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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknologi Informasi dan Ilmu Komputer Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer JURNAL REKAYASA TEKNOLOGI INFORMASI Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) JOMPA ABDI: Jurnal Pengabdian Masyarakat Data Sciences Indonesia (DSI) Jurnal Masyarakat Madani Indonesia Inovasi Teknologi Masyarakat
Hairah, Ummul
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Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Physics Public Health Social Sciences Other

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Journal : International Journal of Electrical and Computer Engineering

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Classification of tea leaf disease using convolutional neural network approach Hairah, Ummul; Septiarini, Anindita; Puspitasari, Novianti; Tejawati, Andi; Hamdani, Hamdani; Eka Priyatna, Surya
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3287-3294

Abstract

Leaf diseases on tea plants affect the quality of tea. This issue must be overcome since preparing tea drinks requires high-quality tea leaves. Various automatic models for identifying disease in tea leaves have been developed; however, their performance is typically low since the extracted features are not selective enough. This work presents a classification model for tea leaf disease that distinguishes six leaf classes: algal spot, brown, blight, grey blight, helopeltis, red spot, and healthy. Deep learning using a convolutional neural network (CNN) builds an effective model for detecting tea leaf illness. The Kaggle public dataset contains 5,980 tea leaf images on a white background. Pre-processing was performed to reduce computing time, which involved shrinking and normalizing the image prior to augmentation. Augmentation techniques included rotation, shear, flip horizontal, and flip vertical. The CNN model was used to classify tea leaf disease using the MobileNetV2 backbone, Adam optimizer, and rectified linear unit (ReLU) activation function with 224×224 input data. The proposed model attained the highest performance, as evidenced by the accuracy value 0.9455.
Dipterocarpaceae trunk texture classification using two-stage convolutional neural network-based transfer learning model Wati, Masna; Puspitasari, Novianti; Hairah, Ummul; Widians, Joan Angelina; Tjikoa, Ade Fiqri
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6874-6882

Abstract

The importance of plant identification has been recognized by academia and industry. There have been several attempts to utilize leaves and flowers for identification. However, the trunk can also be helpful, especially for tall trees. In Borneo, the Dipterocarpaceae family are the main constituents of the tropical rainforest ecosystem. This research focuses on the classification of the dipterocarp family, which can reach a height of between 70 and 85 m. Leveraging convolutional neural network (CNN) models, this research proposes a two-stage transfer learning strategy. In the first stage, the pre-trained CNN models are refined by only modifying the classification layer while keeping the feature layer frozen. The second stage involves selecting and freezing several convolutional layers to adapt the model to classify dipterocarp stems. The dataset consists of 857 images of different dipterocarp species. Experiments show that the VGG16 model with a two-stage transfer learning strategy achieves a high accuracy of 98.246%. This study aims to accurately identify species, benefiting conservation and ecological studies by enabling fast and reliable tree species classification based on stem texture images.
Co-Authors Achmad, Rayhan Zidane Alameka, Faza Alqarani, Hudzaifah Anindita Septiarini, Anindita Arabi, Muhammad Amin Quthbi Arinda, Viona Asya Asmawati, Cici Az Zahrah, Rezha Nur Azzahra, Raudhya Budiman, Edy Budiman, Edy Eka Priyatna, Surya Erwin Erwin Fadila, Tandy Haerullah, Haerullah Hamdani Hamdani Hidayat, Irfan Arman Ismayanti, Rika Joan Angelina Widians, Joan Angelina Kamila, Vina Zahratun Kamila, Vina Zahrotun Manik, Filipus Adriel Masa, Amin Padmo Azam Maya Agustina Nursukamto, Muhammad Hadiseptian Puspitaningtyas, Dhea Puspitasari, Novianti Puspitasari, Novitanti Rama, Mardianto Tandi Ramadhan, Khefyn Rasid, Khairul Riyayatsyah, Riyayatsyah Sahriah, Sahriah Tejawati, Andi Tjikoa, Ade Fiqri Ulhaqi, Ahmad Dhiya Vicky Pranandika Wijaksana Wati, Masna Wijaya, M Rangga Yaqub Zasmadyansyah, Zasmadyansyah