Ekstraksi Fitur Bangunan Secara Cepat pada  Foto UAV Menggunakan Metode  Deep Residual Neural Network Berbasis FCN

Setiaji, Danang

doi:10.14710/elipsoida.2019.4883

Cited by 2 publications

(1 citation statement)

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“…Penelitian terkait arsitektur ResNet penelitian oleh [17] [18].Pada penelitian ini menerapkan metode CNN menggunakan arsitektur ResNet-50 karena menurut [19] ResNet-50 lebih mudah untuk dimaksimalkan dan dapat memperoleh akurasi tinggi.…”

Section: Pendahuluanunclassified

Pengaruh Hyperparameter Convolutional Neural Network Arsitektur ResNet-50 Pada Klasifikasi Citra Daging Sapi dan Daging Babi

Lasniari

Jasril

Sanjaya

et al. 2022

JNKTI

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Abstrak - Kasus kecurangan pedagang mencampur daging sapi dengan daging babi masih terjadi hingga saat ini. Membedakan daging sapi dan babi dapat dilakukan dengan mengamati secara langsung satu persatu, tetapi hal ini dapat dilakukan oleh para ahli, Tetapi secara kasat mata masih sulit membedakannya. Perilaku pedagang seperti ini sangat merugikan konsumen khususnya pemeluk agama Islam karena berkaitan dengan makanan yang halal atau haram. Pada penelitain ini menggunakan metode Deep Learning untuk klasifikasi citra dengan Convolutional Neural Network (CNN) arsitektur ResNet-50. Jumlah data sebanyak 457 citra yang terbagi menjadi 3 kelas, yaitu daging babi, daging oplosan dan daging sapi. Setiap kelas memiliki ukuran gambar yang sama yaitu 300 x 300 pixel. Pembagian data menggunakan split data dengan perbandingan 70% data uji : 30% data uji, 80% data latih : 20% data uji, dan 90% data latih : 10% data uji. Hasil dari pengujian model dengan Confusion Matrix menunjukkan performa klasifikasi tertinggi dengan 100% accuracy, 100% precision, dan 100% recall, pada data citra asli dengan penggunaan batch size 32, 0.001 learning rate, epoch 75 dan split data 90% : 10%.Kata kunci: Convolutional Neural Network, Daging Babi dan Sapi, Deep Learning, Klasifikasi Citra, ResNet Abstract - Traders mixing beef and pork are still committing fraud today. Although professionals can discern between beef and pork by watching them one by one, it is still impossible to do so with the naked eye. This kind of behavior is very detrimental to consumers, especially Muslims because it is related to halal or haram food. This research uses Deep Learning method to classify images with Convolutional Neural Network (CNN) ResNet-50 architecture. The number of data is 457 images which are divided into 3 classes, namely pork, mixed meat and beef. Each class has the same image size, which is 300 x 300 pixels. data distribution using split data with a comparison of 70% training data: 30% test data, 80% training data: 20% test data, and 90% training data: 10% test data. The results of model testing using the Confusion Matrix show the highest classification performance with 100% accuracy, 100% precision, and 100% recall, on the original image data using batch size 32, 0.001 learning rate, epoch 75 and split data 90%: 10%..Kata kunci: Convolutional Neural Networ, Deep Learning, Image Classification, Pork and Beef, ResNet

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Section: Pendahuluanunclassified

Pengaruh Hyperparameter Convolutional Neural Network Arsitektur ResNet-50 Pada Klasifikasi Citra Daging Sapi dan Daging Babi

Lasniari

Jasril

Sanjaya

et al. 2022

JNKTI

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A Simple But Effective Approach of Building Footprint Extraction in Topographic Mapping Acceleration

Susetyo¹,

Rizaldy²,

Hariyono³

et al. 2021

Indonesian J. Geosci.

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Topographic mapping using stereo plotting is not effective, because it takes much time and labour-intensive. Thus, this research was conducted to find the effective way to extract building footprint for mapping acceleration from LiDAR data. Building extraction method in this process comprises four steps: ground/non-ground filtering, building classification, segmentation, and building extraction. Classification of ground and non-ground classes was performed using Adaptive-TIN Surface algorithm. Non-ground points from filtering process were classified as building with the algorithm based on multiscale local dimensionality to separate points at the maximum separability plane. Segmentation using segment growing was used to separate each building, so boundary detection could be conducted for each segment to create boundary of each building. Lastly, building extraction was conducted through three steps: boundary point detection, building delineation, and building regularization. With ten samples and step 0.5, classification resulted in quality and miss factor of 0.597 and 0.524, respectively. The quality was improved by segmentation process to 0.604, while miss factor was getting worse to 0.561. Meanwhile, on the average shape index value from extracted building had 0.02 difference, and the number of errors was 30% for the line segment comparison. Regarding positional accuracy using centroid accuracy assessment, this method could produce RMSE of 1.169 m.

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Ekstraksi Fitur Bangunan Secara Cepat pada Foto UAV Menggunakan Metode Deep Residual Neural Network Berbasis FCN

Cited by 2 publications

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Pengaruh Hyperparameter Convolutional Neural Network Arsitektur ResNet-50 Pada Klasifikasi Citra Daging Sapi dan Daging Babi

Pengaruh Hyperparameter Convolutional Neural Network Arsitektur ResNet-50 Pada Klasifikasi Citra Daging Sapi dan Daging Babi

A Simple But Effective Approach of Building Footprint Extraction in Topographic Mapping Acceleration

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