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Strategic Planning and Knowledge Management Approaches as input to remote area strategic plans: A Case study in Aru Islands Regency Maluku Province 8

Strategic Planning and Knowledge Management Approaches as in...

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Irena Gutandjala, Ilona Harso Supangkat, Suhono Lumban Gaol, Ford Rianti, Benny Bina Nusantara University BINUS Graduate Program-Computer Science Computer Science Department Jakarta11480 Indonesia Bandung Institute of Technology Smart City and Community Innovation Center School of Electrical and Informatics Engineering Bandung Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781665416979

Knowledge is an important asset in an organization. Aru Islands District is one of the districts in Maluku Province. The Government of Aru Islands District Maluku has a vision and mission as outlined in the Regional Strategic Plan or commonly referred to as RENSTRA. This vision and mission are an activity that requires a policy direction that is planned and integrated with the strategic environment of all related aspects. The strategic plan is then realized with the implementation of the government with regional autonomy which is the modern governance in realizing quality public services, fast, easy, fair and unlimited for the welfare and progress of the region. In this research will be used a strategic approach as input from the analysis with knowledge management and assessment of the condition of Aru district. © 2021 IEEE.

关键词： Strategic planning

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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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Information system design on tobacco addiction measurement protocol for smokers 6

Information system design on tobacco addiction measurement p...

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

作者： Mahesworo, Bharuno Budiarto, Arif Parung, Deby Erina Amirulloh, Dian Simon, Simon Pardamean, Bens Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Genetics Indonesia Product Department Jakarta Indonesia Genetics Indonesia Laboratory Department Jakarta Indonesia Board of Director Genetics Indonesia Jakarta Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Smoking habits in Indonesia has become a national health burden despite their huge contribution to national economics. With easy access to inexpensive cigarettes in Indonesia, the percentage of teenage smokers in Indonesia is very high. More than 70% of these smokers have tried to quit, but only approximately 5% have succeeded. These facts show the importance of developing precision stop smoking treatments. We proposed an information system design of smoking measurement questionnaire that is able to describe the characteristic differences between smokers and former smokers. This information system design was implemented in genetic service company's web-based application and is expected to help us extract the necessary variables to help us finding biomarkers associated with smoking cessation in our future study. The information system design has been developed by combining and modifying questionnaires from previous studies. The focus is on strategic variables and the compactness of the questionnaire, and it can be used by other researchers to describe the smoking dependency of their samples. The information system design has been tested on pilot respondents/customers and shows results that meet our expectations. © 2021 IEEE.

关键词： Tobacco

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Strengthening IT Governance in the Crypto Marketplace: Leveraging Penetration Testing and Standards Alignment

Strengthening IT Governance in the Crypto Marketplace: Lever...

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

作者： Semi Yulianto Erly Krisnanik Muhammad Syarif Hartawan Computer Science Department Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information System Department University of Pembangunan Nasional Veteran Jakarta Indonesia Faculty of Technology University of Krisnadwipayana Jakarta Indonesia ICT Department Asia e University Malaysia

This research explores the vital role of penetration testing in cybersecurity, specifically its alignment with ISO 27001:2022, COBIT 2019, and NIST CSF standards in the context of crypto asset exchange management. These sectors grapple with rapidly evolving threat landscapes, necessitating robust cybersecurity strategies. However, the integration of penetration testing with these standards remains underexamined. Through a comprehensive approach, including literature reviews, surveys, case studies, and interviews, this study investigates the incorporation of penetration testing into an organization's cybersecurity. It unveils the current state of penetration testing practices, interprets ISO 27001:2022, COBIT 2019, and NIST CSF perspectives, and pinpoints integration challenges and opportunities. This study offers practical insights and best practices for integrating penetration testing into cybersecurity frameworks by emphasizing alignment. It underscores the importance of aligning penetration testing with information security standards in the crypto asset exchange management sector, enhancing security, proactively identifying vulnerabilities, and effectively mitigating risks in the context of growing crypto asset importance. This research provides invaluable guidance for safeguarding digital assets in this evolving domain.

