2020 8th International Conference on Information and Communication Technology (ICoICT)最新文献_第3页

Fractal Batik Motifs Generation Using Variations of Parameters in Julia Set Function 利用Julia集合函数参数的变化生成分形蜡染图案

2020 8th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2020-06-01 DOI: 10.1109/ICoICT49345.2020.9166282

R. Isnanto, A. Hidayatno, Ajub Ajulian Zahra

{"title":"Fractal Batik Motifs Generation Using Variations of Parameters in Julia Set Function","authors":"R. Isnanto, A. Hidayatno, Ajub Ajulian Zahra","doi":"10.1109/ICoICT49345.2020.9166282","DOIUrl":"https://doi.org/10.1109/ICoICT49345.2020.9166282","url":null,"abstract":"The fractal concept is the generation of images graphically with self-similarity properties produced by recursive or iterative algorithms to produce a new image form. Fractal structure is a common tool for describing the visual effects of one or more objects. In this research, we will design a system that can design batik fractal motifs using the Julia set. Therefore, a programmable tool to generate computerized batik motifs is needed. In this research, Julia set was used as the basis for generating batik motifs. In this system, the process of generating fractal batik images consists of 3 (three) steps: (1) determining the shape of the Julia set function for some batik motifs; (2) visualizing the results of the first step using a Pythonbased program; and (3) designing the fractal batik motifs using Julia set obtained in the second step. From experiments conducted in generating batik fractal, some conclusions were obtained as follows. First, for the greater number of iterations, the resulting image will be more detailed and the number of colors will increase. Second, the result image is influenced by the value of the complex c and the number of colors or gray-levels indicated by the number of iterations, i.e., the greater the number of iterations, the more colors are generated. Third, several variations of input parameters produce images that approach traditional batik motifs. In this research, traditional motifs that can be approached by Julia-set functions are: Parangkusumo, Nitik, Batik Liong, Mega Mendung, and Ceplok motifs. Fourth, changes in the number of iterations and the value of c applied to Julia set are better able to generate the variations of motifs. Thus, the Julia set function can help the batik designer in making fractal batik motifs.","PeriodicalId":113108,"journal":{"name":"2020 8th International Conference on Information and Communication Technology (ICoICT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125617133","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

Method of Systematic Literature Review for Internet of Things in ZigBee Smart Agriculture ZigBee智慧农业物联网系统文献综述方法

2020 8th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2020-06-01 DOI: 10.1109/ICoICT49345.2020.9166195

Taufik Hidayat, Rahutomo Mahardiko, Sianturi Tigor Franky D

引用次数: 17

Face Recognition In Low Lighting Conditions Using Fisherface Method And CLAHE Techniques 基于鱼脸法和CLAHE技术的低光照条件下人脸识别

2020 8th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2020-06-01 DOI: 10.1109/ICoICT49345.2020.9166317

Muhammad Fauzan Rahman, F. Sthevanie, Kurniawan Nur Ramadhani

引用次数: 6

On The Feature Extraction For Sentiment Analysis of Movie Reviews Based on SVM 基于SVM的电影评论情感分析特征提取研究

2020 8th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2020-06-01 DOI: 10.1109/ICoICT49345.2020.9166397

Fitria Cahyanti, Adiwijaya, S. A. Faraby

{"title":"On The Feature Extraction For Sentiment Analysis of Movie Reviews Based on SVM","authors":"Fitria Cahyanti, Adiwijaya, S. A. Faraby","doi":"10.1109/ICoICT49345.2020.9166397","DOIUrl":"https://doi.org/10.1109/ICoICT49345.2020.9166397","url":null,"abstract":"Watching a movie is one of the activities that reduce bored, so it is necessary to look for information about the movie, which is packaged in the form of a movie review to determine whether the movie considered for viewing or no. However, in searching for information through movie reviews, there are obstacles because there are many reviews conducted by reviewers. Therefore, sentiment analysis is needed aims to classify the movie review into positive and negative sentiments. Machine learning methods can use as a sentiment analysis classification because that can produce the best performance, the method called Support Vector Machine (SVM). That was a reason SVM classification used in sentiment analysis on movie review data. Use feature extraction of Term Frequency- Inverse Document Frequency (TF-IDF) was also carried out in the research this as a method of weighting words which then combined with the extraction of Latent features Dirichlet Allocation (LDA) as a method of modeling topics that can overcome the shortcomings of SVM. This research produced the best performance on a combination of TF-IDF and LDA, with 240 topics has 29792 features, which is 82.16%.","PeriodicalId":113108,"journal":{"name":"2020 8th International Conference on Information and Communication Technology (ICoICT)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132937074","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: 6

Computational Parallel of K-Nearest Neighbor on Page Blocks Classification Dataset 页块分类数据集上k近邻的并行计算

