2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)最新文献_第3页

Analysis of Tuberculosis Cases in Indonesian by Using K-means Clustering Technique 用k -均值聚类技术分析印度尼西亚肺结核病例

2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2018-11-01 DOI: 10.1109/ISRITI.2018.8864288

Prihandoko, Bertalya, Lilis Setyowati

引用次数: 0

SEMAR: An Interface for Indonesian Hate Speech Detection Using Machine Learning 基于机器学习的印尼仇恨言论检测界面

2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2018-11-01 DOI: 10.1109/ISRITI.2018.8864484

Umu Amanah Nur Rohmawati, S. W. Sihwi, D. E. Cahyani

{"title":"SEMAR: An Interface for Indonesian Hate Speech Detection Using Machine Learning","authors":"Umu Amanah Nur Rohmawati, S. W. Sihwi, D. E. Cahyani","doi":"10.1109/ISRITI.2018.8864484","DOIUrl":"https://doi.org/10.1109/ISRITI.2018.8864484","url":null,"abstract":"Hate Speech has become government and public's concern because of the high number of hate speech cases on social media that occur in Indonesia, which are getting increased in recent years. Because of that, Indonesian hate speech detection becomes crucial. This research proposes SEMAR, an engine to detect Indonesian hate speech built using machine learning technique. This study tested and compared popular supervised algorithms including Naive Bayes Classifier (NBC), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Logistic Regression (LR) to determine which of the method is most suitable for solving Indonesian hate speech issue. It also compared two vectorizers, which are Hashing vectorizer and Term Frequency Inverse Document Frequency (TF-IDF). SEMAR interfaces were successfully developed, they are Application Programming Interface (API) and anti-hate comment WordPress plugin. SEMAR API was implemented using SVM with TF-IDF model, due to the highest accuracy with average score is (0.870726276). API allows web developer to use machine learning model by accessing endpoint URL from where the API is served and do not need training the model every time they use it, while WordPress is chosen because it is the most widely used Content Management System (CMS) for creating websites in the world (31,7%). Not only detecting hate comment automatically, but the system also designed to make training data continues to grow. It allows user to give feedback on prediction given by engine, feedback stored into database as new training data. The System will perform self-training daily using both old and new training data so the model's performance will improve time by time.","PeriodicalId":162781,"journal":{"name":"2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134497766","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: 7

Information Extraction of Traffic Condition from Social Media using Bidirectional LSTM-CNN 基于双向LSTM-CNN的社交媒体交通状况信息提取

2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2018-11-01 DOI: 10.1109/ISRITI.2018.8864265

M. R. Alifi, S. Supangkat

{"title":"Information Extraction of Traffic Condition from Social Media using Bidirectional LSTM-CNN","authors":"M. R. Alifi, S. Supangkat","doi":"10.1109/ISRITI.2018.8864265","DOIUrl":"https://doi.org/10.1109/ISRITI.2018.8864265","url":null,"abstract":"Twitter is social media that has become a source of information for real-time traffic condition in Indonesia. Generally, information contained on Twitter is used for shortterm needs, such as to find out the point of congestion at that time. Whereas, the information can be collected and processed for long-term needs, such as congestion-prone points mapping at certain times. This processed information is very useful for city stakeholders. Information extraction is needed to process information in the form of text from social media that is originally unstructured to be structured. NER technique can be applied to obtain entities that represent traffic condition. This study classifies entities into 11 classes with the BIO encoding scheme, namely B_TIME, I_TIME, B_LOCT, I_LOCT, B_COND, I_COND, B_CAUS, I_CAUS, B_WEAT, I_WEAT, B_MISC, I_MISC, and O. The defined classes represent entities of time, location, condition, cause, weather, miscellaneous, and others. This study proposes a solution in the form of model architecture design using deep learning approach. Bidirectional LSTM approach is used to handle word level. While the CNN approach is used to handle character level. The combination of these two deep learning methods accompanied by word embedding produces the value of F-measure 0.789.","PeriodicalId":162781,"journal":{"name":"2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115634337","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

LatAksLate: Javanese Script Translator based on Indonesian Speech Recognition using Sphinx-4 and Google API LatAksLate:基于印尼语语音识别的爪哇文字翻译器，使用Sphinx-4和Google API

2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2018-11-01 DOI: 10.1109/ISRITI.2018.8864274

Arif Nursetyo, D. Setiadi

{"title":"LatAksLate: Javanese Script Translator based on Indonesian Speech Recognition using Sphinx-4 and Google API","authors":"Arif Nursetyo, D. Setiadi","doi":"10.1109/ISRITI.2018.8864274","DOIUrl":"https://doi.org/10.1109/ISRITI.2018.8864274","url":null,"abstract":"Javanese script is one of the cultural heritage of Indonesia, especially the Javanese. Currently, the understanding of the younger generation of Javanese culture, especially the Javanese script has decreased. The main cause is, Javanese script only taught in school but never used in everyday life. This research proposes the application of speech recognition combined with the process of translating Indonesian into Javanese script to facilitate the process of learning Javanese script. The application is built based on Android using the Sphinx-4 framework for speech recognition process in Indonesian language and Google API as a database for the translation process. In order to obtain good accuracy, several steps are introduced phonetic recognition of speech input sequences involving model dictionaries, phonetic sequence matching transcripts of language models acting as datasets, testing the sequence of acoustic senones of speech properties to match phonemic properties, decoding acoustic models evaluated by word error rate (WER), syllables category categorization into syllables Javanese text, most recently by classifying syllables of Javanese text to be converted into Javanese script with true type font (TTF). Based on the test results obtained recognition accuracy and translation of 82.46%.","PeriodicalId":162781,"journal":{"name":"2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114374517","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

