2021 9th International Conference on Information and Communication Technology (ICoICT)最新文献

Disaster Tweet Classification Based On Geospatial Data Using the BERT-MLP Method 基于地理空间数据的BERT-MLP方法灾害推文分类

2021 9th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2021-08-03 DOI: 10.1109/ICoICT52021.2021.9527513

Iqbal Maulana, W. Maharani

{"title":"Disaster Tweet Classification Based On Geospatial Data Using the BERT-MLP Method","authors":"Iqbal Maulana, W. Maharani","doi":"10.1109/ICoICT52021.2021.9527513","DOIUrl":"https://doi.org/10.1109/ICoICT52021.2021.9527513","url":null,"abstract":"as a popular social media in the world and even in Indonesia, Twitter has a variety of popular topics making these topics trending, including the topic of natural disasters that have occurred in Indonesia. The DKI Jakarta flood disaster in early 2020 made a big scene on trending twitter topics. This study aims to classify these tweets into \"flooded\" and \"not flooded\" predictions with the tweets and geospatial features. The model proposed for classifying is BERT-MLP. Bidirectional Encoder from Transformers (BERT) is used in the pre-trained model to classify these tweets and Multi Layer Perceptron (MLP) is used to classify geospatial features. The scenario designed for the model focuses on the preprocessing of tweets as follows without stopword removal, without stemming, with both, and without both. Once classified, the tweet will be visualized into a two-dimensional interactive map. The best scenario results have an accuracy of 82% in scenarios without stemming and with stopword removal. This is due to the stemming process eliminates some of the features in tweets around 6%. This study also shows the relationship between the influence of negative context tweets on the \"not flooded\" class with an orientation of 65% of the total data. However, defining manual stopwords can affect because stopword removal will not delete words that still have context related features to the topic.","PeriodicalId":191671,"journal":{"name":"2021 9th International Conference on Information and Communication Technology (ICoICT)","volume":"448 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116757776","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

Aspect Based Sentiment Analysis With Combination Feature Extraction LDA and Word2vec 结合特征提取LDA和Word2vec的面向方面情感分析

2021 9th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2021-08-03 DOI: 10.1109/ICoICT52021.2021.9527506

Rizka Vio Octriany Inggit Sudiro, S. S. Prasetiyowati, Y. Sibaroni

{"title":"Aspect Based Sentiment Analysis With Combination Feature Extraction LDA and Word2vec","authors":"Rizka Vio Octriany Inggit Sudiro, S. S. Prasetiyowati, Y. Sibaroni","doi":"10.1109/ICoICT52021.2021.9527506","DOIUrl":"https://doi.org/10.1109/ICoICT52021.2021.9527506","url":null,"abstract":"A product review is needed by a customer before he buys a product. Currently, several platforms can be used to provide product reviews, one of which is the beauty product. Every customer can read beauty product reviews, not only from one aspect of the review but it can be from several aspects of the review. it is difficult for consumers to find all the reviews from various aspects quickly. Therefore, in this study, a combination of LDA modeling methods and Word Embedding Word2vec were used, to obtain sentiments from each of the predetermined aspects of the review. In this study, the accuracy of the combination of LDA will be compared with the Word2vec Skip-gram and Continuous-bag-of-word (CBOW) models. From the two combinations, it is found that the combination accuracy of LDA and Word2vec Skip gram is 80.36%, and for CBOW is only 74.37%. Meanwhile, the SVM and K-Fold Cross-Validation algorithms are used to find the accuracy of sentiment predictions on the aspects of price, packaging, and fragrances. Compared to the other two aspects, the packaging aspect has the highest accuracy at 89.71%.","PeriodicalId":191671,"journal":{"name":"2021 9th International Conference on Information and Communication Technology (ICoICT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125393161","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

Aspect-Based Sentiment Analysis in Beauty Product Reviews Using TF-IDF and SVM Algorithm 基于TF-IDF和SVM算法的美容产品评论情感分析

2021 9th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2021-08-03 DOI: 10.1109/ICoICT52021.2021.9527489

