Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications最新文献_第8页

Deep Learning for Robust Speech Command Recognition Using Convolutional Neural Networks (CNN) 基于卷积神经网络(CNN)的鲁棒语音命令识别深度学习

Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications Pub Date : 2022-11-22 DOI: 10.1145/3575882.3575902

Zahra Cantiabela, H. Pardede, Vicky Zilvan, W. Sulandari, R. S. Yuwana, A. A. Supianto, Dikdik Krisnandi

{"title":"Deep Learning for Robust Speech Command Recognition Using Convolutional Neural Networks (CNN)","authors":"Zahra Cantiabela, H. Pardede, Vicky Zilvan, W. Sulandari, R. S. Yuwana, A. A. Supianto, Dikdik Krisnandi","doi":"10.1145/3575882.3575902","DOIUrl":"https://doi.org/10.1145/3575882.3575902","url":null,"abstract":"The rapid development of mobile devices has made human-computer interaction through voice increasingly popular and effective. This condition is made possible by the rapid growth of Automatic Speech Recognition (ASR) technologies. ASR can convert human speech signals into text and interpret it as a command for computer systems to perform. One of the remaining challenges for ASR is that the system’s performance can degrade significantly in noisy environments. This research aims to build a speech recognition system capable of recognizing human speech in clean and noisy conditions, enabling the system to recognize speech commands even in noisy conditions. Deep Neural Network (DNN) methods are the dominant methods for ASR. But, there is increasing interest in using a convolutional neural network (CNN) instead. In this study, we develop CNN-based architectures for robust ASR for speech commands. We explore various depths of CNN’s layers of CNN architecture to improve a robust speech recognition system. We also optimized the best model using early stopping and two types of optimizers, i.e., Adam and SGD (Stochastic gradient descent). Our experiment shows that CNN exhibited an accuracy of 90.64%, while the DNN model exhibited 86.74% accuracy in clean conditions. In noisy conditions, an increasing number of CNN layers improves ASR’s robustness. The CNN method achieves 77.38% accuracy in clean conditions and 87.59% in noisy conditions.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132898832","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: 0

An Indonesian Adaption of Visual Aesthetics of Website Inventory (VisAWI) Questionnaire for Evaluating Video Game User Interface 印尼版网站目录(VisAWI)的视觉美学调查问卷，用于评估电子游戏用户界面

Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications Pub Date : 2022-11-22 DOI: 10.1145/3575882.3575956

Lia Sadita, H. Santoso, Luqman Iffan Windrawan, P. Khotimah

{"title":"An Indonesian Adaption of Visual Aesthetics of Website Inventory (VisAWI) Questionnaire for Evaluating Video Game User Interface","authors":"Lia Sadita, H. Santoso, Luqman Iffan Windrawan, P. Khotimah","doi":"10.1145/3575882.3575956","DOIUrl":"https://doi.org/10.1145/3575882.3575956","url":null,"abstract":"Pixel art is one type of stylized style of the game’s visual appearances. Despite its gaining popularity, only a few studies have worked on analyzing the visual aesthetics of pixel art. While existing questionnaires were frequently used to measure user perceptions toward media products, unfortunately, they do not specifically design to measure visual aesthetics. Therefore, the study adapts the Visual Aesthetics of Website Inventory (VisAWI) designed by Moshagen and Tielsch (2010) to measure how users perceived the graphical interface aesthetics. We perform language adaptation with a cross-cultural adaptation approach to fit the target respondents, i.e., Indonesian, and the context of video games. The adaptation covers the translation process as well as language and culture adjustment for both languages. It consists of forward translation, synthesis, back translation, expert committee review, and pre-testing. Based on the validity and reliability tests, the results show that it is valid and reliable to use.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133588957","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: 0

Analysis of H-Component of Geomagnetic Variation in Indonesian Region 印度尼西亚地区地磁变化h分量分析

Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications Pub Date : 2022-11-22 DOI: 10.1145/3575882.3575948

Nia Syafitri, Angga Yolanda Putra, Erlansyah, Muzirwan, Hadi Rasidi, Singgih Anggi Purnama, Helmi Suryaputra, La Ode Muhammad Musafar Kilowasid, Kuncoro Wisnu, Iskandar Bakri, Lambang Nurdiansah, F. Nuraeni, Cahyo Purnomo, S. Rasimeng

{"title":"Analysis of H-Component of Geomagnetic Variation in Indonesian Region","authors":"Nia Syafitri, Angga Yolanda Putra, Erlansyah, Muzirwan, Hadi Rasidi, Singgih Anggi Purnama, Helmi Suryaputra, La Ode Muhammad Musafar Kilowasid, Kuncoro Wisnu, Iskandar Bakri, Lambang Nurdiansah, F. Nuraeni, Cahyo Purnomo, S. Rasimeng","doi":"10.1145/3575882.3575948","DOIUrl":"https://doi.org/10.1145/3575882.3575948","url":null,"abstract":"This paper analyzed geomagnetic variation of H-component from several geomagnetic stations in Indonesia, that are Kototabang (KTB), Pontianak (PTN), Tanjungsari (TJS), Pare-pare (PRP), Manado (MND), Kupang (KPG), and Pamengpeuk (PMK) during 2014. The study aims to analyze and obtain data set that are suitable for PTN geomagnetic data so that they can fill in the data gaps of PTN. We performed data analysis using standard deviation for PTN and other stations. We found that KPG, MND, PMK, and PRP have smaller deviation standard about 10 nT at geomagnetically quiet conditions. Meanwhile, in disturbed conditions, the maximum standard deviation is 17 nT. Substitution of data for geomagnetic quiet conditions should be done with caution and requires further analysis. The substitution of data for disturbed conditions can be done safely because the maximum standard deviation is smaller than the range of disturbance conversion into K index.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116362915","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: 0

