2021 International Seminar on Intelligent Technology and Its Applications (ISITIA)最新文献_第2页

Analysis The Opinion of School-from-Home during The COVID-19 Pandemic using LSTM Approach 基于LSTM方法的新冠肺炎大流行期间“在家上学”意见分析

2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) Pub Date : 2021-07-21 DOI: 10.1109/ISITIA52817.2021.9502206

F. A. Muqtadiroh, D. Purwitasari, E. M. Yuniarno, S. M. S. Nugroho, M. Purnomo

{"title":"Analysis The Opinion of School-from-Home during The COVID-19 Pandemic using LSTM Approach","authors":"F. A. Muqtadiroh, D. Purwitasari, E. M. Yuniarno, S. M. S. Nugroho, M. Purnomo","doi":"10.1109/ISITIA52817.2021.9502206","DOIUrl":"https://doi.org/10.1109/ISITIA52817.2021.9502206","url":null,"abstract":"The purpose of opinion analysis in this research is to perceive public responses concerning School-From-Home (SFH) policy during the pandemic in attempt to curb virus spread and worry about new cluster emergences. The policy entails diverse reactions from the societies, including the citizens in virtual world through their chirps in social media, such as Twitter. Analysis on the social media has proved that it has remarkable potentials to apprehend public opinions on various issues. The opinion analysis was performed to get insights about public perception towards SFH policy. As initially predicted, the result of our analysis would show that the public perceptions towards SFH would be mainly negative. The researcher adopted LSTM model as a deep learning approach. Moreover, implementing the N-Gram extraction technique was able to improve the model’s performance. Model performance accuracy reached 83.30%. It is concluded that the increasing of model accuracy is about 0.018%. While the running time efficiency of LSTM has improved 19.4%. The results of the analysis of SFH’s opinion were 77.90% negative and 22.10% positive.","PeriodicalId":161240,"journal":{"name":"2021 International Seminar on Intelligent Technology and Its Applications (ISITIA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131016276","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

Internet Based Remote Laboratory Architecture for 3-Phase Induction Motor Control System Experiment 基于Internet的三相异步电机控制系统实验远程实验室体系结构

2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) Pub Date : 2021-07-21 DOI: 10.1109/ISITIA52817.2021.9502222

A. Kurniawan, Ahmad Rofif Hakiki, Kevin Natio Banjarnahor, Mohamad Abdul Hady, A. Santoso, Ali Fatoni

{"title":"Internet Based Remote Laboratory Architecture for 3-Phase Induction Motor Control System Experiment","authors":"A. Kurniawan, Ahmad Rofif Hakiki, Kevin Natio Banjarnahor, Mohamad Abdul Hady, A. Santoso, Ali Fatoni","doi":"10.1109/ISITIA52817.2021.9502222","DOIUrl":"https://doi.org/10.1109/ISITIA52817.2021.9502222","url":null,"abstract":"To impede the spread of the COVID-19 virus, governments around the world enacted several rules such as social distancing, local lockdown, and travel restriction in accordance with WHO suggestions. These rules have caused many things such as limitation of social interaction and increased limit of public places, including campuses, schools, and other educational institution. This condition leads to the difficulties of educational activities since most classes are either canceled or moved to an online platform. Instead of classes, most of the workshops, conferences, and other activities which are important to students have been canceled or postponed. To alleviate the pandemic impact in education especially for college students this work proposed a system for conduction remote practicum safely and effectively to support the education and experience of these students. Research or practicum is important to preserve student's hands-on experience so that the student's readiness to handle the post-campus job is adequate. In this research, the proposed system architecture enables internet-based remote access of the laboratory equipment via an API with the MQTT communication protocol. The experiment is tested under a three-phase induction motor. The results show that the proposed system has the potential ability to be implemented further in the practicum activity. The average transmission time of 0,388 seconds without the Live Stream feature and 0,679 seconds with the Live Stream feature. Also, the system reliability of the proposed system could reach 98,76 percent.","PeriodicalId":161240,"journal":{"name":"2021 International Seminar on Intelligent Technology and Its Applications (ISITIA)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122931529","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

Violence Classification Using Support Vector Machine and Deep Transfer Learning Feature Extraction 基于支持向量机和深度迁移学习特征提取的暴力分类

2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) Pub Date : 2021-07-21 DOI: 10.1109/ISITIA52817.2021.9502253

