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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Maulana, Fairuz Iqbal Pramono, Andi Hamim, Miftahul Prihatin, Sukeipah Yuli Arifuddin, Rahman School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Design Bina Nusantara University Interior Design Department Jakarta11480 Indonesia Binus Business School Bina Nusantara University Entrepreneurship Business Creation Jakarta11480 Indonesia Politeknik Kota Malang Informatics Study Program Malang Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

Artificial Intelligence Research in Agriculture has grown so common, and the potential they provide is so revolutionary that it is considered essential for competitive growth. Until we conducted this research, only 1064 academic documents on Trends of Artificial Intelligence Research in Agriculture that we found from 2012 to 2021 were obtained by searching the Scopus database. This study uses bibliometric analysis methods to map scientific publications worldwide. We use an article selection process, the year limitation, then the database is exported to RIS and CSV format files. Researchers in the United States had the most published articles and were indexed by Scopus among the most prolific authors ($\mathrm{N}=287$), followed by the United Kingdom at second ($\mathrm{N}=139$) and Italy with 66 academic publications. Data analysis reveals an increasing trend in the number of Trends of Artificial Intelligence Research in Agriculture publications worldwide, as measured by the Scopus index. © 2022 IEEE.

关键词： Agriculture

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Autonomous car using CNN deep learning algorithm 2

Autonomous car using CNN deep learning algorithm

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2nd Annual Conference of science and Technology, ANCOSET 2020

作者： Sonata, I. Heryadi, Y. Lukas, L. Wibowo, A. Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Jl. Kyai H. Syahdan No.9 Jakarta11480 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jl Jend. Sudirman No.51 Jakarta12930 Indonesia

Autonomous cars have become an interesting discussion in recent years. Several major automotive industries such as Tesla, GM and Nissan are trying to become pioneers in autonomous car technology. Giant technology companies such as Google Waymo, Baidu and Aptiv also developing autonomous car technology. Several technological approaches are carried out in implementing autonomous cars for recognizing surrounding situations, such as radar, lidar, sonar, GPS, and odometry. An automatic control system is used to control navigation based on the data obtained from these sensors. This paper will discuss the use of CNN deep learning algorithm for recognizing the surrounding environment in creating the automatic navigation required by autonomous cars. The system designed will create and learn the data set that will be taken in advance and the learning outcomes will be implemented in an open simulation system. This simulation shows high accuracy in learning to navigate the autonomous car by observing the surrounding environment. © Published under licence by IOP Publishing Ltd.

关键词： Autonomous vehicles

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Fundamental Components of Microlearning for Sustainable Quality Education: A Systematic Literature Review

Fundamental Components of Microlearning for Sustainable Qual...

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International Conference on Information Management and Technology (ICIMTech)

作者： Reza Rahutomo Siti Elda Hiererra Yulyani Arifin Muhamad Nanang Suprayogi Bens Pardamean Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Psychology Department Faculty of Humanities Bina Nusantara University Jakarta Indonesia

E-learning was disrupted by a new concept of education named microlearning that carried various digital learning materials in convenient learning paths that were customizable according to users' necessity by using digital application. The competitive advantage of microlearning has been published but caused ubiquitous bias definition. This study aims to identify and analyze fundamental components of existing microlearning research and implementations in e-learning platforms. 42 research articles from SCOPUS database were collected from 2018 to 2022. They were reviewed to discover fundamental components of microlearning that must be considered in its implementations. The result of this study uncovered “Content's variety” the most fundamental components. In contrast, this study finds that advance technology is the least discussed but required in supporting specific usage of microlearning. Limitation of this study lies on the determination of database and keyword usage and the methodology. This study recommends to consider combination of microlearning components to concept a specialized microlearning for sustainable quality education.

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Sentiment Analysis of Big Cities on The Island of Java in Indonesia from Twitter Data as A Recommender System

Sentiment Analysis of Big Cities on The Island of Java in In...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Boby Siswanto Ford Lumban Gaol Benefano Soewito Harco Leslie Hendric Spits Warnars BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665427340

Text mining is a data mining technique to find hidden things from a set of data in the form of text. One of the things that can be obtained with text mining is opinion or sentiment, whether it is positive or negative. Positive sentiment is used as a reference for a subject or object from a collection of texts to be recommended. Java Island is the city with the most population in Indonesia with a variety of culinary delights. This study aims to analyze sentiment recommendations on culinary data from food or cuisine in big cities on the island of Java. Four big cities were selected, namely Jakarta, Bandung, Yogyakarta, and Surabaya. The data source is a tweet of culinary arts of the four cities from Twitter in Indonesia. The Sastrawi Library is used as a text mining data processing tool in Indonesia. The results obtained are the majority of positive sentiments from all cities. The cumulative sentiment value of the four cities is 54%, meaning that the big cities on the island of Java have good culinary delights and deserve to be recommended to be enjoyed.

关键词： Text mining Java Sentiment analysis Art Social networking (online) Soft sensors Urban areas

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Automatic Smart Crawling on Twitter for Weather Information in Indonesia

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Procedia computer science 2023年 227卷 795-804页

作者： Kartika Purwandari Reza Bayu Perdana Join W.C. Sigalingging Reza Rahutomo Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta 10720 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Although Twitter is a popular platform for social interaction analysis and text data mining, it faces challenges with geolocation automation. To address this problem, the researchers propose the utilization of a Support Vector Machine (SVM) model to develop an automated Twitter crawling system. The system aims to collect data related to weather in Indonesia by employing Twin, a Python-based Twitter scraping software. To overcome null geolocations, the study incorporates aliases created based on the common practice of Indonesian users mentioning the country's location in tweets. The results demonstrate that the SVM model, combined with automated smart crawling, achieves an 85% accuracy rate.