2020 8th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2020-06-01 DOI: 10.1109/ICoICT49345.2020.9166293

Damar Zaky, P. H. Gunawan

{"title":"Computational Parallel of K-Nearest Neighbor on Page Blocks Classification Dataset","authors":"Damar Zaky, P. H. Gunawan","doi":"10.1109/ICoICT49345.2020.9166293","DOIUrl":"https://doi.org/10.1109/ICoICT49345.2020.9166293","url":null,"abstract":"K-Nearest Neighbor (KNN) is considered as one of the simplest machine learning algorithms. While the implementation is quite simple, KNN is actually computationally expensive that makes it take a lot of time when it tries to predict. KNN has been known to be a lazy learning machine learning method that means that this method doesn’t generalize the data, instead it has to memorize the training data, even when testing. This paper aims to optimize the KNN classifier to solve page blocks classification by making the algorithm parallel. The part of the KNN algorithm that is changed to become parallel is the outer part where the task for each test data is divided according to the number of processors. In this work, we use parallel KNN to classify page blocks. Page blocks are any blocks of a page layout that are detected by using a segmentation technique, the KNN is trained to classify whether a block is a vertical line, picture, text, horizontal line or graphic. The experiment shows that the KNN classifier obtains an accuracy of 93.51% and by using parallel KNN, a speedup of 4.64 times faster and an efficiency of 57.96% can be obtained by using 8 processors and an increasing number of grids up to 6040 while it obtains the same accuracy as serial.","PeriodicalId":113108,"journal":{"name":"2020 8th International Conference on Information and Communication Technology (ICoICT)","volume":"271 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134011010","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

QSAR Study of Fusidic Acid Derivative as Anti-Malaria Agents by using Artificial Neural Network-Genetic Algorithm 应用人工神经网络遗传算法对氟西地酸衍生物抗疟疾药物的QSAR研究

2020 8th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2020-06-01 DOI: 10.1109/ICoICT49345.2020.9166158

Hamzah Faisal Azmi, K. Lhaksmana, I. Kurniawan

{"title":"QSAR Study of Fusidic Acid Derivative as Anti-Malaria Agents by using Artificial Neural Network-Genetic Algorithm","authors":"Hamzah Faisal Azmi, K. Lhaksmana, I. Kurniawan","doi":"10.1109/ICoICT49345.2020.9166158","DOIUrl":"https://doi.org/10.1109/ICoICT49345.2020.9166158","url":null,"abstract":"Malaria is a disease that caused many adverse effects on humans. Various attempts have been done to find new anti-malarial agents due to the resistance problem of the existing drug. Fusidic acid is known as one of a compound that is promising to be used as an anti-malaria agent. However, this compound should be derived to obtain a new fusidic acid derivative that has better activity. The exploration of the compound in conventional style has a shortcoming in the term of time and cost. Therefore, an alternative method is required to accelerate the design. In this study, we applied a quantitative structure-activity relationship (QSAR) to produce a predictive model. The produced model can be used to predict the activity of the compound as an anti-malaria agent. The development of the model was performed by using genetic algorithm (GA) for feature selection and artificial neural network (ANN) for model development. We developed five models by utilizing a different number of the descriptor in each model. The validation process was performed by evaluating several validation parameters, such as accuracy. According to the results, we found that the model 3, which is comprised of seven descriptors, produce a better result with the accuracies of internal and external data set are 0.96 and 0.92, respectively.","PeriodicalId":113108,"journal":{"name":"2020 8th International Conference on Information and Communication Technology (ICoICT)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115304002","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: 9

Thymun: Smart Mobile Health Platform for The Autoimmune Community to Improve the Health and Well-Being of Autoimmune Sufferers in Indonesia Thymun:自身免疫性社区的智能移动健康平台，以改善印度尼西亚自身免疫性患者的健康和福祉

2020 8th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2020-06-01 DOI: 10.1109/ICoICT49345.2020.9166356

Yasmin Salamah, Rahma Dany Asyifa, Tsonya Yumna Afifah, Fajar Maulana, Auzi Asfarian

