2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)最新文献_第4页

Ensemble Learning Approach on Indonesian Fake News Classification 印尼假新闻分类的集成学习方法

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982409

H. Al-Ash, Mutia Fadhila Putri, P. Mursanto, A. Bustamam

引用次数: 18

Classification of Indonesian Music Using the Convolutional Neural Network Method 用卷积神经网络方法对印尼音乐进行分类

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982470

S. R. Juwita, S. Endah

{"title":"Classification of Indonesian Music Using the Convolutional Neural Network Method","authors":"S. R. Juwita, S. Endah","doi":"10.1109/ICICoS48119.2019.8982470","DOIUrl":"https://doi.org/10.1109/ICICoS48119.2019.8982470","url":null,"abstract":"Music has a variety of genres, namely pop, rock, jazz, and so on. Indonesia has its own music that other countries do not have, including campursari, dangdut, and keroncong music. The three types of music have musical instruments that are almost similar, which makes it difficult for listeners to distinguish the genre of music, especially the younger generation, so we need a tool called classification. This study uses a mel-spectogram and the Convolutional Neural Network (CNN) method to classify Indonesian music. The CNN parameters and architecture tested in this study were batch normalization, ReLU activation, dropout, activation of sigmoid and softmax output, epoch value, learning rate value, and dense layer value. The entire parameter is tested using input with two different data sharing methods, namely stratified split and k-fold cross validation. The highest accuracy of 82% was obtained by using the stratified split data distribution method and using batch normalization parameters, ReLU activation, activation of outputs sigmoid and softmax, 30 epoch values, 0.05 learning rate values, and 200 layer dense values. The model with the highest accuracy value is used as the basis for classifying Indonesian music into campursari, dangdut, or keroncong classes","PeriodicalId":105407,"journal":{"name":"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128973269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Snake Fruit Classification by Using Histogram of Oriented Gradient Feature and Extreme Learning Machine 基于定向梯度特征直方图和极限学习机的蛇果分类

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982528

Rismiyati, H. A. Wibawa

引用次数: 4

Twitter Buzzer Detection for Indonesian Presidential Election 印尼总统选举的推特蜂鸣器检测

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982529

Andi Suciati, A. Wibisono, P. Mursanto

引用次数: 12

Acquiring Domain Knowledge for Cardiotocography: A Deep Learning Approach 获取心脏造影领域知识:一种深度学习方法

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982397

Priyamvada Pushkar Huddar, S. Sontakke

引用次数: 2

Clustering of Districts in Indonesia using the 2015 High School Social Sciences National Examination Results 使用2015年高中社会科学国家考试结果对印度尼西亚地区进行聚类

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982524

R. Ferdhiana, K. Amri, T. Abidin

引用次数: 1

Multi-Layered Encryption Method 多层加密方法

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982407

Usman Sudibyo, Cinantya Paramita

引用次数: 0

Conceptual Model for Human Anatomy Learning Based Augmented Reality on Marker Puzzle 3D Printing 基于标记谜题3D打印增强现实的人体解剖学学习概念模型

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982471

W. Hidayat, A. E. Permanasari, P. Santosa, N. Arfian, L. Choridah

引用次数: 6

Deep Convolutional Adversarial Network-Based Feature Learning for Tea Clones Identifications 基于深度卷积对抗网络的茶叶克隆识别特征学习

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982416

Endang Suryawati, Vicky Zilvan, R. S. Yuwana, A. Heryana, D. Rohdiana, H. Pardede

引用次数: 4

Feature Extraction using Self-Supervised Convolutional Autoencoder for Content based Image Retrieval 基于内容的图像检索中基于自监督卷积自编码器的特征提取

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982468

I. Siradjuddin, Wrida Adi Wardana, M. K. Sophan

{"title":"Feature Extraction using Self-Supervised Convolutional Autoencoder for Content based Image Retrieval","authors":"I. Siradjuddin, Wrida Adi Wardana, M. K. Sophan","doi":"10.1109/ICICoS48119.2019.8982468","DOIUrl":"https://doi.org/10.1109/ICICoS48119.2019.8982468","url":null,"abstract":"This paper presents Autoencoder using Convolutional Neural Network for feature extraction in the Content-based Image Retrieval. Two type of layers are in the convolutional autoencoder architecture, they are encoder and decoder layer. The encoder layer extracts the important representation of the image using feature learning capability of the convolutional neural network, and reduces the dimension of the image. The decode layer reconstructs the representation, such that, the output of the autoencoder is close to the input data. The important representation of the image from the encoder layer in convolutional autoencoder, is used as the extracted features in the content-based image retrieval. Similarity distance between the extracted feature of the query image and the database is calculated to retrieve relevant images. The images in Corel dataset are used for the experiment and tested using the proposed model. The experiments show that the extracted features are representable for the images, and can be used to retrieve relevant images in the content-based image retrieval.","PeriodicalId":105407,"journal":{"name":"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124765059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17