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Tomato Ripeness Classification Using Transfer Learning Approach with ResNet50 CNN Model
Rohman, Habibur
; Nafi'iyah, Nur
; Bettaliyah, Azza Abidatin
; Rohman, Habibur
; Nafi'iyah, Nur
; Bettaliyah, Azza Abidatin
JUISI : Jurnal Ilmiah Sistem Informasi
Vol 4
, No 1
(2025)
Tomat merupakan komoditas hortikultura bernilai ekonomi tinggi dengan permintaan pasar yang luas, baik domestik maupun internasional. Salah satu tantangan utama dalam distribusinya adalah menjaga kualitas produk, khususnya tingkat kematangan buah. Penilaian kematangan yang akurat sangat penting karena berdampak pada masa simpan, cita rasa, dan kelayakan konsumsi. Namun, metode konvensional yang mengandalkan pengamatan visual manusia cenderung subjektif, memerlukan banyak tenaga kerja, dan kurang...
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Analisis peramalan data kekeringan lahan pertanian di kabupaten lamongan menggunakan metode Fuzzy Time Series-Markov Chain
Faroh, Rifky Aisyatul
; Nabilah, Salwa
; Affandy, Nur Azizah
; Nafi'iyah, Nur
; Said, Muhammad
AKSIOMA : Jurnal Matematika dan Pendidikan Matematika
Vol 16
, No 2
(2025)
The agricultural sector in Lamongan Regency is severely affected by the threat of drought, potentially affecting food production and farmers' welfare. This study aims to forecast the area of agricultural drought with a very severe category in Lamongan Regency using the Fuzzy Time Series-Markov Chain (FTS-MC) method. The data used includes the average drought area based on the processing of Landsat 8 imagery from 2020–2024, rainfall data, and historical drought data from the Regional Disaster Man...
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Garbage Image Classifier using Modified ResNet-50
Santoso, Bagus Dwi
; Universitas Islam Lamongan
; Nafi'iyah, Nur
; Universitas Islam Lamongan
Telematika
Vol 17
, No 2
(2024)
This research proposes a deep learning model pretrained with ResNet-50 to classify 12 types of garbage. The model uses a modified ResNet-50 architecture with the Adamax and Adadelta optimizers and varying learning rates (0.1, 0.01, and 0.001). Six experiments were conducted to determine the most optimal training parameter configuration for the proposed model. Results show that the model performed best with the Adadelta optimizer and a learning rate of 0.1, achieving a validation accuracy of 93.8...
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CNN Architecture for Classifying Types of Mango Based on Leaf Images
Nafi'iyah, Nur
; Maknun, Jauharul
; Universitas Islam Lamongan
Telematika
Vol 14
, No 2
(2021)
In such conditions, it is necessary to have a system that can automatically classify plant species or identify types of plant diseases using either machine learning or deep learning. The plant classification system for ordinary people who are not familiar with the field of crops is not an easy job, it requires in-depth knowledge of the field from the experts. This study proposes a system for identifying mango plant species based on leaves using the CNN method. The reason for proposing the CNN me...
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