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

作者： Yulianton, H. Warnars, H.L.H.S. Soewito, B. Gaol, F.L. Abdurachman, E. Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Faculty of Information Technology Universitas Stikubank Semarang50243 Indonesia

The web continues to grow and attacks against the web continue to increase. This paper focuses on the literature review on scanning web vulnerabilities and solutions to mitigate web attacks. Vulnerability scanning methods will be reviewed as well as frameworks for improving web security. This research is the basis for future work that will end with the elaboration of web scanning and security with the aim of proposing better innovations. © Published under licence by IOP Publishing Ltd.

关键词： Scanning

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The Technology Behind Genomic Database

The Technology Behind Genomic Database

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Joko Pebrianto Trinugroho Anzaludin Samsinga Perbangsa Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 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

ISBN: (纸本)9781665453967

In this digital era, we are exposed to a large amount of data. This includes biological data, which stores information about living organisms, including Deoxyribonucleic acid (DNA), genes, and proteins. With the development of information technology and information system, most of available biological data are stored in an online public database. Many of the databases are free-access and easily used, which helps the users, especially researchers, to make use of the data. Among the known public biological databases are the University of California Santa Cruz (UCSC) Genome Browser Database and the Rat Genome Database (RGD). These two databases provide access to the biological data from different organisms. This paper aims to describe the technology of public biological databases. Also elucidated in this paper are the differences features between UCSC Genome Browser Database and the RGD. Our results showed that the UCSC contains much more biological data and features than the RGD. However, the genome browser of UCSC has a complex display, while the RGD has a simple display. Overall, both databases give the users the option to choose the most suitable database for them.

关键词： Proteins Databases Genomics DNA Rats Biology Organisms

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Customer churn prediction in telecommunication company

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AIP Conference Proceedings 2023年第1期2510卷

作者： Dota Biomantoro Gede Putra Kusuma Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Churn prediction methods are widely used to anticipate customer churn from services provided by a company for some reasons. This study aims to develop an optimal churn prediction model based on customer data from a telecommunication company in Indonesia. The model development and evaluation processes are performed by following the Cross-Industry Standard Process for Data Mining (CRISP-DM), which consist of business understanding, data understanding, data preparation, modelling, and evaluation. Various combination of data preparation and modelling methods have been evaluated. The evaluation results show that the combination of feature selection and prediction model yields better results compared to prediction model without feature selection. The highest accuracy is achieved by Random Forrest at 97.82%, which is followed by Decision Tree at 97.06%, and Naive Bayes at 90.62%. This result indicates that a prediction model can be reliably used to predict customer churn in a telecommunication company.

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Drones computer Vision using Deep Learning to Support Fishing Management in Indonesia 3

Drones Computer Vision using Deep Learning to Support Fishin...

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3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Liawatimena, Suryadiputra Atmadja, Wiedjaja Abbas, Bahtiar Saleh Trisetyarso, Agung Wibowo, Antoni Barlian, Erland Hardanto, Lilik Tri Triany, Natassya Afdalena Faisal Sulistiawan, Jaka Yojana, Albert Cahya Computer Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Doctor Computer Science Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Management Department BINUS Business School - Doctor of Research in Management Bina Nusantara University Jakarta11480 Indonesia

It is necessary to study technical factors such as bait used, oceanographic conditions of fishing areas and skipjack tuna trade patterns (Katsuwonus pelamis) as well as other factors in Sulawesi Fisheries. Supporting data to study these technical factors need to be obtained so that it can be improved throughout the year as a skipjack fishing standard with weights taken by the skipjack weights. Therefore, drones and underwater drones with cameras with high-resolution video and images to be fed and processed by computer vision. These drones are operated via Huhate ships which are then used to test efficient technical factors as expected, can be used in determining the size, shape, and colour as well as the analysis of skipjack fish that are in the Fish Aggregating Devices (FADs or Rumpon) in order to manage the management of fisheries managed in Indonesia. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Drones

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Journalist system using named entity recognition based on Twitter location extraction

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AIP Conference Proceedings 2023年第1期2691卷

作者： Abba Suganda Girsang Sani Muhamad Isa Raditya Fajar BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Interactions that occur on Twitter social media are easier to do and can easily reach all levels of society, as well as conversations tweeted by users. They can easily spread information or issues that are developing. Based on this, the use of conversational data on the Twitter platform can get an overview of issues that are developing and even those that are just being formed from community conversations on this Twitter platform. Still related to previous research, which researched to implement the Twitter social media monitoring system to provide access to additional information for journalists. This study aligns with its predecessors but comes with a richer data collection mechanism, so it can gather more conversations. The difficulty to be answered in this study is the difficulty of journalist determining a bunch of location of an incident on a Twitter conversation, which describes the name of a place or contains the address of a location in it. Also in this study, there are additional modifications to extract location names using the NER model, which has been customized according to the structure of naming location names in Indonesia. So from the text of the Twitter conversation, whether it's an address name or a description of a location name, it can get a location name that refers to the name of a place where it could be the location where an event occurred. The results of an Indonesian language location NER model, with an accuracy score of 97.67%, with a precision value of 90.57%, recall 59.30% and f1-score 71.67%.

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Sentiment Analysis of Indonesian Government Policies In Handling Covid 19 Through Twitter Data

Sentiment Analysis of Indonesian Government Policies In Hand...