关键词： Social media Twitter SVM smart crawling weather

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A Literature Review: Drug Supply Chain and Neural Networks 6

A Literature Review: Drug Supply Chain and Neural Networks

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6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

作者： Emmanuel, Gunadi Meyliana Hidayanto, Achmad Nizar Prabowo, Harjanto Bina Nusantara University Computer Science Jakarta Indonesia Fakultas Sains Dan Teknologi Unika Katolik Musi Charitas Palembang Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia Management Departement BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350399615

The drug supply chain is an important variable that will be asked when there is a shortage of drugs. The distribution and storage of drugs require accurate predictions to support the drug supply chain. Many methods have been studied to obtain a high level of prediction accuracy. One of the methods used is an artificial neural network to recognize the pattern of drug demand. It is important to find breakthroughs in artificial neural network technology to anticipate drug shortages. This study seeks to identify and find challenges from previous research in drug supply management and drug demand prediction by utilizing neural networks over the past five years. From the seven sources used, 34 articles were selected based on inclusion criteria and keyword searches according to Kitchenham's systematic review methodology. The discussion areas of the selected articles are 29% in the Logistics and Inventory area, 20% Risk - Resilience, and 18% for Drug supply chain planning which is a description of the research development area. Integrated development provides room for the use of neural networks in these three correlated areas. The model for implementing neural networks in maintaining the continuity of drug shortages is always associated with policies adopted by all drug supply chain stakeholders to anticipate drug shortages. © 2022 IEEE.

关键词： artificial neural network drugs supply chains pharmaceutical

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Text-based Depression Detection on Social Media Posts: A Systematic Literature Review

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Procedia computer science 2021年 179卷 582-589页

作者： David William Derwin Suhartono Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Due to the huge increase of awareness of mental health well-being, the detection of mental illness itself is starting to become a huge concern. Many psychiatrists found difficulties in identifying the existence of mental illness in a patient because of the complicated nature of each mental disorder, thus making it hard to give the appropriate treatment to the patient before it’s too late. However, due to the integration of social media into people’s daily life, this create an environment that may provide additional information regarding the mental disorder a patient bear. This study has been undertaken as a Systematic Literature Review (SLR), which is defined as a process of identifying, assessing, and interpreting the available resources to provide answers for a set of research questions. Analysis is made to answer questions regarding text-based mental illness detection based on the social media activity of people with mental disorders, and reveals that it indeed is possible to do early detection of depression on social due to the existence of a particular characteristics in the way these subjects use their social media. This SLR found that from the small amount of research using text-based approach, most studies use deep learning models such as RNN on the early detection of depression cases due to the limitation of data availability. However, this study will look to find method that may prove to be more effective.

关键词： Linguistic analysis Natural language processing Depression detection Social media

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Data warehouse for analysing music sales on a digital media store 2

Data warehouse for analysing music sales on a digital media ...

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2nd International Conference on computer, science, Engineering, and Technology, ICComSET 2019

作者： Ranti, Kiefer Stefano Tuapattinaya, Deyanara Chang, Calvin Girsang, Abba Suganda Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

Nowadays, every company knows that when making a decision that has a potential in affecting their assets, an accurately processed report is necessary in order to support the reasoning behind their decision. Generating a report for stakeholders quickly and accurately is highly required in assisting them making a data-driven decision. By developing a data warehouse, it is possible for a company to do a data-driven decision making to appeal to their customer segments. This paper proposes a data warehouse model design to analyse the sales data contained in the database. The method that was implemented for this particular data warehouse development is the nine-step methodology designed by Kimball. The results are then presented in pdf form and an interactive dashboard. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Data warehouses

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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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Aspect-based Sentiment Analysis in Tourism Industry for Tourism Recommender System

Aspect-based Sentiment Analysis in Tourism Industry for Tour...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Choirul Huda Yaya Heryadi Lukas Widodo Budiharto Computer Science Department Doctor of Computer Science Graduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia Computer Engineering Research Group (CERG) Atma Jaya University Jakarta Indonesia

The improvement of some aspects in tourism industry needs further study through aspect-based sentiment analysis based on tourist experience. The aim of this study is presenting the empiric results of aspect-based sentiment analysis to extract some useful aspects of services for leveraging tourism industry development based on user's feedback. The study uses customer review data from TripAdvisor website for developing classification model using machine learning algorithms including Decision Tree (DT), Random Forest Classifier (RFC), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) models. Each machine learning model under study is trained using K-Fold Cross Validation. The empiric results found that accuracy of RFC achieved the highest result (99.5%) followed by XGBoost (87.3%), DT (82.9%), and SVM (80.5%). The extracted meaningful aspects from customer feedback give many valuable insights for service quality improvement in tourism industry in the effort to strengthening the rising-up of sustainability economic growth through tourism industry.

关键词： Support vector machines Sentiment analysis Machine learning algorithms Tourism industry Boosting Data models Tokenization

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