Agricultural Strategic Commodity Price Forecasting Using Artificial Neural Network 基于人工神经网络的农业战略商品价格预测

2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2018-11-01 DOI: 10.1109/ISRITI.2018.8864442

Wiwik Anggraeni, Faizal Mahananto, M. Rofiq, K. B. Andri, Sumaryanto, Z. Zaini, A. P. Subriadi

引用次数: 9

Development of A Warning Receiver System for Public Display Based on Application-Controlled Video Switcher 基于应用控制视频切换器的公共显示报警接收系统的研制

2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2018-11-01 DOI: 10.1109/ISRITI.2018.8864275

Moh. Hasbi Assidiqi, M. Bachtiar, Muchamad Chaninul Fuad, I. Ferdiansyah, Mohamad Nasyir Tamara

引用次数: 0

Data Communication Protocols Supporting Monitoring Service of Traffic Management Center 支撑交通管理中心监控业务的数据通信协议

2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2018-11-01 DOI: 10.1109/ISRITI.2018.8864453

A. Affandi, E. Setijadi, Gatot Kusrahardjo, Michael Ardita

引用次数: 0

Secured PVD Video Steganography Method based on AES and Linear Congruential Generator 基于AES和线性同余发生器的安全PVD视频隐写方法

2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2018-11-01 DOI: 10.1109/ISRITI.2018.8864466

E. H. Rachmawanto, Khabib Prasetyo, C. A. Sari, D. Setiadi, Nova Rijati

引用次数: 6

Logic Scoring of Preference Method for Determining Landfill with Geographic Information System 基于地理信息系统的垃圾填埋场优选方法的逻辑评分

2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2018-11-01 DOI: 10.1109/ISRITI.2018.8864330

Dovel Pirmanto, J. E. Suseno, K. Adi

{"title":"Logic Scoring of Preference Method for Determining Landfill with Geographic Information System","authors":"Dovel Pirmanto, J. E. Suseno, K. Adi","doi":"10.1109/ISRITI.2018.8864330","DOIUrl":"https://doi.org/10.1109/ISRITI.2018.8864330","url":null,"abstract":"Environmental pollution is a challenge that must be solved by each region, pollution of the environment will have a negative impact on humans, the environment and social. One of the causes is waste management. Until now garbage in Sungai Penuh City has reached 2.3 ton/day which was dumped in landfill. The purpose of this study is for developing an information system for the selection of landfill by using the logic scoring of preference method. The method of logic scoring of preference can maintain the input value of a number of input attributes and produce realistic land capability and match maps. So that the logic scoring of preference method becomes an effective tool to integrate regional land use planning. Geographic Information Systems in the spatial support system are very suitable for mapping landfill, because of the ease of interacting and using together. Based on the results of the research, the best alternative land for landfill in the Bungkal River subdistrict is Koto Tengah village with a value of 0.88 from the calculation of criteria attribute and agregator on logic scoring of preference.","PeriodicalId":162781,"journal":{"name":"2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131283487","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

Performance Comparison of Genetic Algorithm Operator Combinations for optimization Problems 遗传算法算子组合优化问题的性能比较

2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2018-11-01 DOI: 10.1109/ISRITI.2018.8864469

Ica Kurnia Hildayanti, I. Soesanti, A. E. Permanasari

{"title":"Performance Comparison of Genetic Algorithm Operator Combinations for optimization Problems","authors":"Ica Kurnia Hildayanti, I. Soesanti, A. E. Permanasari","doi":"10.1109/ISRITI.2018.8864469","DOIUrl":"https://doi.org/10.1109/ISRITI.2018.8864469","url":null,"abstract":"Genetic Algorithm (GA) is a meta-heuristic search algorithms and the process of GA inspired by natural evolution theory. GA has chromosome-forming operators i.e. selection, crossover, and mutation. These operators have different techniques to solve optimization problems. This paper presents performance comparison of 36 combination operators from 4 selection techniques, 3 crossover techniques, and 3 mutation techniques. Combinations of selection, crossover and mutation operators are simulated for optimizing three benchmark function: Rastrigin function, Sphere function, and Exponential Function. The purpose of this study to find suitable combinations of selection, crossover, and mutation technique for each objective function tested. The performance to be analyzed and compared in this study are average iteration, the best value of objective function f(x)/fitness value, xi value, and average time. The result shows that different selection, mutation and crossover have different effectiveness to optimize various objective functions, and for each objective function have different suitable GA operator combination to find the optimal solution.","PeriodicalId":162781,"journal":{"name":"2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126657286","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