Perbandingan Jumlah Epoch Dan Steps Per Epoch Pada Convolutional Neural Network Untuk Meningkatkan Akurasi Dalam Klasifikasi Gambar

Yuliani, Enie; Aini, Afifah Nur; Khasanah, Chan Uswatun

doi:10.46808/informa.v5i3.140

Cited by 9 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[14] The higher of epoch value and quantity of datasets, the higher the accuracy value. The more quantity of epochs, and the step each epoch used would make a higher value of training and validation accuracy [15].…”

Section: Resultsmentioning

confidence: 99%

Identification of adulterated rice bran with rice husks by using the image analytics

Albarki,

Agustin,

Permana

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Rice bran is often adulterated with milled rice husks which can lead to losses and decreased production of livestock. Testing of rice bran adulteration is still carried out manually which is quite subjective, and thus it is necessary to test using the image analysis method based on convolutional neural network (CNN) through a visual image analysis process. The purpose of this study was to evaluate the accuracy of training, validation, and test data on rice bran adulteration with rich husks using the CNN-based image analysis. This study employed staining treatment using phloroglucinol. The stages of this research consisted of the process of mixing forgery, colouring treatment, taking pictures, sharing data, and building the CNN model. This study found that the results of the accuracy of training data, validation data, and test data with phloroglucinol staining treatment had accuracy that was still far from 100% accuracy. This result is due to the epoch value, quantity of datasets, and the different viewing angles when capturing an image.

show abstract

Section: Resultsmentioning

confidence: 99%

Identification of adulterated rice bran with rice husks by using the image analytics

Albarki,

Agustin,

Permana

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…Single-layer perceptron itself is a feedforward type, which is a type of NN where neurons in one layer can only connect with neurons in different layers. Therefore, in this case, the Single Layer perceptron network method includes supervised learning because the learning method is carried out by studying examples of known input and output [22].…”

Section: Imentioning

confidence: 99%

Sentiment Analysis on Hotel Ratings Using Dynamic Convolution Neural Network

Sinaga¹,

Gunawan²,

Wanayumini³

2023

ICoSTEC

View full text Add to dashboard Cite

Currently, the role of information technology is very important in everyday life because heavy workloads can become easier, communication time can be made shorter and data processing can be faster and more accurate. Hotel ranking sentiment analysis can provide important information for hotel owners and managers to improve the quality of service and guest experience. It can also be used by prospective guests to make the right booking decisions. Sentiment analysis can identify positive or negative feelings from guest reviews. There are 694,213 data reviews about hotels using English which are used as training data. The data was preprocessed and 76,905 vocabularies were obtained by utilizing Word2Vec. The training data was carried out at the encoding stage. The DCNN model is given a K-Max-Polling value of 2. The model is trained for 20 epochs. The model that has been formed is tested with 173,554 data and obtained an accuracy rate of 95%.

show abstract

“…Data yang telah dibagi menjadi data pelatihan dan data uji akan diolah dengan menggunakan epoch. Epoch merupakan satu kali proses pelatihan model dimana seluruh paket pelatihan yang digunakan untuk mengajar model akan diulang sebanyak periode yang telah ditentukan, dalam penelitian ini adalah lima puluh epoch [11]. Selama proses fitting (pelatihan), model akan mengulangi dataset pelatihan sebanyak lima puluh kali, dan pada setiap epoch, parameter model akan diperbarui berdasarkan gradien dari batch sampel yang dievaluasi.…”

Section: Hasil Dan Pembahasanunclassified

Penerapan Convolutional Neural Network Pada Klasifikasi Tanaman Menggunakan ResNet50

Kulsum,

Cherid

2023

simkom

View full text Add to dashboard Cite

Penelitian ini bertujuan untuk mengklasifikasikan tanaman apel menjadi dua kategori, yaitu sehat dan busuk, serta menciptakan perangkat lunak berbasis Desktop yang dapat mengklasifikasikan jenis tanaman berdasarkan citra digital menggunakan Convolutional Neural Network (CNN) dengan model ResNet50. Data yang digunakan untuk melatih model terdiri dari 1545 gambar, sedangkan data uji yang digunakan berjumlah 661 gambar. Proses klasifikasi dilakukan untuk dua kelas, yaitu citra daun apel sehat dan citra daun apel busuk. Hasil evaluasi model menggunakan Confusion Matrix menunjukkan akurasi sebesar 91% setelah proses pelatihan dilakukan selama 50 epoch pada data latih. Selanjutnya, aplikasi perangkat lunak berbasis Desktop dibuat untuk menampilkan hasil jenis daun apel yang dipilih, dengan menampilkan informasi apakah daun tersebut termasuk dalam kategori sehat atau busuk.

show abstract

Perbandingan Jumlah Epoch Dan Steps Per Epoch Pada Convolutional Neural Network Untuk Meningkatkan Akurasi Dalam Klasifikasi Gambar

Cited by 9 publications

References 0 publications

Identification of adulterated rice bran with rice husks by using the image analytics

Identification of adulterated rice bran with rice husks by using the image analytics

Sentiment Analysis on Hotel Ratings Using Dynamic Convolution Neural Network

Penerapan Convolutional Neural Network Pada Klasifikasi Tanaman Menggunakan ResNet50

Contact Info

Product

Resources

About