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All Journal International Journal of Electrical and Computer Engineering Techno.Com: Jurnal Teknologi Informasi MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika JURIKOM (Jurnal Riset Komputer) Jurnal Review Pendidikan dan Pengajaran (JRPP) JISKa (Jurnal Informatika Sunan Kalijaga) Journal of Computer System and Informatics (JoSYC) International Journal of Advances in Data and Information Systems Jurnal Nasional Teknik Elektro dan Teknologi Informasi Servis : Jurnal Pengabdian dan Layanan kepada Masyarakat
Irwan Budi Santoso
Universitas Islam Negeri Maulana Malik Ibrahim, Malang
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Classification of Vegetation Land Cover Area Using Convolutional Neural Network Galib, Galan Ramadan Harya; Santoso, Irwan Budi; Crysdian, Cahyo
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3050

Abstract

The decrease and reduction of vegetation land or forest area over time has become a serious and significant problem to be considered. Increasing the Earth’s temperature is a consequence of deforestation, which can contribute to climate change. The other issues that researchers face concern diversity and various objects in satellite imagery that may be difficult for computers to identify using traditional methods. This research aims to develop a model that can classify vegetation land cover areas on high-resolution images. The data used is sourced from the ISPRS (International Society for Photogrammetry and Remote Sensing) Vaihingen. The model used is a Convolutional Neural Network (CNN) with a VGG16-Net Encoder architecture. Tests were conducted on eight scenarios with training and test data ratios of 80:20% and 70:30%. The classifier method that we employed in this research is argmax and threshold. We also compared the performance of Neural Networks with two hidden layers and three hidden layers to investigate the impact of adding another layer on the Neural Network's performance in classifying vegetation land cover areas. The results show that using the threshold classifier method can save training time compared to the argmax method. By increasing the number of hidden layers in the neural network, model performance improves, as shown by increases in recall, accuracy, and F1-score metrics. However, there is a slight decrease in the precision metric. The model achieved its best performance with a precision (Pre) of 99.5%, accuracy (Acc) of 83.3%, and F1-score (Fs) of 70.3%, requiring a training time (T-time) of 16 minutes and 41 seconds and an inference time (I-time) of 0.1535 seconds.
Klasifikasi Penyakit Padi Menggunakan Convolutional Neural Network (CNN) Berbasis Citra Daun Moh. Heri Susanto; Irwan Budi Santoso; Suhartono; Ahmad Fahmi Karami
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 3: Agustus 2025
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i3.18791

Abstract

Rice diseases significantly impact agricultural productivity, making classification models essential for accurately distinguishing rice leaf diseases. Various classification models have been proposed for image-based rice disease classification; however, further performance improvement is still required. This study proposes the use of a convolutional neural network (CNN) to classify rice diseases based on leaf images. The dataset used in this study included leaf images categorized into four conditions: leaf blight, blast, tungro, and healthy. In the initial stage, data preprocessing was conducted, including resizing, augmentation, and normalization. Following preprocessing, a custom CNN architecture was developed, consisting of four convolutional layers, four pooling layers, and three fully connected layers. Each convolutional layer employed a 3 × 3 kernel with a stride of 1 and ReLU activation, while the pooling layers used max pooling with a 3 × 3 kernel and a stride of 2. Using a batch size of 32 and the Adam optimizer, the best test performance was achieved with 100 training epochs and a learning rate of 0.0002, resulting in a training accuracy of 0.9930, a loss of 0.0221, and a test accuracy of 0.9647. Model evaluation demonstrated a balanced performance across precision, recall, and F1 score, each achieving 0.9647, indicating highly effective classification without bias toward any specific class. These findings suggest that the simplified CNN model can deliver competitive classification performance without the need for complex architectures or additional enhancement techniques. The proposed CNN model outperformed existing CNN architectures, such as Inception-ResNet-V2, VGG-16, VGG-19, and Xception.
Co-Authors Ahmad Fahmi Karami Ainul Yaqin Ambiya, Sri winduri Anwar, Aldian Faizzul Asep Sunarko Cahyani, Vivin Octavia Cahyo Crysdian Cahyo Crysdian Cahyo Crysdian Cahyo Crysdian Diantoro, Aris Dwi Purbo Yuwono Eko Agus Moh. Iqbal Galib, Galan Ramadan Harya Hamim Tohari Heni Prasetyo Ihda Noor Mujib Ihsan Bagus Fahad Arafat Mar Atul Ma’rifah Moh. Heri Susanto Mokhamad Amin Hariyadi Muhammad Ainul Yaqin Muhammad Faisal Nurussakinah, Nurussakinah Purwanto, Sofi Dwi Ririen Kusumawati Romadhani, Ahmad Shoffin Nahwa Utama Shoffin Nahwa Utama Sri Harini Suhartono Supriyono Supriyono Supriyono Zuhri, Abdurrozaq Ashshiddiqi