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Menampilkan 1–2 dari 2 artikel
LDWFOX Optimization for Hyperparameter Tuning of Inception CNN in UAV-Based Vegetation Density Mapping
Pramunendar, Ricardus Anggi
; Alomoush, Ashraf
; Prabowo, Dwi Puji
; Megantara, Rama Aria
; Alzami, Farrikh
; Winarsih, Nurul Anisa Sri
; Pergiwati, Dewi
; Shidik, Guruh Fajar
Vegetation density classification from UAV imagery is a practical necessity in fire-prone landscapes, since fuel load on the ground directly informs risk management decisions. Convolutional neural networks handle this classification reasonably well, but good performance requires careful hyperparameter tuning, and manual trial and error produces results that are unstable and hard to reproduce. This study proposes LDW-FOX, a modified FOX metaheuristic using a Linearly Decreasing Weight mechanism t...
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DEEP LEARNING JARINGAN SARAF TIRUAN UNTUK PEMECAHAN MASALAH DETEKSI PENYAKIT DAUN APEL
Sutriawan, Sutriawan
; Fanani, Ahmad Zainul
; Alzami, Farrikh
; Basuki, Ruri Suko
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN)
Vol 11
, No 1
(2023)
Diseases on apple leaves are becoming a major issue for apple growers since they can cause the crop to fail. Due to the diversity of diseases that can affect apple leaves, it can be challenging for farmers to determine the cause of leaf damage. The purpose of this research is to evaluate a convolutional neural network (CNN) method for its potential use in solving the problem of apple leaf disease identification. Four types of illness are dealt with: normal, multi-illness, rusty, and scabby. Many...
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