Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal SISFOTENIKA Jurnal Ilmiah Sinus
Farrel Witamajaya
Politeknik Akbara Surakarta
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Implementasi Data Augmentation Random Erasing dan GridMask pada CNN untuk Klasifikasi Batik Chan Uswatun Khasanah; Angkin Kusuma Pertiwi; Farrel Witamajaya
SISFOTENIKA Vol 13, No 1 (2023): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/jst.v13i1.1274

Abstract

Penelitian ini bertujuan untuk mengetahui pengaruh data augmentation Random Erasing dan GridMask pada klasifikasi batik dengan 550 gambar yang terbagi menjadi 5 kelas, yaitu Ceplok, Kawung, Lereng, Nitik, dan Parang. Dataset terbagi menjadi data train, validation, dan test dengan perbandingan 70% : 20% : 10% sehingga masing-masing data train dan validation terdiri dari 500 gambar dan jumlah data test adalah 50 gambar. Kami mengimplementasikan data augmentation Random Erasing dan GridMask pada model pre-trained VGG16 dengan metode transfer learning dan fine-tuning (melakukan training pada block5 convolutional layer).Berdasarkan training menggunakan model pre-trained VGG16 pada dataset batik dengan membandingkan metode fine-tuning dan transfer learning menunjukkan bahwa metode fine-tuning menghasilkan akurasi lebih tinggi daripada transfer learning. Training tanpa data augmentation dapat menghasilkan akurasi yang lebih tinggi daripada saat menggunakan data augmentation, namun masih mengalami overfitting. Overfitting tersebut dapat diperkecil dengan mengimplementasikan data augmentation Random Erasing dan GridMask.
Deteksi Wajah Bermasker Menggunakan Deep Neural Network dan Tensorflow Rian Dwi Yulian Prakoso; Sriyati Sriyati; Nabil Makarim; Farrel Witamajaya
Jurnal Ilmiah SINUS Vol 21, No 1 (2023): Vol. 21 No. 1 Januari 2023
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/sinus.v21i1.629

Abstract

The use of face masks is an important part of life in the midst of the Covid-19 pandemic which has been designated as a global pandemic, people are urged to cover their faces when in public areas to avoid the spread of the virus. The use of these face masks has raised serious questions regarding face detection systems. This study used Deep Neural Network and Tensorflow methods to detect faces both using masks and without masks. This study used two datasets, a face collection dataset without a mask and a face group using a mask. The result of this study was the detection of faces with masks trained on the dataset achieved 98% accuracy in training, and for testing got 100% accuracy on faces without masks, and 99.99% accuracy for face with mask. 
Co-Authors Angkin Kusuma Pertiwi Chan Uswatun Khasanah Nabil Makarim Rian Dwi Yulian Prakoso Sriyati Sriyati