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SSRN 2022年

作者： Girsang, Abba Suganda Noveta, Bima Krisna Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Indonesia is in an area prone to natural disasters, there were 4,650 natural disasters that occurred in 2020. However, the community does not have spatial data regarding the location of the disasters that occurred. The purpose of this study is to provide the location of natural disasters that are poured into maps to the public by utilizing Twitter data which has a fast distribution with the fifth most users in the world. This study proposes a system for geolocation of types of natural disasters obtained from Twitter, which are classified into eight classes of natural disasters using the Support Vector Machine (SVM) method. Then the Twitter text is extracted to find the location in the Twitter text using Named Entity Recognition (NER). NER uses six special classes based on the regional level in Indonesia with an accuracy rate of 85.65%. The result of geolocation using ArcGIS maps is 87,5%. It is hoped that with this research, people will get spatial data to see the points of natural disasters that occur using maps. So, the public can anticipate future disasters, and the authorities can send aid to the location. © 2022, The Authors. All rights reserved.

关键词： Location

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White rice stem borer pest detection system using image-based convolution neural network

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Procedia computer science 2024年 245卷 518-527页

作者： Akhmad Saufi Suharjito Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480

Preventing agricultural resource loss caused by pests remains a crucial issue. While technological advancements are being achieved, the current agricultural management methods and equipment have yet to meet the required level for precise pest control, a huge portion of the pest population analysis process is still conducted manually. As a solution to this issue, the development of a White Rice Stem Borer pest detection system has been conducted by applying Convolutional Neural Network (CNN) technology to calculate the pest population count at the research location. This system has been specifically designed to detect the White Rice Stem Borer using available traps. The method involves training data from a direct dataset obtained from the field, categorized into two positive and negative classes of the White Stem Borer pests. Six models have been trained from this dataset, utilizing two different architectures. Out of the six trained models, four showed potential overfitting, one exhibited underfitting, and one model demonstrated optimal results. The highest accuracy in image detection achieved by the most optimal CNN model was 97.35%, with a training accuracy of 98.54%. This best-performing model utilized an architecture with three Convolution layers, 50 Epochs, and an automatic data split with an 80:20 training-validation data ratio. From the research findings, it is concluded that this study can assist in automatically analyzing the quantity of White Stem Borer pests in a specific area without directly counting the number of pests from existing traps. However, the study still encounters a limitation—the detection process still requires substantial server resources and cannot be directly processed on the Raspberry PI device installed in the trap. Consequently, the detection relies on transmitting image data from the field device to the server before the detection process can occur.

关键词： White Rice Stem Borer Stem Borer White Stem Borer Detection Pest Detection Convolutional Neural Network (CNN)

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Analysis of delay factors on drilling projects on PT China Oilfields Services Limited 2

Analysis of delay factors on drilling projects on PT China O...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Sardjono, W. Perdana, W.G. Ansori, R. Utama, D.N. Information Systems Management Department BINUS Graduate Program-Master of Information Systems Management BINA NUSANTARA University Jakarta11480 Indonesia Post Graduate Program School of Environmental Science University of Indonesia Jakarta Indonesia Graduate Program Master of Business Administration Faculty of Economic and Business University of Gadjahmada Yogyakarta Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

In carrying out drilling projects at PT China Oilfields Services Limited ( COSL) Indo, especially Project 1 to Project 4, there were a mismatch between the initial plan of the project and the actualisation in the field because there were inhibiting factors in implementing the project. The purpose of the research was to look the factors that caused for the additional time in well drilling project at PT. COSL Indo, to build a relationship model of these factors, and formulate a strategy for the company to be able to overcome the occurrence of additional time in the future project. Data analysis was conducted by using a factor analysis method and the location of the research was carried out in just one company with 102 valid respondents. The results of the research indicate that there are four factors that influence the time gap, namely Project Management Activities, Risk Analysis and Procurement, Manage Stairs, and Project Planning Development. By knowing the causes of the delay, companies can find the best solutions for future learning and the impact on environmental, social and economic problems can also be anticipated so that sustainable development occurs in the context of petroleum management. © Published under licence by IOP Publishing Ltd.

