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All Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Komputasi Indonesian Journal of Artificial Intelligence and Data Mining InComTech: Jurnal Telekomunikasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknoinfo Indonesian Journal of Social and Environmental Issues (IJSEI) Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Soft Computing Exploration Jurnal Pepadun Malcom: Indonesian Journal of Machine Learning and Computer Science JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Andrian, Rico
Ilmu Komputer FMIPA Unila
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Social Sciences

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Journal : Indonesian Journal of Artificial Intelligence and Data Mining

 1 
Implementation of Convolutional Neural Network for Classification of Density Scale and Transparency of Needle Leaf Types Diah Adi Sriatna; Rico Andrian; Rahmat Safei
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.26258

Abstract

Crown density and transparency are among the parameters in determining forest health using magic card. This is still less effective because it only relies on direct vision. Therefore, a more sophisticated and accurate application using digital image technology is needed. Convolutional Neural Network (CNN) is designed to help recognize objects in images with various positions. There are 1000 images of needle leaf types with ten classes of crown density and transparency for every kind of needle leaf, including araucaria heterophylla, cupressus retusa, pine merkusii, and shorea javanica, which are classified using AlexNet. AlexNet is a CNN architecture that has eight feature extraction layers. The AlexNet model succeeded in classifying coniferous trees on the scale of density and crown transparency with an accuracy level of 87.00% for araucaria heterophylla, cupressus retusa 96.00%, merkusii pine 86.00%, and shorea javanica 95.00%. Although some errors were still found in classification, this was caused by similar patterns and similar image positions. It is hoped that the results of this research will be used in monitoring forest health in the future.
Tree Damage Type Classification Based on Forest Health Monitoring Using Mobile-Based Convolutional Neural Network Gandadipoera, Faishal Hariz Makaarim; Andrian, Rico; safei, rahmat
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.29421

Abstract

One of the fundamental parts of surveying forest health conditions with Forest Health Monitoring (FHM) is to visually assess the damage experienced by trees under certain conditions. This visual assessment can be facilitated using a Convolutional Neural Network (CNN) which involves building the MobileNetV2 model architecture. The model was trained using 1600 image data with 16 classes. The image data was pre-processed by resizing it to 224x224. The data was categorized into three categories: 80% was allocated for training, 10% for validation, and testing with 10% also. Training was done by changing the values from batches with a maximum of 100 epochs. The model was then incorporated into a mobile application using TensorFlow Lite and testing the application gave satisfactory results.  The model results get the best accuracy rate of 98.75% and a loss of 0.0497. This research concludes that the classification of tree damage types based on FHM with CNN can be done. For accurate results, the image provided by the user must be clear and reflect the actual damage observed on the tree.
Development of EfficientNet Model on Broad and Needles Leaved Species Tree Crowns with Forest Health Monitoring Method Hernani, Livia Ayu Istoria; Andrian, Rico; Safei, Rahmat; Tristiyanto, Tristiyanto
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37463

Abstract

Forest Health Monitoring (FHM) is a method for monitoring forest health conditions using various ecological indicators, such as tree canopy density and transparency. This research aims to evaluate the performance of the EfficientNet model in classifying the density and transparency values of broadleaf and coniferous tree canopies. The dataset consists of 3,956 tree canopy images collected from Tahura Wan Abdul Rachman (WAR), a conservation forest in Lampung, and is divided into 10 classes based on magic cards. Magic cards are a learning medium in the form of picture cards containing values of density and transparency. This research uses the EfficientNet-B0 architecture with certain training parameters. The results show that the EfficientNet-B0 model provides the best performance with an accuracy of 90.00%, a precision of 97.00%, a recall of 97.00%, and an F1-score of 97.00%. This research shows that EfficientNet can be used effectively to assist decision making related to automatic visual monitoring of forest health.
Co-Authors . Wamiliana Adi Pribadi, Raden Irwan Admi Syarif Admi Syarif Agatha Beny Himawan Ahmad Adi Wijaya, Ahmad Adi Akmal Junaidi Alkhadafi Saddam Simparico Ananto Danu Prasetyo Andikha Yunar Cornelius Dabukke Andriyan Hutomo Ardiansyah Ardiansyah Aristoteles, Aristoteles Astria Hijriani Astria Hijriani Astria Hijriani, Astria Ayu Taqiya Ulfa Basir Efendi Dedy Hermawan Dedy Miswar Destian ade anggi Sukma Diah Adi Sriatna Dian Riskiyana Didik Kurniawan Dwi Sakethi Dwi Sakethi Dwi Sakethi Dwi Sakethi Eka Fitri Jayanti Eko Septiawan Favorisen R. Lumbanraja Febi Eka Febriansyah Flaurensia Riahta Tarigan Florencia Irena Gandadipoera, Faishal Hariz Makaarim Heningtyas, Yunda Hernani, Livia Ayu Istoria Igo Febrianto Indrianti Indrianti Irawati, Anie Rose Ismail Indra Pratama Jannati Asri Safitri Kristina Ademariana Kurnia Muludi Lisa Suarni Lona Ertina M. Juandhika Rizky Machudor Yusman Maharani, Devi Malik Abdul Azis Malik Abdul Aziz Meizano Ardhi Muhammad Muhammad Chairuddin Muhammad Iqbal Muhammad Iqbal Muhammad, Meizano Ardhi Muhaqiqin Muhaqiqin Novita Dwilestari Octarina, Nur Ayu Prabowo, Rizky Prabowo, Rizky Pradana Marlando Qonitati Qonitati RA Dina Nia Pratiwi Raden Irwan Adi Pribadi Rahman Taufik Rahmat Safe'i Rahmat Safe'i Rahmat Safe'i Rahmat Safe’i Reda Meiningtiyas Rika Ningtias Azhari S Susiyani Safei, Rahmat Safitri, Jannati Asri Saipul Anwar Saipul Anwar Sholehurrohman, Ridho Sunita Agustina TANJUNG, AKBAR RISMAWAN Tri Maryono Tristiyanto Tristiyanto Utami, Noera Yudhiarti Verina, Vira Wamiliana Wamiliana Wartariyus Wartariyus Zuhri Nopriyanto