Karisma, E. Imah, A. Wintarti

{"title":"Violence Classification Using Support Vector Machine and Deep Transfer Learning Feature Extraction","authors":"Karisma, E. Imah, A. Wintarti","doi":"10.1109/ISITIA52817.2021.9502253","DOIUrl":"https://doi.org/10.1109/ISITIA52817.2021.9502253","url":null,"abstract":"Violence detection research is still quite a challenge for researchers and a considerable amount of effort. Before the video can be processed for classification, feature extraction is an important process to obtain important information. Determination of feature extraction and classification algorithms is an important factor for accurate classification results. This study uses deep transfer learning for feature extraction and combining it with the Support Vector Machine (SVM) classifier. The deep transfer learning algorithm in this study is a pre-trained model of Visual Geometry Group Network-16 (VGGNet-16). The video data was extracted using VGGNet-16 and then classified using SVM. Tests were carried out with 5-fold cross-validation with a variety of linear kernel, RBF, and Polynomial functions. The results were also compared with the Principal Component Analysis (PCA) feature extraction algorithm combining with SVM also. The results showed that the combination of deep transfer learning with SVM linear kernel functions resulted in higher accuracy compared to RBF and Polynomial kernel functions, and also compared to PCA combined with SVM.","PeriodicalId":161240,"journal":{"name":"2021 International Seminar on Intelligent Technology and Its Applications (ISITIA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131261760","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

Dataset transformation using hybrid method of polar-based cartesian and image filtering technique for annual rainfall clustering 基于极坐标和图像滤波技术的数据集变换用于年降水聚类

2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) Pub Date : 2021-07-21 DOI: 10.1109/ISITIA52817.2021.9502202

B. Suprapty, Anggri Sartika Wiguna, Agusma Wajiansyah, R. Malani

{"title":"Dataset transformation using hybrid method of polar-based cartesian and image filtering technique for annual rainfall clustering","authors":"B. Suprapty, Anggri Sartika Wiguna, Agusma Wajiansyah, R. Malani","doi":"10.1109/ISITIA52817.2021.9502202","DOIUrl":"https://doi.org/10.1109/ISITIA52817.2021.9502202","url":null,"abstract":"Clustering is categorized as unsupervised learning because there is no class label information available in grouping a dataset. For this reason, an assessment of the quality of the clustering results is critical. In general, two essential clustering parameters are the similarities between cluster members in a cluster and the cluster centers’ separation. Various approaches can be used to improve a clustering algorithm’s performance: raw data pre-processing, cluster center initialization techniques, objective function assignment, modification of specific steps, and others. The subject of this study is the pattern of rainfall every month during the year of the observation period obtained from the clustering process. This study aims to improve the performance of K-Mean Clustering through manipulation of raw data pre-processing into certain datasets. Image filtering technique is used to generate a dataset based on the relationship between neighboring rainfall values. Polar-based Cartesian data space transformation is used to generate a dataset based on a range of rainfall values for each month during the year of the observation period. Four scenarios have been used to test the performance of the proposed method. The study results show that the proposed method produces the highest performance ratio (54.79%) of all scenarios’ total average GOS (Global Optimum Solution). Meanwhile, increasing GOS to the original method also resulted in the highest increase in GOS ratio (63.68%) compared to other methods. Further studies will focus on the application of the proposed methods for improving the performance of SOM and Fuzzy C-Mean Clustering.","PeriodicalId":161240,"journal":{"name":"2021 International Seminar on Intelligent Technology and Its Applications (ISITIA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133725893","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

Design of EV Hardware-in-the-Loop Simulator of Battery and Supercapacitor Hybrid Storage System 电动汽车电池与超级电容器混合存储系统硬件在环模拟器设计

2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) Pub Date : 2021-07-21 DOI: 10.1109/ISITIA52817.2021.9502254

L. Rachim, V. Lystianingrum, D. Riawan, I. Gunanda

{"title":"Design of EV Hardware-in-the-Loop Simulator of Battery and Supercapacitor Hybrid Storage System","authors":"L. Rachim, V. Lystianingrum, D. Riawan, I. Gunanda","doi":"10.1109/ISITIA52817.2021.9502254","DOIUrl":"https://doi.org/10.1109/ISITIA52817.2021.9502254","url":null,"abstract":"This paper presents a design of electric vehicle (EV) hardware-in-the-loop (HIL) simulator of battery and supercapacitor hybrid storage system. The EV HIL simulator in this paper uses 2 DC machines and load, where 1 DC machine functions as a motor and 1 DC machine functions as a generator, while the load is used as a load on the generator. The power sharing method used in this paper is filter based control (FBC). The reference or set point used is power, so the error between the reference power and the actual power is controlled by the proportional controller. In this paper also, to equalize the output voltage of each converter with different input voltages, the PWM signal on the supercapacitor converter is multiplied by the gain. There are 2 scenarios presented to see the performance of the designs that have been made, namely no regenerative braking programmed and with regenerative braking programmed. There are 3 conditions seen in the results of the simulator implementation, namely when starting, deceleration and acceleration to see the performance of the power sharing method used. The results show that the simulator that has been designed with the power sharing method used has a very good performance.","PeriodicalId":161240,"journal":{"name":"2021 International Seminar on Intelligent Technology and Its Applications (ISITIA)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133956490","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

A New Approach for Hot Spot Solar Cell Detection based on Multi-level Otsu Algorithm 基于多级Otsu算法的太阳能电池热点检测新方法