Nadira Putri Arthamevia, Adiwijaya, Mahendra Dwifebri Purbolaksono

{"title":"Aspect-Based Sentiment Analysis in Beauty Product Reviews Using TF-IDF and SVM Algorithm","authors":"Nadira Putri Arthamevia, Adiwijaya, Mahendra Dwifebri Purbolaksono","doi":"10.1109/ICoICT52021.2021.9527489","DOIUrl":"https://doi.org/10.1109/ICoICT52021.2021.9527489","url":null,"abstract":"Product reviews are essential in e-commerce as they can help potential buyers make decisions prior to making purchases and help sellers get the measure of their products. A product can have thousands of reviews, making it burdensome for potential buyers and sellers to draw a conclusion from those abundant reviews. This research built a system that applies Aspect-based Sentiment Analysis (ABSA) with a dataset from product reviews on the Female Daily website. The system was built using TF-IDF as its feature extraction method combined with word bigram and word bigram. The Support Vector Machine (SVM) algorithm is used to classify the sentiments. This experiment results indicate that the preprocessing stage, especially the stemming and stopwords removal process are greatly affects the accuracy results. The choice of word N-gram is also crucial, where this research shows that the word unigram gives a higher accuracy than the word bigram. The final results of this research show that TF-IDF combined with word unigram and SVM with a linear kernel brings out the best accuracy, that is to say, 88.35%.","PeriodicalId":191671,"journal":{"name":"2021 9th International Conference on Information and Communication Technology (ICoICT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125564929","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

Randomness, Uniqueness, and Steadiness Evaluation of Physical Unclonable Functions 物理不可克隆函数的随机性、唯一性和稳定性评价

2021 9th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2021-08-03 DOI: 10.1109/ICoICT52021.2021.9527493

Rivaldo Ludovicus Sembiring, Rizka Reza Pahlevi, Parman Sukarno

{"title":"Randomness, Uniqueness, and Steadiness Evaluation of Physical Unclonable Functions","authors":"Rivaldo Ludovicus Sembiring, Rizka Reza Pahlevi, Parman Sukarno","doi":"10.1109/ICoICT52021.2021.9527493","DOIUrl":"https://doi.org/10.1109/ICoICT52021.2021.9527493","url":null,"abstract":"The development of the Internet of Things (IoT) can be found in various places. However, multiple kinds of attacks have also increased. IoT devices are very vulnerable to attacks, both physical and non-physical, because of their unmanned nature. In non-physical attacks, the most important thing is to secure the data on memory devices. Physical unclonable function (PUF) is the strongest and lightest method to securing memory devices and can be used on unmanned IoT devices. The advantage of PUF over current classical cryptography types is its compatibility on IoT devices with limited computing resources. However, before PUF can be claimed to provide security property, it must meet the evaluation indicators: randomness, uniqueness, and steadiness. PUF can be the best solution for securing data on IoT devices because the encryption process does not put a secret key on the device. Instead, the key is generated randomly. This research is evaluating two different PUF chips with the same PUF design. We designed the arbiter PUF on the FPGA and evaluated the results of the responses given. Through rigorous experiments, this research succeeded to evaluate the three indicators of PUF where the randomness is 54.43%:45.4%, and 25.88%: 74.2%, the uniqueness between chip is 69.53%, and lastly, the steadiness is 89.84%, and 91.41%.","PeriodicalId":191671,"journal":{"name":"2021 9th International Conference on Information and Communication Technology (ICoICT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126762596","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

Emotion Classification on Indonesian Twitter Using Convolutional Neural Network (CNN) 使用卷积神经网络(CNN)对印尼推特进行情绪分类

2021 9th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2021-08-03 DOI: 10.1109/ICoICT52021.2021.9527447

Firhan Maulana Rusli, Rita Rismala, Hani Nurrahmi

引用次数: 0

Convolutional Neural Networks for Indonesian Aspect-Based Sentiment Analysis Tourism Review 卷积神经网络在印尼基于方面的情感分析中的应用

2021 9th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2021-08-03 DOI: 10.1109/ICoICT52021.2021.9527518

