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All Journal Indonesian Journal of Artificial Intelligence and Data Mining ULIN: Jurnal Hutan Tropis Journal of Soft Computing Exploration
Safei, Rahmat
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Computer Science & IT Environmental Science

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Classification of crown density and foliage transparency scale for broadleaf tree using VGG-16 Octarina, Nur Ayu; Andrian, Rico; Safei, Rahmat
Journal of Soft Computing Exploration Vol. 4 No. 4 (2023): December 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i4.251

Abstract

Crown density and foliage transparency are important parameters for tree crown conditions. Previously, observers carried out crown density and foliage transparency assessments manually, which could be a less efficient process.This research aims to use the VGG-16 deep learning architecture to classify the density and transparency of broadleaves tree crowns. In this study, broadleaves tree crown datasets were collected for four types of broadleaves tree: cacao (theobroma cacao), durian (durio zibethinus), rubber (havea brasiliensis), candlenut (aleurites moluccana); then the data is labeled based on the crown density and foliage transparency scale card. The research applies resize and augmentation preprocessing. The model training process uses a scenario of 80% train data, 10% test data, and 10% validation data. After training using the VGG-16 model, the test results showed impressive accuracy, with the highest accuracy reaching 98.40% for candlenut trees, rubber (96.00%), cacao (92.00%), and durian (86.60%). This research shows quite good results in classifying the scale of crown density and foliage transparency with four types of broadleaves tree (cacao, durian, rubber and candlenut) using VGG-16.
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.
Analisis Geospasial Lahan Agroforestri Untuk Budidaya Lebah Kelulut (Trigona sp.) di Desa Karang Jaya, Kecamatan Merbau Mataram, Kabupaten Lampung Selatan Febrian, Ardi; Safei, Rahmat; Darmawan, Arief; Asmarahman, Ceng; Hidayat, Wahyu
ULIN: Jurnal Hutan Tropis Vol 9, No 2 (2025)
Publisher : Fakultas Kehutanan Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32522/ujht.v9i2.21125

Abstract

Dalam mengoptimalisasikan pengelolaan dan pemanfaatan budidaya lebah kelulut terdapat beberapa faktor yang harus diperhatikan, antara lain kondisi iklim dan vegetasi; ketersediaan sumber pakan; dan praktik serta inovasi dalam budidaya lebah kelulut. Melalui analisis daya dukung, kegiatan budidaya lebah kelulut dapat dioptimalisasikan berdasarkan kesesuaian antara syarat hidup lebah kelulut dengan kondisi ruang yang menjadi lokasi budidaya lebah kelulut melalui pencermatan terhadap faktor lingkungan, faktor habitat, faktor iklim, dan faktor vegetasi. Penelitian ini bertujuan untuk mengetahui peran dan potensi daya dukung dari lahan agroforestri di Desa Karang Jaya, Kecamatan Merbau Mataram, Kabupaten Lampung Selatan. Hasilnya menunjukkan bahwa berdasarkan uji Maxent kesesuian habitat dan daya dukung untuk budidaya lebah kelulut di Desa Karang Jaya dapat diklasifikasikan Tinggi, dengan variabel ketinggian (36,2%), variabel NDVI (24,3%), dan variabel suhu (19,4%) memiliki pengaruh yang signifikan terhadap tingkat kehadiran dan kesesuaian habitat lebah kelulut.
Co-Authors Andrian, Rico Arief Darmawan Ceng Asmarahman Febrian, Ardi Gandadipoera, Faishal Hariz Makaarim Hernani, Livia Ayu Istoria Octarina, Nur Ayu Tristiyanto Tristiyanto Wahyu Hidayat