Majid; Dhani Ariatmanto
Jurnal Bangkit Indonesia Vol 14 No 2 (2025): Bulan Oktober 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i2.459

Abstract

Diseases on tomato leaves can reduce the quality and quantity of agricultural yields, as well as affect market prices. This study compares the effectiveness of the YOLO11 and YOLOv8 models in detecting diseases on tomato leaves with traditional CNN-based models such as VGG-16 and Inception-V3. The results show that the YOLO11 model provides the best accuracy of 99.4%, followed by YOLOv8 with 98.5%, both excelling in real-time detection. CNN-based models like VGG-16 and Inception-V3 have high accuracy (99% and 93.8%), but are slower in computation. The ensemble model of VGG-16 and NASNet Mobile achieves an accuracy of 98.7%, but is slightly lower than YOLO11. The YOLO model is more efficient in detection speed, making it a better choice for field applications. This study shows that YOLO11 offers the best combination of accuracy and detection speed for a real-time plant disease detection system.
Co-Authors Agus Fatkhurohman Alva Hendi Muhammad Ari Rudiyan Bambang Soedijono Bima Widianto Dewi Yustika Lakoro Dloifur Rohman Al Ghifari Eko Rahmad Darmawan Ema Utami Ferda Ernawan M. Iqbal Abdullah Sukri2 Maulana Brama Shandy Ma’ruf Aziz Muzani Minggu, Emanuel Muhammad Arif Kholis Majid Rizki Firdaus Mulya Roy Jordy Syifa Nur Fauziah