{"title":"Thymun: Smart Mobile Health Platform for The Autoimmune Community to Improve the Health and Well-Being of Autoimmune Sufferers in Indonesia","authors":"Yasmin Salamah, Rahma Dany Asyifa, Tsonya Yumna Afifah, Fajar Maulana, Auzi Asfarian","doi":"10.1109/ICoICT49345.2020.9166356","DOIUrl":"https://doi.org/10.1109/ICoICT49345.2020.9166356","url":null,"abstract":"Autoimmune disease occurs when the autoimmune response or an immune system response attacks the body’s tissues causing altered functions in a human’s body. Some autoimmune diseases are life-threatening and require a lifetime of treatment, and the cure to the autoimmune itself has not yet to be discovered. This condition interferes with daily activities and causes drawbacks in productivity, mental health, and their healing progress. In this paper, we introduce Thymun, a smart health mobile application-based community platform to improve the wellness of autoimmune sufferers. Based on the requirements of several health professionals with years in autoimmune fields, activist and patients itself Thymun comes with the aims to help the digitalization of therapy management in order to help literate health professional and patients on ongoing-clinical trials, symptom’s progression tracking, dietary approach on disease management and the personal data support of selective dieting, community platform, and other major symptoms management. Thymun is made into an android prototype using prototyping method. Based on consumer evaluation and other professional feedbacks thymun’s features are met with major requirements for people with autoimmune.","PeriodicalId":113108,"journal":{"name":"2020 8th International Conference on Information and Communication Technology (ICoICT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125032409","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

Design and Characterization of Mobile Landslide Monitoring System 移动式滑坡监测系统的设计与特性研究

2020 8th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2020-06-01 DOI: 10.1109/ICoICT49345.2020.9166437

Suryadi, E. Kurniawan, A. Tohari, P. Priambodo

引用次数: 0

Brand Awareness Using Network Modeling Method 使用网络建模方法的品牌意识

2020 8th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2020-06-01 DOI: 10.1109/ICoICT49345.2020.9166245

Muhamad Fulki Firdaus, Z. Baizal, Made Kevin Bratawisnu, Hanafi Abdullah Gusman

{"title":"Brand Awareness Using Network Modeling Method","authors":"Muhamad Fulki Firdaus, Z. Baizal, Made Kevin Bratawisnu, Hanafi Abdullah Gusman","doi":"10.1109/ICoICT49345.2020.9166245","DOIUrl":"https://doi.org/10.1109/ICoICT49345.2020.9166245","url":null,"abstract":"The using of online social network has made powerful evolution in digital era. Nowadays, social network is a center of information exchange. Online social networks provide information in the form of user opinion about their brand awareness. The user’s opinion represents the level of awareness of the user regarding the existence of the brand. The circulation of information on the social network is widely known as User Generated Content (UGC). Organizations can use the UGC data to assess their brand rankings. The proper method is needed to be able to process UGC so that it is able to generate insight for the organization. This study utilizes social network phenomena to measure brand ranking in analyzing human awareness of a brand using Social Network Analysis (SNA). SNA is an analysis method for observing social network (or social media) by graph modelling. We use network properties to measure interaction intensity on Traveloka.com, Tiket.com, and Pegi-Pegi.com. The results show that the brand awareness of Pegi-Pegi is superior compared to the others. Network property valuation can be used as an alternative for ranking the company’s position based on UGC in social media, especially Twitter.","PeriodicalId":113108,"journal":{"name":"2020 8th International Conference on Information and Communication Technology (ICoICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125812626","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

Hate Code Detection in Indonesian Tweets using Machine Learning Approach: A Dataset and Preliminary Study 使用机器学习方法检测印度尼西亚推文中的仇恨代码:一个数据集和初步研究

2020 8th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2020-06-01 DOI: 10.1109/ICoICT49345.2020.9166251

Damayanti Elisabeth, I. Budi, Muhammad Okky Ibrohim

{"title":"Hate Code Detection in Indonesian Tweets using Machine Learning Approach: A Dataset and Preliminary Study","authors":"Damayanti Elisabeth, I. Budi, Muhammad Okky Ibrohim","doi":"10.1109/ICoICT49345.2020.9166251","DOIUrl":"https://doi.org/10.1109/ICoICT49345.2020.9166251","url":null,"abstract":"The existence of social media causes side effects from freedom of speech to freedom to hate. People can spread hate speech with creative ways to avoid the hate speech detector. Implicit intends used using many codes. The purpose of using these codes is to disguise their hate speech targets. This paper presents an implementation of hate code detection for Indonesian tweets using machine learning and a classification explainer. First, we developed a dataset for hate codes ground truth. We generated hate codes from two scenarios i.e., hate code from hate speech classification and hate code from hate code classification. We used Logistic Regression (LR), Naive Bayes (NB), and Random Forest Decision Tree (RFDT) as our classifier. We also used TF-IDF and word bigrams as the features. The codes consist of word and phrase form. The best f-measure score is 94.90% from hate code classification using Logistic Regression with abusive codes elimination. This number means the model can detect all tweets that have no hate codes. For tweets that annotated have hate code, the f-measure is 28.23% for recognized all the hate codes, and the recall is 56.91%.","PeriodicalId":113108,"journal":{"name":"2020 8th International Conference on Information and Communication Technology (ICoICT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130362763","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: 11