Deep Learning and Machine Learning Model Comparison for Diagnosis Detection from Medical Records 基于病历诊断检测的深度学习和机器学习模型比较

Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications Pub Date : 2022-11-22 DOI: 10.1145/3575882.3575941

Lukman Heryawan, Fitra Febriansyah, Arif Bukhori

{"title":"Deep Learning and Machine Learning Model Comparison for Diagnosis Detection from Medical Records","authors":"Lukman Heryawan, Fitra Febriansyah, Arif Bukhori","doi":"10.1145/3575882.3575941","DOIUrl":"https://doi.org/10.1145/3575882.3575941","url":null,"abstract":"Structured data is needed in hospitals as a means of exchanging information between doctors, nurses, pharmacy department, coder/medical record section, and administration section. Structured data improves interoperability and uniformity of interpretation between entities working in the hospital. One of the stages of the process to generate structured data such as ICD codes is to detect diagnoses from medical records written by doctors. The entity in charge of interpreting medical records and determining the relevant ICD code according to the doctor's diagnosis written in the medical record is called a coder. In determining the ICD code, the coder looks for the patient's diagnosis in the medical record. However, coders with minimal experience may find it challenging to find a patient's diagnosis. This will cause inaccuracy in determining the ICD code to diagnose the patient's disease. This research constructed a predictor in the diagnostic recommendation system. We developed a supervised deep learning model, which is an LSTM model, and a Stochastic Gradient Descent model as a baseline in this study. Compared to the Stochastic Gradient Descent model, it was discovered that the proposed LSTM model produced the best results, reaching up to 98% accuracy in 14 epochs.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122744736","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: 1

Developing Semantic Annotation Representation of Social Media Sentiments and Metadata as Resource Description Framework: A Study of Indonesian New Capital Related Tweets Written in Bahasa 开发社交媒体情感的语义标注表示和元数据作为资源描述框架——基于印尼语新首都相关推文的研究

Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications Pub Date : 2022-11-22 DOI: 10.1145/3575882.3575926

Josua Geovani Pinem, Agung Septiadi, Siti Shaleha, Muhammad Reza Alfin, Aulia Haritsuddin Karisma Muhammad Subekti, Jemie Muliadi, G. Wibowanto, Agung Santosa, M. T. Uliniansyah, Asril Jarin, Andi Djalal Latief, Gunarso, Hammam Riza

{"title":"Developing Semantic Annotation Representation of Social Media Sentiments and Metadata as Resource Description Framework: A Study of Indonesian New Capital Related Tweets Written in Bahasa","authors":"Josua Geovani Pinem, Agung Septiadi, Siti Shaleha, Muhammad Reza Alfin, Aulia Haritsuddin Karisma Muhammad Subekti, Jemie Muliadi, G. Wibowanto, Agung Santosa, M. T. Uliniansyah, Asril Jarin, Andi Djalal Latief, Gunarso, Hammam Riza","doi":"10.1145/3575882.3575926","DOIUrl":"https://doi.org/10.1145/3575882.3575926","url":null,"abstract":"Social Media has become a tool abiding the press in this modern society. Everyone can write their minds and build their mass media to publish opinions. Thus, in this manuscript, we develop a resource description framework scheme (RDFS) to enrich the information and metadata from Indonesian tweets regarding their New Capitol. This work focused on applying a popular method (i.e., the Tweetskb scheme) to construct the RDF of those tweets. We also developed the Schema to fulfill our need to contain all the information to RDF. RDF Triples were generated by connecting several established vocabularies to ensure the connection between its related nodes has meaning. The sentiment polarity (i.e., neutral, positive, and negative sentiment) is used in this manuscript. Thus, our proposal can be used as an initial work to make use of twitter's metadata to predict how reliable a user is, how the community interact with a certain topic, spam detection, clustering, and even implementing machine learning and deep learning sentiment analysis in a manner of knowledge graph.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128192161","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: 0

Android-based Forest Fire Danger Rating Information System for Early Prevention of Forest / Land fires 基于android的森林火险等级信息系统，用于森林/土地火灾的早期预防

Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications Pub Date : 2022-11-22 DOI: 10.1145/3575882.3575951

Anwar Annas, Silvan Permana, Taufik Hidayat, Aby Al Khudri

{"title":"Android-based Forest Fire Danger Rating Information System for Early Prevention of Forest / Land fires","authors":"Anwar Annas, Silvan Permana, Taufik Hidayat, Aby Al Khudri","doi":"10.1145/3575882.3575951","DOIUrl":"https://doi.org/10.1145/3575882.3575951","url":null,"abstract":"The significant losses and negative impacts due to forest and land fires cause the need for an effort to prevent forest and land fires from an early stage. Therefore doing prevention before a disaster occurs has a low cost and smaller losses compared to efforts to manage disasters that have already occurred. The Remote Sensing Applications Center-LAPAN has produced fire hazard rating information. Information on fire hazard ratings which are up to date, fast and precise can be used to monitor forestry resources. The development of an accountable monitoring system can be a reference in efforts to control forest and land fires. At the same time participate in maintaining the sustainability of Indonesia’s natural resources sustainably. The development of an Android-based information dissemination system was created to facilitate the dissemination of information on fire hazard ratings to the public. This android-based information system provides information about the hazard rating of forest/land fires and analyzes the potential for forest/land fires and their spread through smartphones. It can be used to support decisionmaking to make strategic steps in preventing forest/land fires in Indonesia more widely.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115011933","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: 0

Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications 2022计算机、控制、信息学及其应用国际会议论文集

Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications Pub Date : 1900-01-01 DOI: 10.1145/3575882

引用次数: 0