Royan Abida N. Nayoan, Ahmad Fathan Hidayatullah, Dhomas Hatta Fudholi

{"title":"Convolutional Neural Networks for Indonesian Aspect-Based Sentiment Analysis Tourism Review","authors":"Royan Abida N. Nayoan, Ahmad Fathan Hidayatullah, Dhomas Hatta Fudholi","doi":"10.1109/ICoICT52021.2021.9527518","DOIUrl":"https://doi.org/10.1109/ICoICT52021.2021.9527518","url":null,"abstract":"In recent years, electronic word of mouth (e-WOM) has been widely used by people around the world. Tripadvisor is an e-WOM travel website that provides information about reviews and opinions on travel-related content. To help users gather information faster, aspect-based sentiment analysis is necessary. Aspect-based sentiment analysis helps users to capture and extract important features from the reviews. Therefore, this study aims to build an aspect-based sentiment analysis model of Indonesian tourism review by extracting aspect-category and their corresponding polarities from user reviews. To gain the best model, we performed several experiments by using Convolutional Neural Networks (CNN). Moreover, we compared our CNN model with CNN-LSTM and CNN-GRU to identify the sentiment and aspects from the reviews. We also performed negation handling in our feature extraction process to improve our CNN models. Based on our experiments, CNN combined with both POS tag and negation handling outperformed the other models with the accuracy of sentiment analysis of 0.9522 and aspect category of 0.9551.","PeriodicalId":191671,"journal":{"name":"2021 9th International Conference on Information and Communication Technology (ICoICT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117311711","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: 4

Sentinel 1 Classification for Garlic Land Identification using Support Vector Machine 基于支持向量机的大蒜地Sentinel 1分类

2021 9th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2021-08-03 DOI: 10.1109/ICoICT52021.2021.9527446

M. A. Agmalaro, I. S. Sitanggang, Mia Larisa Waskito

引用次数: 5

Holick's Rule Implementation: Calculation of Produced Vitamin D from Sunlight Based on UV Index, Skin Type, and Area of Sunlight Exposure on the Body 霍利克规则的实施:根据紫外线指数、皮肤类型和身体阳光照射面积计算从阳光中产生的维生素D

2021 9th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2021-08-03 DOI: 10.1109/ICoICT52021.2021.9527498

J. Salomo, Eduardus Ariasena, Athaya Syaqra, Salma Majidah

引用次数: 0

Fingerprint Enhancement using Iterative Contextual Filtering for Fingerprint Matching 基于迭代上下文滤波的指纹匹配增强

2021 9th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2021-08-03 DOI: 10.1109/ICoICT52021.2021.9527482

Brama Yoga Satria, Agus Bejo, Risanuri Hidayat

引用次数: 1

Assurance Case Pattern using SACM Notation 使用SACM符号的保证案例模式

2021 9th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2021-08-03 DOI: 10.1109/ICoICT52021.2021.9527483

N. Selviandro

{"title":"Assurance Case Pattern using SACM Notation","authors":"N. Selviandro","doi":"10.1109/ICoICT52021.2021.9527483","DOIUrl":"https://doi.org/10.1109/ICoICT52021.2021.9527483","url":null,"abstract":"The Structured Assurance Case Metamodel (SACM) is a metamodel and specification that can be used to represent structured assurance cases. An assurance case is an approach for analysing, documenting, and communicating a clear structured argument and evidence about a particular system within a specific environment and circumstances. SACM provides abstract syntax with a set of features to develop assurance cases, including supporting the development of an assurance case pattern. A pattern in the assurance case development context is useful, for example, as an approach for abstracting the details of the argument, and when possible, it can be used for the development of other arguments by instantiating the pattern in a specific domain application. To support the development and adoption of SACM, we have developed SACM Notation (SACMN) as a concrete syntax that consists of visual vocabularies and compositional rules (as visual grammar). The developed notation has been included as part of the SACM standard 2.1 update version. In this paper, we introduce and discuss the application of the assurance case pattern using SACMN to support the SACM adoption in the development of assurance cases.","PeriodicalId":191671,"journal":{"name":"2021 9th International Conference on Information and Communication Technology (ICoICT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125780639","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