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

作者： Bagus Sujiwo Antoni Wibowo Dewi Retno Sari Saputro BINUS Graduate Program - Master of Computer Science Bina Nusantara University Indonesia Program Studi Matematika FMIPA UNS Universitas Sebelas Maret Indonesia

ISBN: (纸本)9781665401524

This paper presented a sentiment analysis of the Indonesian government's policies in overcoming Covid 19 through twitter data using several classification methods, namely SVM, Naive Bayes, and LSTM. Based on the analysis of the twitter data, it was found that the twitter community in Indonesia gave negative sentiments to government policies in handling Covid 19. From the experimental results, it was found that SVM gave the best sentiment results compared to Naïve Bayes and LSTM by providing an accuracy of 88.5%.

关键词： COVID-19 Government policies Support vector machines Seminars Sentiment analysis Social networking (online) Blogs

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Sarcasm detection in twitter - Performance impact while using data augmentation: Word embeddings

arXiv

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arXiv 2021年

作者： Handoyo, Alif Tri Hidayaturrahman Suhartono, Derwin Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Sarcasm is the use of words usually used to either mock or annoy someone, or for humorous purposes. Sarcasm is largely used in social networks and microblogging websites, where people mock or censure in a way that makes it difficult even for humans to tell if what is said is what is meant. Failure to identify sarcastic utterances in Natural Language Processing applications such as sentiment analysis and opinion mining will confuse classification algorithms and generate false results. Several studies on sarcasm detection have utilized different learning algorithms. However, most of these learning models have always focused on the contents of expression only, leaving the contextual information in isolation. As a result, they failed to capture the contextual information in the sarcastic expression. Moreover, some datasets used in several studies have an unbalanced dataset which impacting the model result. In this paper, we propose a contextual model for sarcasm identification in twitter using RoBERTa, and augmenting the dataset by applying Global Vector representation (GloVe) for the construction of word embedding and context learning to generate more data and balancing the dataset. The effectiveness of this technique is tested with various datasets and data augmentation settings. In particular, we achieve performance gain by 3.2% in the iSarcasm dataset when using data augmentation to increase 20% of data labeled as sarcastic, resulting F-score of 40.4% compared to 37.2% without data augmentation. © 2021, CC BY.

关键词： Social networking (online)

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Systematic Evaluation of Cross Population Polygenic Risk Score on Colorectal Cancer

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Procedia computer science 2021年 179卷 344-351页

作者： Bharuno Mahesworo Arif Budiarto Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The number of findings in cancer genomics research has grown rapidly in the last decade due to the decline in the cost of human sequencing and genotyping. However, the majority of the reported significant marker associated with cancer traits are based on European and East Asian population. Large population such as South Asian and South-East Asian population are under-represented in genomics research. In this study, we explored the possibility of computing a Polygenic Risk Score (PRS) of colorectal cancer on our test sample based on reported significant Single Nucleotide Polymorphism (SNP). The SNPs used to compute the risk score were collected from GWAS Central and GWAS Catalog. Significant SNPs from IC3 study were used as a benchmark. The result shows that calculating colorectal cancer risk score using reported significant marker from different population group is possible. The p-value of our statistic model shows significant differences between case and control group risk score.

关键词： Polygenic Risk Score Cross Population Colorectal Cancer

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Convolutional Neural Networks for Scops Owl Sound Classification

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Procedia computer science 2021年 179卷 81-87页

作者： Alam Ahmad Hidayat Tjeng Wawan Cenggoro Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Adopting a deep learning model into bird sound classification tasks becomes a common practice in order to construct a robust automated bird sound detection system. In this paper, we employ a four-layer Convolutional Neural Network (CNN) formulated to classify different species of Indonesia scops owls based on their vocal sounds. Two widely used representations of an acoustic signal: log-scaled mel-spectrogram and Mel Frequency Cepstral Coefficient (MFCC) are extracted from each sound file and fed into the network separately to compare the model performance with different inputs. A more complex CNN that can simultaneously process the two extracted acoustic representations is proposed to provide a direct comparison with the baseline model. The dual-input network is the well-performing model in our experiment that achieves 97.55% Mean Average Precision (MAP). Meanwhile, the baseline model achieves a MAP score of 94.36% for the mel-spectrogram input and 96.08% for the MFCC input.

关键词： acoustic features bird sound classification convolutional neural network mean average precision scops owl

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A Convolutional Neural Network-based Ancient Sundanese Character Classifier with Data Augmentation

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Procedia computer science 2021年 179卷 195-201页

作者： Alam Ahmad Hidayat Kartika Purwandari Tjeng Wawan Cenggoro Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

With an increasing interest in the digitization effort of ancient manuscripts, ancient character recognition becomes one of the most important areas in the automated document image analysis. In this regard, we propose a Convolutional Neural Network (CNN)-based classifier to recognize the ancient Sundanese characters obtained from a digital collection of Southeast Asian palm leaf manuscripts. In this work, we utilize two different preprocessing techniques for the dataset. The first technique involves the use of geometric transformations, noise background addition, and brightness adjustment to augment the imbalanced samples to be fed into the classifier. The second technique makes use of the Otsu’s threshold method to binarize the characters and only uses the usual geometric transformations for the data augmentation. The proposed network with different data augmentation processes is trained on the training set and tested on the testing set. Image binarization from the second technique can outperform the performance of the CNN-based classifier upon the first technique by achieving a testing accuracy of 97.74%.

关键词： ancient sundanese characters convolutional neural network data augmentation document image analysis Otsu’s threshold method

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