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The Impact of Online Learning System Collaborator on Students' Ability 8

The Impact of Online Learning System Collaborator on Student...

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Adrian, Steven Tandy, Jovan Ardiyansyah, Sultan Adhitya Evangeline, Gabriella Dendy Oktavia, Tanty Gaol, Ford Lumban Hosoda, Takaaki Bina Nusantara University School of Information System Information Systems Department Jakarta Barat Indonesia Bina Nusantara University Binus Graduate Program-Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan

ISBN: (纸本)9781665416979

Pandemic Covid-19 has change learning process to use online learning platform to limit interaction between participant of learning and reduce the Covid-19. This shifting has a major impact on education. Even though, many things have been done by education institution, lecturers, and students in adapting to online learning. In this study focus to check the effect of online learning on students' ability to understand the learning process. In this research, we use quantitative approach. To collect the data, this study uses a questionnaire method and in data analysis this study uses descriptive methods. In the results of this research can conclude that there is a positive influence from online learning on student's ability to understand the material. Therefore, higher education institution have to consider online learning to be good so that in the future it can continue to be developed or upgrade so that it can make it easier for students to understand the material and learn effectively and efficiently. © 2021 IEEE.

关键词： Students

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Genome data mining approach to identify potential protein in crop plants

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AIP Conference Proceedings 2024年第1期3026卷

作者： Joko Pebrianto Trinugroho Faisal Asadi Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

There is a need to produce more crop plants to meet the future global demand. However, the climate change has affected the global crop yield. Consequently, finding an alternative approach to improve crop yield becomes essential. The development of sequencing techniques, as well as information technologies, have enabled us to perform genome data mining. Using genome data mining approach, it is possible to identify or discover a protein which has a particular characteristic. This study aims to identify a protein, which could potentially improve crop yield, using genome data mining approach. D1 protein was used as the target, as this protein is highly involved in photosynthesis. Then, protein sequences of various crop plants were collected from biological database. After conducting data trimming and filtering, sequence analysis was performed. The analysis was used to construct phylogenetic tree and create a 3D protein model. Sequence analysis displayed variation in amino acid sequence in D1 protein. Protein modelling located the variations, which scattered within D1 protein. Furthermore, we highlighted the amino acid residues that are the targets for genetic engineering. The research findings may provide a reference to improve crop production through genome mining approach.

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Predicting Human Activity with LSTM Face Detection on Server Surveillance System

Predicting Human Activity with LSTM Face Detection on Server...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Alexander Nurenie Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Indonesia

Surveillance server technology was growth with new technology, effective, extra new features, human friendly, and human deals with big amount data, can't view and collect the data in the short time, and took time to analyze, playback video/picture to determine machine, human, vehicle or environment issues or performance, Surveillance Server Systems now which has the ability to face recognition, face detection, human detection, motion detection, license plate recognition, The authors perform this study that still new this research has never been done before to determine the efficacy of the LSTM in predicting human behavior (Long Short Term Memory) Face Detection on Server surveillance system, by taking log view data with a total of 91501 Face detection data downloaded from 10/18/2022~11/9/2022, the data will be processed using Python programming and training so that it can be used to predict the future regarding human activities that vary utilizing time series prediction LSTM include the number of daily activities, the highest and lowest numbers of days, and the maximum and minimum numbers of days. from the results of this study it was found to help to find out the days with the lowest number of humans and the days with the highest number of human activities, so that the owner can predict with sequence of the data the service would be provided when human activity is high in certain area or certain day, it can also can find out the maximum or minimum amount human counting day by day, and compare able some different date and location, the author will continue to do more in-depth research the others data related with prediction with deep learning server surveillance machine system interaction with human, vehicle behavior in the future studies.

