2022 International Symposium on Information Technology and Digital Innovation (ISITDI)最新文献_第3页

BERT and ULMFiT Ensemble for Personality Prediction from Indonesian Social Media Text 印尼社交媒体文本个性预测的BERT和ULMFiT集成

2022 International Symposium on Information Technology and Digital Innovation (ISITDI) Pub Date : 2022-07-27 DOI: 10.1109/ISITDI55734.2022.9944476

Noptovius Halimawan, Derwin Suhartono, Aryo Pradipta Gema, Rezki Yunanda

{"title":"BERT and ULMFiT Ensemble for Personality Prediction from Indonesian Social Media Text","authors":"Noptovius Halimawan, Derwin Suhartono, Aryo Pradipta Gema, Rezki Yunanda","doi":"10.1109/ISITDI55734.2022.9944476","DOIUrl":"https://doi.org/10.1109/ISITDI55734.2022.9944476","url":null,"abstract":"Predicting personality is a growing topic in the field of natural language processing. The study of personality prediction has been proven to benefit the development of recommender systems and automated personality assessments by previous studies. Additionally, the widespread usage of social media in Indonesia such as Twitter has served as a potential source of data for developing such models. Existing personality prediction models has explored the implementation of both traditional machine learning models and deep learning models, with the latter proven to perform better with more data. Despite so, there is not much development of deep learning-based personality prediction models in the domain of Indonesian text. We propose a deep learning-based ensemble model based on Universal Language Model Fine Tuning (ULMFiT) and Bidirectional Encoder Representations from Transformers (BERT), the state-of-the-art pre-trained models for text classification. The model is trained with Indonesian tweets labelled with Five Factor Model (FFM) personality traits along with the users' metadata. The model is tested with five predetermined scenarios to prove the robustness of its performance. The final evaluation of the model proves that it has successfully achieved optimal performance of 76.41 % macro-average accuracy and 75.55% macro-average F-1 score, exceeding the state-of-the-art BERT and ULMFiT","PeriodicalId":312644,"journal":{"name":"2022 International Symposium on Information Technology and Digital Innovation (ISITDI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130215847","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

Smart City Service System Design Based On Microservice Architecture: Case Study in Magelang City 基于微服务架构的智慧城市服务系统设计——以马葛朗市为例

2022 International Symposium on Information Technology and Digital Innovation (ISITDI) Pub Date : 2022-07-27 DOI: 10.1109/ISITDI55734.2022.9944498

Muhammad Tri Adhi Utomo, R. Hartanto, S. Sulistyo

引用次数: 0

Predictive Analytic Healthcare Sector Using Classification Machine Learning Algorithm 使用分类机器学习算法的医疗保健行业预测分析

2022 International Symposium on Information Technology and Digital Innovation (ISITDI) Pub Date : 2022-07-27 DOI: 10.1109/ISITDI55734.2022.9944492

Anita Sindar Ros Maryana Sinaga, R. E. Putra

{"title":"Predictive Analytic Healthcare Sector Using Classification Machine Learning Algorithm","authors":"Anita Sindar Ros Maryana Sinaga, R. E. Putra","doi":"10.1109/ISITDI55734.2022.9944492","DOIUrl":"https://doi.org/10.1109/ISITDI55734.2022.9944492","url":null,"abstract":"There is a wealth of data that can be used to improve public health services by leveraging machine intelligence technology. The problem that arises is the distribution of unstructured data, not detailed and incomplete. In this study, data is processed with the machine learning algorithm classification stages that are tailored to the case and the objectives to be achieved. This Big Data Analytics model uses Descriptive analytics in combination with Predictive Analytics to work by looking at future situations in relation to past data. Prescriptive Analytics tries to analyze decision making to deal with this condition. Predictive analytics aims to analyze what patterns emerge in the health sector to improve health services. The Naive Bayes classifier is used to classify whether a patient will return for treatment or not, based on the village area the probability of returning to treatment is 0.40 and not returning to treatment is 0.60. Of the 13 health indicators, the data are grouped into 2 classes, namely fulfilled and unfulfilled by using a type of algorithm that includes the SVM Classifier. The model is same to have perfect prediction accuracy if the AUC value is 0.90. The best test model is Coarse Gaussian SVM result 90.1%. Grouping through decision trees aims to inform the adequacy of tree forecast accuracy = 97.9%, Linear Regression = 96.6%.","PeriodicalId":312644,"journal":{"name":"2022 International Symposium on Information Technology and Digital Innovation (ISITDI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121811173","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 Socio-Technopreneur Platform Model as Innovative Solution to Environmental and Economic Problems 社会-技术企业家平台模式创新解决环境与经济问题的设计

