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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) G-Tech : Jurnal Teknologi Terapan 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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Journal : Jurnal Nasional Teknik Elektro dan Teknologi Informasi

 1 
Citra Tekstur Terbaik Untuk Gaussian Naïve Bayes Dengan Interpolasi Nearest Neighbor Irwan Budi Santoso; Shoffin Nahwa Utama; Supriyono
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 1: Februari 2024
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v13i1.8730

Abstract

One of the factors affecting the performance of the Gaussian naïve Bayes classifier (GNBC) in texture image classification is the image size (dimensions). Image size is one of the best texture image criteria besides its pixel value. In this study, a method is proposed to obtain the size of the best texture image for GNBC by nearest neighbor (NN) interpolation optimization. The best texture image size with interpolated pixel values makes GNBC able to distinguish texture images in each class with the highest performance. The first step of the proposed method was to determine the texture image size for training through a combination of row and column sizes in the optimization process. The next important step in generating the new texture images was resizing each of the original texture images using NN interpolation. The next step was to build GNBC based on the new image from interpolation and determine the classification accuracy. The last step was to select the best texture image size based on the largest classification accuracy value as the first criterion and image size as the second criterion. The evaluation of the proposed method was carried out using texture image data from the CVonline public dataset involving several test scenarios and interpolation methods. The test result shows that in scenarios involving five classes of texture images, GNBC with NN interpolation gives the smallest classification accuracy value of 89% and the largest 100% at the best image size, 14 × 32 and 47 × 42, respectively. In scenarios involving small to large class numbers, GNBC with NN interpolation provides classification accuracy of 81.6%–95%. From these results, GNBC with NN optimization gives better results than other nonadaptive interpolation methods (bilinear, bicubic, and Lanczos) and principal component analysis (PCA).
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 Arafat, Ihsan Bagus Fahad Asep Sunarko Cahyani, Vivin Octavia 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 Ma'muri, Ma'muri 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 Sugiharto, Tomy Ivan Suhartono Supriyono Supriyono Supriyono Syahreni, Syahreni Totok Chamidy Zarkoni, Ahmad Zuhri, Abdurrozaq Ashshiddiqi