关键词： Risk analysis

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Recent Trends for Text Summarization in Scientific Documents

Recent Trends for Text Summarization in Scientific Documents

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Arief Agus Sukmandhani Yulyani Arifin Muhammad Zarlis Widodo Budiharto Computer Science Department Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Text summarization is a natural language processing (NLP) technique in artificial intelligence that has been studied in recent years. Every document containing text is tested to get a good summary result. In producing a good summary, proper accuracy is required; therefore, various techniques and methods are used, and another challenge is the use of language in the documents being tested. Various types of documents have been used as research for text summarization, including scientific documents, so researchers are interested in studying them more intensely to get the state of the art in research. This research was conducted to review previous research using literature studies from various publications that published research on text summarization in scientific documents. The researcher collected data from several publishers who had published this type of research from 1988 to 2023 quarter 1 and obtained three hundred and eleven articles, then the researcher analyzed the data using the Kitchenham method to get the right literature to answer the research question. Thirty-seven articles were selected for further in-depth analysis. In addition, the researcher found some interesting findings beyond the research question, including other techniques used to solve the text summarization problem and other types of output that are not commonly produced so that it can increase knowledge in researching text summarization.

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An Augmented Reality Game's User Experience and Its Effect on Immersion Leading to Intention

An Augmented Reality Game's User Experience and Its Effect o...

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International Conference on ICT For Smart Society (ICISS)

作者： Tanty Oktavia Kawang Utomo Ford Lumban Gaol Takaaki Hosoda Information Systems Management Department BINUS Graduate Program - Master of Information System Management Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Decision Science Department Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Since the emergence of Pokémon Go in 2016, the world has been introduced to Augmented Reality (AR) games. Many IT companies have begun developing augmented reality (AR) games due to the great commercial potential in this industry. Certain products may have managed to remain viable in the market, while others were forced to close due to one or two factors. Since the emergence of Pokémon Go, no other game has managed to surpass its unprecedented success. The company responsible for developing Pokémon Go has decided to shut down one of their games due to a lack of consumer interest. The technology of augmented reality (AR) is consistently associated with the concept of immersion. Immersion is a key aspect of the game that allows users to deeply engage and feel fully involved in the experience. This study will employ a quantitative methodology, utilising a questionnaire that will be distributed to and completed by those who have engaged in augmented reality (AR) games. The data will be analysed using Smart PLS to examine the impact of user experience on immersion, which in turn influences intention. After gathering over 200 participants by spreading google form questionnaires and doing data analysis, the findings indicate that only a few aspects of user experience, namely Brand Experience and User Need Experience, have an impact on reasons. Niantic, the developer of Pokémon Go, was unable to replicate their success with a similar augmented reality game called Harry Potter: Wizards Unite. The initial release occurred on June 21, 2019, and the shutdown took place on December 21, 2021. In the beginning, they shown a profound enthusiasm for the game but were unable to sustain it, much as Pokémon Go. According to Niantic CEO John Hanke, the primary reason for the game's shutdown was its immersion. Immersion has a notable impact on User Confirmation, which in turn has a notable impact on Satisfaction, and Satisfaction has a notable impact on Intention. Moreover, there have be

关键词： Surveys Industries Data analysis Games Companies User experience Software Hardware Internet Augmented reality

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Tourism recommender system using hybrid multi-criteria approach 2

Tourism recommender system using hybrid multi-criteria appro...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Maru'Ao, M.S.P. Suharjito Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia

Traveling has become a part of life at the time of the current urbanization. The growth rate of the internet and the availability of information allows travellers to access tourist information easier and faster. However, the individual's ability to find the information that they want is not proportional to the complexity of the information. This research would like to develop a reliable tourism recommender system that is able to provide destination recommendations according to user preferences with a combination of several methods i.e., Content-based, Collaborative Filtering, Multi-Criteria ratings, Demographic and Ontology-based. This research aims to implement a recommendations system for tourism through mobile devices by using the Android operating system. The results of this research indicate that the Hybrid Multi-Criteria method is able to provide recommendations to the user in accordance with the user's personal preferences and history of previous user rating with the average precision is 0.7. Evaluation metric of each methods indicate the lowest RMSE 0.84 in Multi-Criteria. © Published under licence by IOP Publishing Ltd.