2022 International Symposium on Information Technology and Digital Innovation (ISITDI) Pub Date : 2022-07-27 DOI: 10.1109/ISITDI55734.2022.9944530

Dwi Welly Sukma Nirad, Afriyanti Dwi Kartika, Andrew Kurniawan Vadreas, Winda Amelia, Salsabila Rahmah, H. Andre

{"title":"Design of Socio-Technopreneur Platform Model as Innovative Solution to Environmental and Economic Problems","authors":"Dwi Welly Sukma Nirad, Afriyanti Dwi Kartika, Andrew Kurniawan Vadreas, Winda Amelia, Salsabila Rahmah, H. Andre","doi":"10.1109/ISITDI55734.2022.9944530","DOIUrl":"https://doi.org/10.1109/ISITDI55734.2022.9944530","url":null,"abstract":"Garbage is residual material that comes from various human activities. Poor waste management can cause problems with hygiene, health, and environmental damage. Usually, the cleaners collect the waste and send it to the landfill. However, landfills often have limited capacity, so piling waste in landfills is no longer the best solution. Garbage must be recycled to reduce piles because it can damage the environment. However, not many people are able and willing to manage their daily waste into valuable products. Therefore, this work designed a socio-technopreneur platform with educational and business features. This application allows anyone to be involved in waste management through activities to save waste, learn about waste processing processes, and buy and sell waste. It is an innovative solution to environmental problems and can generate additional income for the community. The business concept in this application is analyzed using the Business Model Canvas (BMC). Application development uses an object-oriented approach and the Waterfall Method. The result of this research is a model of a technological solution that can move the community to protect the environment through independent waste management through the video tutorial feature. Anyone can upload tutorial videos to share. However, the party who will upload consistently is the waste bank manager. In addition, there is also a buying and selling feature that allows users to earn income from product sales. Users can sell natural products such as organic waste or processed products such as liquid organic fertilizer. Of course, the price set for each product is very diverse. The results of the BMC analysis become the basis for determining specific application feature requirements.","PeriodicalId":312644,"journal":{"name":"2022 International Symposium on Information Technology and Digital Innovation (ISITDI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122200923","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

LPWAN Communication in IoT Network for Electrical Energy Monitoring 面向电能监测的物联网LPWAN通信

2022 International Symposium on Information Technology and Digital Innovation (ISITDI) Pub Date : 2022-07-27 DOI: 10.1109/ISITDI55734.2022.9944470

H. Andre, Rizki Wahyu Pratama, Febi Sabila Utari, Yoppi lisyadi Oktapianus, Teddy Yuliswar, Darmawan, Dwi Welly Sukma Nirad, Andrew Kurniawan Vadreas, J. Rahmadoni

引用次数: 0

Image Enhancement on Deep Learning Algorithm for COVID-19 Lung X-Ray Classification 基于深度学习算法的COVID-19肺部x射线分类图像增强

2022 International Symposium on Information Technology and Digital Innovation (ISITDI) Pub Date : 2022-07-27 DOI: 10.1109/ISITDI55734.2022.9944501

Said Thaufik Rizaldi, Mustakim, Inggih Permana, M. Afdal

引用次数: 0

Comparison of Naïve Bayes, C4.5 and K-Nearest Neighbor for Covid-19 Data Classification Naïve贝叶斯、C4.5和k近邻算法在Covid-19数据分类中的比较

2022 International Symposium on Information Technology and Digital Innovation (ISITDI) Pub Date : 2022-07-27 DOI: 10.1109/ISITDI55734.2022.9944507

Umairah Rizkya Gurning, Mustakim, Said Thaufik Rizaldi, Hamdi Syukron

引用次数: 4

Hidden Markov Model as the Predictive Time Series Movement of Natural Gas Price 隐马尔可夫模型对天然气价格时间序列运动的预测

