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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ILMU KELAUTAN: Indonesian Journal of Marine Sciences Jurnal Keteknikan Pertanian IPTEK Journal of Proceedings Series PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Ilmu Komputer dan Agri-Informatika International Journal of Advances in Intelligent Informatics JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Science and Mathematics Education Building of Informatics, Technology and Science Journal of Robotics and Control (JRC) Jurnal Pusat Inovasi Masyarakat Cylinder : Jurnal Ilmiah Teknik Mesin Jurnal Pengabdian Rekayasa dan Wirausaha
Karlisa Priandana
Computer Science Department Faculty Of Mathematics And Natural Sciences IPB University (Bogor Agricultural University)
Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences

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Journal : JOIN (Jurnal Online Informatika)

 1 
Texture Analysis of Citrus Leaf Images Using BEMD for Huanglongbing Disease Diagnosis Sumanto; Agus Buono; Karlisa Priandana; Bib Paruhum Silalahi; Elisabeth Sri Hendrastuti
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1075

Abstract

Plant diseases significantly threaten agricultural productivity, necessitating accurate identification and classification of plant lesions for improved crop quality. Citrus plants, belonging to the Rutaceae family, are highly susceptible to diseases such as citrus canker, black spot, and the devastating Huanglongbing (HLB) disease. Traditional approaches for disease detection rely on expert knowledge and time-consuming laboratory tests, which hinder rapid and effective disease management. Therefore, this study explores an alternative method that combines the Bidimensional Empirical Mode Decomposition (BEMD) algorithm for texture feature extraction and Support Vector Machine (SVM) classification to improve HLB diagnosis. The BEMD algorithm decomposes citrus leaf images into Intrinsic Mode Functions (IMFs) and a residue component. Classification experiments were conducted using SVM on the IMFs and residue features. The results of the classification experiments demonstrate the effectiveness of the proposed method. The achieved classification accuracies, ranging from 61% to 77% for different numbers of classes, the results show that the residue component achieved the highest classification accuracy, outperforming the IMF features. The combination of the BEMD algorithm and SVM classification presents a promising approach for accurate HLB diagnosis, surpassing the performance of previous studies that utilized GLCM-SVM techniques. This research contributes to developing efficient and reliable methods for early detection and classification of HLB-infected plants, essential for effective disease management and maintaining agricultural productivity.
Multi-Platform Detection of Melon Leaf Abnormalities Using AVGHEQ and YOLOv7 Ishak, Sahrial Ihsani; Priandana, Karlisa; Wahjuni, Sri
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1441

Abstract

This research develops a multiplatform system for detecting abnormalities in melon leaves, integrating an Internet of Things (IoT) approach using Jetson Nano, a Streamlit-based website, and a mobile application for real-time monitoring. The system employs preprocessing with Average Histogram Equalization (AVGHEQ) to enhance image quality, followed by modeling with the YOLOv7 algorithm on a dataset of 469 training images and 52 test images, validated through 5-fold cross-validation. The model achieved a mean Average Precision (mAP) of 84% with an inference detection time of 4.5 milliseconds. Implementation on Jetson Nano resulted in a 25% increase in CPU usage (from 25% to 50%) and a 20% increase in RAM usage (from 70% to 90%). By combining these platforms and leveraging robust data preprocessing and modeling techniques, the system provides an accessible, efficient, and scalable solution for agricultural monitoring, enabling farmers to address plant health issues promptly and effectively.
Texture Analysis of Citrus Leaf Images Using BEMD for Huanglongbing Disease Diagnosis Sumanto; Buono, Agus; Priandana, Karlisa; Paruhum Silalahi, Bib; Sri Hendrastuti, Elisabeth
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1075

Abstract

Plant diseases significantly threaten agricultural productivity, necessitating accurate identification and classification of plant lesions for improved crop quality. Citrus plants, belonging to the Rutaceae family, are highly susceptible to diseases such as citrus canker, black spot, and the devastating Huanglongbing (HLB) disease. Traditional approaches for disease detection rely on expert knowledge and time-consuming laboratory tests, which hinder rapid and effective disease management. Therefore, this study explores an alternative method that combines the Bidimensional Empirical Mode Decomposition (BEMD) algorithm for texture feature extraction and Support Vector Machine (SVM) classification to improve HLB diagnosis. The BEMD algorithm decomposes citrus leaf images into Intrinsic Mode Functions (IMFs) and a residue component. Classification experiments were conducted using SVM on the IMFs and residue features. The results of the classification experiments demonstrate the effectiveness of the proposed method. The achieved classification accuracies, ranging from 61% to 77% for different numbers of classes, the results show that the residue component achieved the highest classification accuracy, outperforming the IMF features. The combination of the BEMD algorithm and SVM classification presents a promising approach for accurate HLB diagnosis, surpassing the performance of previous studies that utilized GLCM-SVM techniques. This research contributes to developing efficient and reliable methods for early detection and classification of HLB-infected plants, essential for effective disease management and maintaining agricultural productivity.
Co-Authors Agus Buono Ahmad Zulfikar S Alex Coccia Arsy Novita Syahada Arsy Novita Syahada Auzi Asfarian Basir, Nurlida Bib Paruhum Silalahi Budhi Hascaryo Iskandar Diah Zakiah Doli Aryo Dani Sinaga Doli Aryo Dani Sinaga Elisabeth Sri Hendrastuti Evy Damayanthi Farohaji Kurniawan Handoko, Dadang Hasan, Rozaimi Che Hendra Rahmawan Hendra Rahmawan Henry Munandar Manik Herianto Herianto Hestirianoto, Totok Hidayat Hidayat Hidayat Husin Alatas Iis Rodiah Imantho, Harry Indra Jaya Irene Pricella Situmorang Irene Pricella Situmorang Irman Hermadi Ishak, Sahrial Ihsani ISKANDAR ZULKARNAEN SIREGAR Islamy, Fajrul Kahfi Gunardi Karina Eku Dwinanda Gunawan Kudang Boro Seminar Laksono Trisnantoro Leo P. Ligthart Ligar, Bonang Waspadadi M Alwan Farhandany M. Cholid Mawardi Medria Kusuma Dewi Hardhienata Melinda Melinda Michael Julyus Christopher Manullang Mohamad Solahudin Muhammad Hazim Muhammad Irhamsyah Muhammad Rafi Muttaqin Mukhlis Mukhlis Muttaqin, Muhammad Rafi Nazala Hafidhullah Nugraha Edhi Suyatma Peter John Morley Puspita Kartika Sari Puspita Kartika Sari Rahmat, Ayi Rini Safitri RR. Ella Evrita Hestiandari Santoso, Syifa Afnani Sarah Arisanti Seminar, Annisa Utami Shelvie Nidya Neyman Siregar, Ardinsyah Sri Hendrastuti, Elisabeth Sri Nurdiati Sri Wahjuni Sukarman Sumanto, Sumanto Supiyandi, Abi Try Kusuma Wardana Utama, Muhammad Jaka Wisnu Ananta Kusuma WULANDARI Yandra Arkeman Yunidar