关键词： Training Surveillance Time series analysis programming Behavioral sciences Servers Face detection

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Database management system design for COVID-19 symptom analysis 6

Database management system design for COVID-19 symptom analy...

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

作者： Rahutomo, Reza Darmalim, Ferdinan Cen, Andrew Brilianto, Jayaku Pardamean, Bens School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia PT. Mega Medica Pharmaceuticals IT Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Faculty of Technology and Design Bunda Mulia University Jakarta Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Applying a digital strategy is proven to be one way to reduce the infection rate of COVID-19. As a fundamental part of it, database is capable to create a data repository to predict uncertain impacts of the pandemic. The research proposed a database design for project C-Beta that emphasized on creating an ideal system to record and monitor the daily health condition of people with COVID-19. Mainly, the research adopted Connolly and Begg principles of Database Software Development Life Cycle. As a result, the database design activities produced blueprints in the forms of data dictionary, ERD, and user views. The database design delivered in the research included with several features namely user verification, restricted research membership, and flexibility in survey creation. Based on analysis and design phase, C-Beta provides a method to collect data for COVID-19 clinical study. Researchers who utilize C-Beta is able to manage multiple surveys and extract all collected records at the same time. © 2021 IEEE.

关键词： COVID-19

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Weight-dropped long short term memory network for stock prediction with integrated historical and textual data

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2020年第3期47卷 367-377页

作者： Thayogo Wibowo, Antoni Computer Science Department Binus Graduate Program-Master of Computer Science Bina Nusantara University Indonesia

Investors and traders need an accurate stock prediction model to help them make decisions. They can use deep learning models such as Long Short-Term Memory Network (LSTM). However, a weakness of LSTM is that it tends to overfit to the training data and have unstable results. To overcome this weakness, this paper proposes using Averaged Stochastic Gradient Descent and Weight-Dropping on an LSTM network (AWD-LSTM). The proposed model regularizes the network by weight-dropping with DropConnect and optimizes the training process using a Non-Monotically Triggered Averaged Stochastic Gradient Descent (NT-ASGD). Additionally, this paper tested with integrating historical data with textual data which was shown to be valuable by other studies. This paper evaluated six variants of the model with different regulizers, optimizers, and data. The results show that (1) DropConnect regulizer performed better than DropOut or No Drop;(2) Adam optimizer is better for stock prediction than NT-ASGD;(3) Adding textual data slightly increases performance;(4) The models were able to gain consistent profits in a market simulation;(5) A variant of the model outperformed a previous study in 4 out of 5 indexes. © International Association of Engineers.

关键词： Optimization

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Measurement of airline service sentiment analysis using vector space model

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AIP Conference Proceedings 2024年第1期3132卷

作者： Aries Maesya Harco Leslie Hendric Spits Warnars Ford Lumban Gaol Benfano Soewito Department of Computer Science BINUS Graduate Program Bina Nusantara University Jakarta Indonesia

Airlines are an organization that provides flight services for passengers or goods in Indonesia. There are many airlines available at this time giving passengers the option to choose the airline they want to use. Online reviews are one factor that is quite important in influencing consumer trust and interest in choosing an airline. On the Tripadvisor site, few or many reviews are given by consumers will have an influence on other potential customers but monitoring and organizing reviews from other consumers is not easy. There are too many reviews published from online review sites if they are processed manually. Therefore, it is necessary to use a sentiment analysis application to classify positive, negative, and neutral reviews from consumers so that it speeds up and makes it easier to review the shortcomings of each airline. Method Vector Space Model is used because it is relevant and effective in searching and categorizing text documents by using Cosine Similarity to look for similarities between the query vector and the document vector. The evaluation method used is Confusion Matrix which will calculate the accuracy precision and recall of the resulting test value. The results of the evaluation and validation carried out by this study resulted in an accuracy value of 76,42% with a value of precision of 83% and a recall of 87%.

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