Pengenalan Tulisan Tangan Huruf Hijaiyah Menggunakan Convolution Neural Network Dengan Augmentasi Data

Pradika, Sunu Ilham; Nugroho, Budi; Puspaningrum, Eva Yulia

doi:10.33005/santika.v1i0.35

Cited by 4 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Selain menggunakan algoritma CNN dalam penelitian ini dilakukan pula optimasi yaitu Data Augmentation atau augmentasi data untuk menambah variasi data pada data latih. Selain itu digunakan pula arsitektur SIP-Net, dan didapatkan hasil akurasi sebesar 99,7% [11].…”

Section: Pengolahan Data Awalunclassified

Use of Augmentation Data and Hyperparameter Tuning in Batik Type Classification using the CNN Model

Auliaddina,

Arifin

2024

SISTEMASI

View full text Add to dashboard Cite

Batik adalah salah satu budaya seni Indonesia yang paling dikenal di dunia dan memiliki motif serta jenis batik tradisional yang berbeda-beda dan memiliki keunikannya sendiri-sendiri. Namun sayangnya, masih begitu banyak masayarakat Indonesia yang belum dapat membedakan jenis-jenis batik berdasarakan motifnya. Karena itulah dibutuhkan sebuah cara untuk membantu masyarakat agar mudah untuk dapat membedakan jenis-jenis batik berdasarkan motifnya. Penelitian ini bertujuan untuk mengklasifikasikan jenis-jenis batik berdasarkan motifnya dengan menggunakan model deep learning Convolutional Neural Network dengan menggunakan Data Augmentation dan Hyperparameter Tuning. CNN termasuk dalam jenis Deep Neural Network karena kedalaman jaringannya yang tinggi dan banyak diterapkan pada data citra. Selain itu diterapkan pula Data Augmentation dan Hyperparameter Tuning untuk mengurangi overfitting. Hasil dari penelitian ini menunjukkan model CNN yang menggunakan optimasi Data Augmentation dan Hyperparameter Tuning mendapatkan nilai akurasi validasi, presisi dan recall jauh lebih tinggi yaitu sebesar 66,67% dibandingkan dengan mode CNN yang tidak menggunakan Data Augmentation dan Hyperparameter Tuning yang memiliki akurasi validasi, presisi, dan recall sebesar 28,15%. Selain itu diantara Data Augmentation dan Hyperparameter Tuning, Data Augmentation lah yang paling mempengaruhi peningkatan akurasi validasi, presisi, dan recall dibandingkan Hyperparameter Tuning dengan peningkatan akurasi validasi menjadi 64% dari akurasi validasi sebesar 28,15%.

show abstract

Section: Pengolahan Data Awalunclassified

Use of Augmentation Data and Hyperparameter Tuning in Batik Type Classification using the CNN Model

Auliaddina,

Arifin

2024

SISTEMASI

View full text Add to dashboard Cite

show abstract

“…Machine learning is a computational algorithm or computer process that works based on historical data to improve performance in creating predictors. In machine learning, there are three learning methods, namely unsupervised learning, supervised learning, and reinforcement learning (Pradika, et al, 2020). In unsupervised learning, the training data used does not yet have a class, so the data are grouped based on the same characteristics.…”

Section: Introductionmentioning

confidence: 99%

Classification of Edelweiss Flowers Using Data Augmentation and Linear Discriminant Analysis Methods

Malau¹,

Mulyana²

2022

JAETS

View full text Add to dashboard Cite

Edelweiss is a plant that grows at a height, and is known as a perennial flower because it has beautiful petals and does not wilt easily. Although edelweiss in Indonesia is still in the same family as Leontopodium Alpinum, it turns out that the type of edelweiss found in the mountains of Indonesia is different from edelweiss found abroad. Therefore, in this study, an image processing system was developed that can classify the types of edelweiss flowers based on their image using Linear Discriminant Analysis to classify data into several classes based on the boundary line (straight line) obtained from linear equations. In this study, the types of edelweiss flowers used in this study were Anaphalis Javanica and Leontopodium Alpinum, the two types of edelweiss flowers were distinguished based on their color characteristics using hue and saturation values. The images used are 1500 images for training data and 450 test data images with a training and test data ratio of 70:30, so that the accuracy produced in the testing process is 99.77% in the Linear Discriminant Analysis method.

show abstract

Classification Of Javanese Script Using Convolutional Neural Network With Data Augmentation

Haromainy

Sihananto

Prasetya

et al. 2022

2022 IEEE 8th Information Technology International Seminar (ITIS)

View full text Add to dashboard Cite

Pengenalan Tulisan Tangan Huruf Hijaiyah Menggunakan Convolution Neural Network Dengan Augmentasi Data

Cited by 4 publications

References 21 publications

Use of Augmentation Data and Hyperparameter Tuning in Batik Type Classification using the CNN Model

Use of Augmentation Data and Hyperparameter Tuning in Batik Type Classification using the CNN Model

Classification of Edelweiss Flowers Using Data Augmentation and Linear Discriminant Analysis Methods

Classification Of Javanese Script Using Convolutional Neural Network With Data Augmentation

Contact Info

Product

Resources

About