Generating Javanese Stopwords List using  K-means Clustering Algorithm

Wibawa, Aji Prasetya; fithri, hidayah kariima; Zaeni, Ilham Ari Elbaith; Nafalski, Andrew

doi:10.17977/um018v3i22020p106-111

Cited by 12 publications

(12 citation statements)

References 15 publications

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“…Minimize the loss caused by reconstruction to obtain the optimal parameters *  and * '  as equation (4). The loss function used in this paper is the Kullback-Leibler divergence as equation (5).…”

Section: Basic Autoencodermentioning

confidence: 99%

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Automatic Knowledge Integration Method of English Translation Corpus Based on Kmeans Algorithm

Liang

Bayatti

2023

Applied Mathematics and Nonlinear Sciences

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We propose a feature extraction method based on the Kmeans algorithm based on the text characteristics in the English translation corpus. The article first uses a sparse autoencoder unsupervised learning method to reduce dimensionality. It then uses the Kmeans clustering algorithm for text clustering. The experimental results prove that the text features extracted by the sparse autoencoder based on the Kmeans algorithm can be used for English translation corpus knowledge clustering to achieve automatic integration. And this method can effectively solve the problems of high-dimensional, sparse, and noisy texts in the English translation corpus. The algorithm mentioned in the article can significantly improve the accuracy of the clustering results.

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Section: Basic Autoencodermentioning

confidence: 99%

“…This can avoid over-learning insignificant features in short texts. We adjust formula (5) to formula (7) and (8) to calculate.…”

Section: L1 Normal Form Regularizationmentioning

confidence: 99%

Automatic Knowledge Integration Method of English Translation Corpus Based on Kmeans Algorithm

Liang

Bayatti

2023

Applied Mathematics and Nonlinear Sciences

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“…Analisis hasil klasifikasi dilakukan dengan menggunakan pendekatan matriks konfusi [23]. Matriks Konfusi merupakan tabel yang digunakan untuk menunjukkan keefektifan hasil keluaran dari suatu algoritma.…”

Section: Analisis Hasilunclassified

Convolutional Neural Network (Cnn) Untuk Menentukan Gagrak Wayang Kulit

2022

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Indonesia adalah negara yang kaya akan keragaman budaya, salah satu dari budaya Indonesia adalah Wayang Kulit. Wayang kulit di Indonesia memiliki beragam gagrak, mulai dari Cirebon, Solo, Jawa-Timuran, Yogyakarta dan lain sebagainya. Keberagaman dari gagrak wayang kulit membuat generasi muda kesulitan untuk mengetahui gagrak dari wayang kulit. Dari permasalahan tersebut, peneliti akan mengembangkan sebuah machine learning dengan menggunakan Convolutional Neural Network (CNN) untuk menentukan gagrak wayang kulit. Tujuan penelitian ini adalah menghasilkan machine learning yang bisa menentukan gagrak dari wayang kulit. Data yang digunakan pada penelitian ini terdiri dari 280 gambar wayang kulit yang diunduh satu persatu di platform Facebook dan Google penelusuran gambar, warna latar belakang gambar tersebut diubah menjadi putih dan diubah ukuran menjadi 640*480 pixel. Data tersebut disebarkan melalui google form dan dilakukan validasi menggunakan Inter-annotator Agreement sehingga dapat digunakan pada proses pelatihan dengan metode CNN di Google Colab. Setelah itu dilakukan pengujian untuk menentukan gagrak wayang kulit menggunakan 3 arsitektur CNN yaang sudah dibuktikan pada penelitian-penelitian sebelumnya. Hasil dari penelitian ini menunjukan bahwa arsitektur yang digunakan oleh Sudiatmika & Dewi merupakan model klasifikasi terbaik dari ketiga arsitektur, arsitektur tersebut mendapatkan akurasi sebesar 92,27%, presisi sebesar 92,22%, recall sebesar 96,85% dan f-measure sebesar 91,93%.

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“…The fully connected layer process connects all the results of the neurons in the previous process to the next layer of neurons so that images can be classified. The confusion matrix approach was used to test the classification results [20]. The Confusion Matrix is a table that demonstrates how effective an algorithm's output results are.…”

Section: Scenariomentioning

confidence: 99%

Convolutional Neural Network (CNN) to determine the character of wayang kulit

Wibawa

Pratama

Handayani

et al. 2021

Int. J. Vis. Perform. Arts

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Indonesia is a country with diverse cultures. One of which is Wayang Kulit, which has been recognized by UNESCO. Wayang kulit has a variety of names and personalities, however most younger generations are not familiar with the characters of these shadow puppets. With today's rapid technological advancements, people could use this technology to detect objects using cameras. Convolutional Neural Network (CNN) is one method that can be used. CNN is a learning process that is included in the Deep Learning section and is used to find the best representation. The CNN is commonly used for object detection, would be used to classify good and bad characters. The data used consists of 100 black and white puppet images that were downloaded one at a time. The data was obtained through a training process that uses the CNN method and Google Colab to help speed up the training process. After that, a new model is created to test the puppet images. The result obtained a 92 percent accuracy rate, means that CNN can differentiate the Wayang Kulit character

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Generating Javanese Stopwords List using K-means Clustering Algorithm

Cited by 12 publications

References 15 publications

Automatic Knowledge Integration Method of English Translation Corpus Based on Kmeans Algorithm

Automatic Knowledge Integration Method of English Translation Corpus Based on Kmeans Algorithm

Convolutional Neural Network (Cnn) Untuk Menentukan Gagrak Wayang Kulit

Convolutional Neural Network (CNN) to determine the character of wayang kulit

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