关键词： Recommender systems

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Smart village design using enterprise architecture framework model 6

Smart village design using enterprise architecture framework...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Faisal Suharjito Bina Nusantara University BINUS Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Online Learning Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

This paper describes the design of smart village that can be describe as an indicator for the smart village in Indonesia, where the Indonesian population is now more than 250 million peoples. with several islands as much as 17, 504 islands, consisting of 53.3% were in urban areas, the rest spread in rural areas, but it will turn around if there is no solution currently to address the disparity in the village in terms of infrastructure and technology. One of the concepts can be applied to overcome these problems is "Smart Village". Conducting a study of four villages on the island of Java in Indonesia, which was awarded the village of Information Technology, the study aims to research the indicators that make the village as the village of Information Technology. By using the TOGAF Enterprise Architecture analysis, the result of study are the components that exist in the ICT village winners and what the other components needs to complement the design of smart village which can be a reference in Indonesia in future. © 2021 IEEE.

关键词： Rural areas

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Comparison of Gaussian Hidden Markov Model and Convolutional Neural Network in Sign Language Recognition System 1

Comparison of Gaussian Hidden Markov Model and Convolutional...

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1st International Conference on computer science and Artificial Intelligence, ICCSAI 2021

作者： Gunawan, Herman Suharjito Pratama, Devriady Bina Nusantara University Binus Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Online Learning Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Sign language Recognition is the study to help bridging communication of deaf-mute people. Sign Language Recognition uses techniques to convert gestures of sign language into words or alphabet. In Indonesia, there are two types of sign languages which are used, Bahasa Isyarat Indonesia (BISINDO) and Sistem Isyarat Bahasa Indonesia (SIBI). The purpose of this research is comparing sign language recognition methods between Gaussian Hidden Markov Model and Convolutional Neural Network using indonesian sign language SIBI as a dataset. The dataset comes from 200 videos from 2 signers. Each signer performs 10 signs with 10 repetitions. To improve the recognition accuracy, modified histogram equalization is used as an image enhancement. Skin detection was used to track the movement of the gesture as input features in the Gaussian Hidden Markov Model and fine tuning was used in Convolutional Neural Network using transfer learning, freeze layer, and dropout. The results of the research are the Gaussian Hidden Markov Model provides accuracy value of 84.6% and Convolutional Neural Network provides accuracy value of 82%. © 2021 IEEE.

关键词： Hidden Markov models

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An Improved Vision Transformer Architecture-Based computer Recognition Platform for Lilium Classification

An Improved Vision Transformer Architecture-Based Computer R...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Huang, Xiang-Rui Ke, Huai-Jhan Kuo, Szu-Yu Chen, Liang-Bi Master Program in Electrical Engineering and Computer Science Department of Electrical Engineering National Penghu University of Science and Technology Penghu Taiwan Department of Shipping and Transportation Management National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan

ISBN: (纸本)9798331521165

Lilies are popular in the global flower market, but consumers often lack information about specific varieties. To address this issue, this paper proposes a computer recognition platform based on the Vision Transformer (ViT) architecture. The proposed platform uses an improved vision transformer (ViT) architecture to classify different types of lilies, allowing consumers to access information and names of various Lilium species. The experimental results show that the proposed lily classification model achieved a 96.4% accuracy rate in classifying six lily species. © 2025 IEEE.

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Low-Cost Air Box Application for Indoor Environment Classification and LSTM Microclimate Prediction 6

Low-Cost Air Box Application for Indoor Environment Classifi...

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6th International Conference on Control and Robotics, ICCR 2024

作者： Hsun-Jui, Hu Yu-Hsien, Chiu Pei-Ching, Yang Chang Jung Christian University Master Degree Program in Information and Design Tainan Taiwan Kaohsiung Medical University Department of Healthcare Administration and Medical Informatics Kaohsiung Taiwan Artificial Intelligence Research Center Chang Jung Christian University Tainan Taiwan Chang Jung Christian University Department of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798331518158

Air pollution is a significant issue because it burdens human health and the environment. Particulate matter with a diameter of 2.5 micrometers or smaller (PM2.5) can penetrate the lungs and enter the bloodstream, seriously affecting the cardiovascular system, causing strokes, and impacting the respiratory system. Modern individuals spend 70% to 90% of their time indoors, making indoor air quality even more important than outdoor air quality. To estimate and predict indoor air quality, this study utilizes low-cost, self-assembled air boxes based on the Internet of Things to collect various air quality data. The collected data is then used in an indoor air quality forecasting system that combines Long Short-Term Memory (LSTM) and K-means clustering methods to compare the overall performance of the algorithms. The experimental results are analyzed and statistically described based on each model method prediction accuracy and clustering results. These methods are integrated to develop an indoor air quality forecasting system to help indoor populations prevent issues in advance or optimize energy usage. © 2024 IEEE.

关键词： Indoor air pollution

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