2022 International Symposium on Information Technology and Digital Innovation (ISITDI) Pub Date : 2022-07-27 DOI: 10.1109/ISITDI55734.2022.9944536

D. Devianto, Al Raqna Sari, Maiyastri, A. Ahmad, D. Sulistiowati

{"title":"Hidden Markov Model as the Predictive Time Series Movement of Natural Gas Price","authors":"D. Devianto, Al Raqna Sari, Maiyastri, A. Ahmad, D. Sulistiowati","doi":"10.1109/ISITDI55734.2022.9944536","DOIUrl":"https://doi.org/10.1109/ISITDI55734.2022.9944536","url":null,"abstract":"Natural gas is a vital component of energy supply in the world. The price of natural gas as energy commodities has a huge impact on economic growth. Analysis of natural gas price movements and their volatility is very important for world economic actors. Therefore, the price movement of natural gas cannot be observed directly (hidden), so this study focuses on predicting the probability for an increase at natural gas prices within a certain time series data using the Hidden Markov Model (HMM). There are three fundamental steps in order to solve the HMM model, the first is calculating the probability of observation using the Forward-Backward algorithm, the second is determining the hidden state sequence using the Viterbi algorithm, and the third is predicting HMM parameters using the Baum-Welch algorithm. This study uses monthly natural gas prices data for the period of February 2011 until 2021. The results showed that the most optimal observation sequence prediction for natural gas prices is around March 2020, then natural gas prices are predicted to fluctuate around April and May 2021. In general, based on HMM analysis in the long term, there will be an upward trend in the prices of natural gas in the future. This is presumably due to limited resources and increasing demand. These results can be considered in the analysis of sustainability energy for natural gas demand in the future in the aspect of time series price movement.","PeriodicalId":312644,"journal":{"name":"2022 International Symposium on Information Technology and Digital Innovation (ISITDI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125188504","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

Detection, Prevention and Emergency Solution of Road Accidents in Bangladesh using IoT 使用物联网的孟加拉国道路交通事故检测、预防和应急解决方案

2022 International Symposium on Information Technology and Digital Innovation (ISITDI) Pub Date : 2022-07-27 DOI: 10.1109/ISITDI55734.2022.9944436

Md. Sumon Fakir, Najmus Sakib, Md. Solaiman Mia, Najmus Sakib Sizan

引用次数: 0

Gamification Methods of Game-Based Learning Applications in Medical Competence: A Systematic Literature Review 基于游戏学习的游戏化方法在医学能力中的应用:系统的文献综述

2022 International Symposium on Information Technology and Digital Innovation (ISITDI) Pub Date : 2022-07-27 DOI: 10.1109/ISITDI55734.2022.9944535

Lazuardi Fatahilah Hamdi, B. Hantono, A. E. Permanasari

{"title":"Gamification Methods of Game-Based Learning Applications in Medical Competence: A Systematic Literature Review","authors":"Lazuardi Fatahilah Hamdi, B. Hantono, A. E. Permanasari","doi":"10.1109/ISITDI55734.2022.9944535","DOIUrl":"https://doi.org/10.1109/ISITDI55734.2022.9944535","url":null,"abstract":"E-learning implementation can overcome many problems of learning process such as distance, time, and cost. For example, in companies, e-learning can increase the knowledge of employee. E-learning also can be used to improve skills for medical personnel that are important in health care education. Gamification can be implemented by turning the whole learning process into a game by using game elements such as badges, points, levels, and leaderboards in non-game settings to encourage the desired behavior and achievement goals. This study aims to find the implementation of Game-Based Learning Applications in Medical Competence. Using electronic search, we found 5 papers using a mobile application, 2 papers using a desktop application, 10 papers using virtual reality, 5 papers using a web application, and 2 Miscellaneous. All of the papers have environment or media and game-elements to apply gamification. All paper said that gamification can enhance medical competence, but just 7 papers said gamification can increase user attention and motivation and 3 papers said gamification can make user feel challenging and interesting. However, this paper proposes a different way to implement gamification elements. This study shows that gamification is a suitable learning method for medical personnel to improve their knowledge and increase motivation, attention, learning process, and learning outcomes.","PeriodicalId":312644,"journal":{"name":"2022 International Symposium on Information Technology and Digital Innovation (ISITDI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114429757","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