Model Fast Tansfer Learning pada Jaringan Syaraf Tiruan Konvolusional untuk Klasifikasi Gender Berdasarkan Citra Wajah

Triwijoyo, Bambang Krismono

doi:10.30812/matrik.v18i2.376

Cited by 6 publications

(7 citation statements)

References 23 publications

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“…The output feature maps of one layer are used as the input for the following layer, producing a feature extraction hierarchy. This procedure enables the network to learn complicated features and accurately categorize images [9].…”

Section: Cnnmentioning

confidence: 99%

“…SVM is an algorithm that can be used to classify images both unsupervised and supervised [6][7][8]. Supervised learning is a type of machine learning that will be created because the category of types of waste has been determined in the rules formed in machine learning so that the machine no longer needs to cluster data into categories that the machine determines [9]. Related work to this research is research conducted by Leonardo [10].…”

Section: Introductionmentioning

confidence: 99%

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Image Processing Using Morphology on Support Vector Machine Classification Model for Waste Image

Fahmi,

Yudhana,

Sunardi

2023

matrik

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Sorting waste has always been an important part of managing waste. The primary issue with the waste sorting process has been the discomfort caused by prolonged contact with waste odor. A machinelearning method for identifying waste types was created to address this issue. The study’s goal was to create machine learning to solve waste management challenges by applying the most accurate categorization model available. The research approach was the quantitative analysis of the classification model accuracy. The Kaggle dataset was used to collect and curate data, which was subsequently preprocessed using the morphology approach. Based on picture sources, the data was trained and used to classify waste. The Support Vector Machine model was used in this investigation and feature extraction via the Convolutional Neural Network. The results showed that the system categorized waste successfully, with an accuracy of 99.30% and a loss of 2.47% across all categories. According to the findings of this study, SVM combined with morphological image processing functioned as a strong classification model, with a remarkable accuracy rate of 99.30%. This study’s outcomes contributed to waste management by giving an efficient and dependable waste classification solution compared to many previous studies.

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Section: Cnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image Processing Using Morphology on Support Vector Machine Classification Model for Waste Image

Fahmi,

Yudhana,

Sunardi

2023

matrik

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“…CNN memiliki dua metode; yakni klasifikasi menggunakan feed forward dan tahap pembelajaran menggunakan back propagation. Cara kerja CNN memiliki kesamaan pada MLP, namun dalam CNN setiap neuron ditampilkan dalam bentuk dua dimensi (Triwijoyo, 2019).…”

Section: Pendahuluanunclassified

“…MLP menggunakan teknik pembelajaran yang diawasi yang disebut propagative mundur untuk pelatihan. Beberapa lapisan dan aktivasi non-liniernya membedakan MLP dari perceptron linier itu dapat membedakan data yang tidak dapat dipisahkan secara linier (Triwijoyo, 2019).…”

Section: Convolutional Neural Networkunclassified

Pengklasifikasian Genre Musik Indonesia Menggunakan Convolutional Neural Network

Wairata

Swedia

Cahyanti

2021

Sebatik

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Pada zaman sekarang sudah terdapat banyak sekali teknologi Artificial Intelligence. Artificial Intelligence sendiri memiliki beberapa sub bab, salah satunya adalah Machine Learning dan Deep Learning merupakan salah sub bab dari Machine Learning itu sendiri. Convolutional Neural network (CNN) adalah salah satu jenis neural network yang biasa digunakan pada data gambar. Pada penelitian ini akan digunakan untuk mengklasifikasikan genre musik dengan cara mengonversi data pada lagu menjadi sebuah gambar yang kita sebut spektogram. Pada penelitian akan mengimplementasikan CNN dalam mengategorikan 3 genre musik di Indonesia yakni; dangdut, Jazz dan Pop. Pada penelitian ini terdapat 100 lagu untuk masing-masing genre sebagai data setnya. Tujuan dari penelitian ini sendiri adalah mengoptimalkan tingkat akurasi dalam pengategorian genre musik menggunakan model CNN. Berdasarkan penelitian yang sudah dilakukan didapatkan bahwa penggunaan 35 epoch memiliki tingkat akurasi yang optimal yakni; tingkat akurasi tes sebesar 81,33% dan tingkat akurasi validasi sebesar 100%. Implementasi ini diharapkan dapat menentukan kategori dalam musik.

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“…In our previous study, we proposed a model for recognizing gender from facial images [15]. In this study, a different CNN model architecture with batch normalization is proposed, namely three layers of double convolutional layers with a simpler architectural model for the recognition of emotional expression based on human face images in the FER2013 dataset from Kaggle.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network With Batch Normalization for Classification of Emotional Expressions Based on Facial Images

Triwijoyo¹,

Adil

Anggrawan

2021

matrik

Self Cite

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Emotion recognition through facial images is one of the most challenging topics in human psychological interactions with machines. Along with advances in robotics, computer graphics, and computer vision, research on facial expression recognition is an important part of intelligent systems technology for interactive human interfaces where each person may have different emotional expressions, making it difficult to classify facial expressions and requires training data. large, so the deep learning approach is an alternative solution., The purpose of this study is to propose a different Convolutional Neural Network (CNN) model architecture with batch normalization consisting of three layers of multiple convolution layers with a simpler architectural model for the recognition of emotional expressions based on human facial images in the FER2013 dataset from Kaggle. The experimental results show that the training accuracy level reaches 98%, but there is still overfitting where the validation accuracy level is still 62%. The proposed model has better performance than the model without using batch normalization.

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Model Fast Tansfer Learning pada Jaringan Syaraf Tiruan Konvolusional untuk Klasifikasi Gender Berdasarkan Citra Wajah

Cited by 6 publications

References 23 publications

Image Processing Using Morphology on Support Vector Machine Classification Model for Waste Image

Image Processing Using Morphology on Support Vector Machine Classification Model for Waste Image

Pengklasifikasian Genre Musik Indonesia Menggunakan Convolutional Neural Network

Convolutional Neural Network With Batch Normalization for Classification of Emotional Expressions Based on Facial Images

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