2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) Pub Date : 2021-07-21 DOI: 10.1109/ISITIA52817.2021.9502239

A. N. N. Afifah, Indrabayu, A. Suyuti, Syafaruddin

引用次数: 3

Development of a Low-Cost System for Liquid Clustering Using a Spectrophotometry Technique 利用分光光度技术开发低成本的液体聚类系统

2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) Pub Date : 2021-07-21 DOI: 10.1109/ISITIA52817.2021.9502194

Andrés Ariza, T. Mujiono, T. A. Sardjono

引用次数: 0

Effect of CT-Scan Image Resizing, Enhancement and Normalization on Accuracy of Covid-19 Detection ct扫描图像调整、增强和归一化对Covid-19检测准确性的影响

2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) Pub Date : 2021-07-21 DOI: 10.1109/ISITIA52817.2021.9502217

N. G. Pratiwi, Yumna Nabila, Rian Fiqraini, A. W. Setiawan

{"title":"Effect of CT-Scan Image Resizing, Enhancement and Normalization on Accuracy of Covid-19 Detection","authors":"N. G. Pratiwi, Yumna Nabila, Rian Fiqraini, A. W. Setiawan","doi":"10.1109/ISITIA52817.2021.9502217","DOIUrl":"https://doi.org/10.1109/ISITIA52817.2021.9502217","url":null,"abstract":"Covid-19 continues to be a global health problem with an impact on at least 70 million people exposed and more than 1.5 thousand people died in December 2020. Detection by RT-PCR as the gold standard of WHO is still difficult to reach in some areas and has a low sensitivity issue. Many studies have focused on the detection of Covid-19 using computer vision, especially deep learning methods. However, it is necessary to evaluate the preprocessing stage before carrying out the classification to increase the accuracy of its detection. Therefore, the objective of this study was to compare the choice of the CT-Scan image pre-processing method and its effect on the results of covid-19 classification accuracy. The benefit of this study is that it can be used as a recommendation when considering the choice of a CT-Scan image preprocessing method to improve the accuracy of Covid-19 detection through more comprehensive deep learning. This study uses a CT scan image because it is considered to be of better quality than an X-ray image, although the price is relatively more expensive. The various methods used were resizing (deformed and non-deformed), enhancement(HE, CLAHE, EFF), and normalization ranges ([–1 1] and [0 1]). Meanwhile, the deep learning method used is the VGG-16 classifier. The results showed that there was an influence generated by the variations in the preprocessing methods on the precision of the Covid-19 classification. The highest accuracy results were obtained with 88.54% using the deformation ranges of size change, CLAHE improvement and normalization [0 1] and [–1 1]. This result quite competitive compared to other studies.","PeriodicalId":161240,"journal":{"name":"2021 International Seminar on Intelligent Technology and Its Applications (ISITIA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130112971","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: 3

ICT and Consumer Behavior: A Study of Students’ Self-Perceived Digital 信息通信技术与消费者行为:学生自我感知数字的研究

2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) Pub Date : 2021-07-21 DOI: 10.1109/ISITIA52817.2021.9502265

N. Othman, D. Alamsyah, Indriana, Mira Rustine, R. Aryanto, I. Setyawati

引用次数: 4

Traffic Density Classification Using Multilayer Perceptron and Random Forest Method 基于多层感知机和随机森林方法的交通密度分类

2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) Pub Date : 2021-07-21 DOI: 10.1109/ISITIA52817.2021.9502269

N. Maulida, K. Mutijarsa

{"title":"Traffic Density Classification Using Multilayer Perceptron and Random Forest Method","authors":"N. Maulida, K. Mutijarsa","doi":"10.1109/ISITIA52817.2021.9502269","DOIUrl":"https://doi.org/10.1109/ISITIA52817.2021.9502269","url":null,"abstract":"Traffic management is done to overcome congestion due to overcrowding and overcapacity. However, this arrangement still utilizes information obtained from various entities on the road, namely the police and transportation service officers. Observation of conditions and situations on the road is still subjective, so traffic management becomes subjective. However, there are potential technologies that can be utilized to help the existing problems. With these problems and opportunities, there is in providing traffic density information that is more objective utilizing the latest technology. The development of various types of information system adaptation and the use of technology is able to provide information on a regular basis. Machine learning as a form of technology development that is being optimized, can solve the information needs typical of traffic control. In this study, a traffic density classification model was made using an algorithm based on Artificial Neural Network-Multilayer Perceptron and Random Forest. The application of this research is carried out in five stages, namely understanding business needs, understanding data, cleaning and preparing data, optimizing parameters and modeling, and evaluating. By, using the method, Artificial Neural Network gives the optimum result and can help traffic management system.","PeriodicalId":161240,"journal":{"name":"2021 International Seminar on Intelligent Technology and Its Applications (ISITIA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130719486","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