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2nd Annual Conference of science and Technology, ANCOSET 2020

作者： Tho, C. Heryadi, Y. Lukas, L. Wibowo, A. Department of Computer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia

Nowadays mixing one language with another language either in spoken or written communication has become a common practice for bilingual speakers in daily conversation as well as in social media. Lexicon based approach is one of the approaches in extracting the sentiment analysis. This study is aimed to compare two lexicon models which are SentiNetWord and VADER in extracting the polarity of the code-mixed sentences in Indonesian language and Javanese language. 3,963 tweets were gathered from two accounts that provide code-mixed tweets. Pre-processing such as removing duplicates, translating to English, filter special characters, transform lower case and filter stop words were conducted on the tweets. Positive and negative word score from lexicon model was then calculated using simple mathematic formula in order to classify the polarity. By comparing with the manual labelling, the result showed that SentiNetWord perform better than VADER in negative sentiments. However, both of the lexicon model did not perform well in neutral and positive sentiments. On overall performance, VADER showed better performance than SentiNetWord. This study showed that the reason for the misclassified was that most of Indonesian language and Javanese language consist of words that were considered as positive in both Lexicon model. © Published under licence by IOP Publishing Ltd.

关键词： Sentiment analysis

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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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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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Weather Prediction in Agriculture Yields with Transformer Model

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Procedia computer science 2024年 245卷 750-758页

作者： Yulyani Arifin Ilvico Sonata Maryani Elizabeth Paskahlia Gunawan Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jl. K.H. Syahdan No.9 Kemanggisan Palmerah Jakarta 11480 Indonesia Information System Department School of Information System Bina Nusantara University Jl. K.H. Syahdan No.9 Kemanggisan Palmerah Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jl. K.H. Syahdan No.9 Kemanggisan Palmerah Jakarta 11480 Indonesia

Agriculture has a lot of relations with SDG from United Nations especially in end hungers and sustainable agriculture. One of factor important in agriculture is weather. Weather prediction is very important in agriculture. Weather greatly influences agricultural yields and the types of crops to be planted. Weather predictions, especially temperature and humidity, can be done using time series data analysis. Current developments in deep learning enable more accurate predictions of temperature and humidity based on time series dataset. Some deep learning models can be used to predict data based on training using time series data. The deep programming models used include Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). The Transformer deep learning model, which was first introduced in 2017, is a sequence-to-sequence model which is very suitable for predicting time series data. This paper will explain temperature and humidity prediction using the Transformer deep learning model. Experimental results show that the Transformer model can predict temperature and humidity based on training results using a time series dataset. The accuracy obtained by the Transformer deep learning model is better compared to the LSTM deep learning model as indicated by lower MAE, MSE and RMSE values compared to the LSTM model.

关键词： Forecast LSTM SDG Goals Time series Transformer Weather

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Semi-Automated Classification of Arabic User Requirements into Functional and Non-Functional Requirements using NLP Tools

Semi-Automated Classification of Arabic User Requirements in...

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2021 International Conference on Information Technology, ICIT 2021

作者： Shehadeh, Karmel Arman, Nabil Khamayseh, Faisal Palestine Polytechnic University Deanship of Graduate Studies/Master's of Informatics Program Hebron Palestine Palestine Polytechnic University Department of Computer Science and Information Technology Hebron Palestine

ISBN: (纸本)9781665428705

Functional and non-functional requirements are equally important in software engineering. Both of them are mixed together within the same software requirement document. Usually, they are expressed in natural languages. So, a lot of human effort is required to classify them. Software requirements classification is a challenging task. Requirements classification can help developers to deliver quality software that meets users' expectations completely. In this paper, we present a Semi-Automated classification approach of Arabic functional and non-functional requirements using a natural language processing (NLP) tool. We propose a set of heuristics based on basic constructs of Arabic sentences in order to extract information from Arabic software requirements to classify the requirements into functional and non-functional requirements. This research aims to help software engineers by reducing the cost and time required in performing manual classification of software requirements. © 2021 IEEE.

关键词： Requirements engineering

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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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Implementation and Evaluation of 0.6 µm CMOS Complex RCPF for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2025年第1期145卷 25-37页

作者： Sato, Lina Fukagawa, Kanta Shouno, Kazuhiro Tanimoto, Hiroshi Muto, Cosy Moriyama, Seijiro Takahashi, Chikau Yoshino, Michitaka Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan Anagix Corporation 3-22-27 Numama Kanagawa Zushi249-0004 Japan Takamori Corporation Japan Research Center for Micro-Nano Technology Hosei University 3-11-15 Midori-cho Koganei Tokyo184-0003 Japan

This paper describes the implementation and evaluation of an RC polyphase filter (RCPF) and circuitry for measuring its frequency characteristics. The integrated circuit is fabricated on a 0.6 µm CMOS process and its supply voltage is 5 V. The integrated circuit includes a filter under test (FUT), a four-phase square wave generator, several analog switches and differential amplifiers to amplify the high-impedance signals of the RCPF. This FUT is a third-order complex RCPF whose passband is 8 − 40 MHz. This paper describes the design of these circuits in detail. This integrated circuit can select two measurement methods by means of external control signals. The first method uses a four-phase square wave. The frequency characteristics are measured by using an on-chip four-phase square wave generator driven by an external clock generator and by a spectrum analyzer tuned to the fundamental frequency. The second method uses an external network analyzer. Furthermore, this integrated circuit is equipped with a calibration circuit to measure the frequency characteristics of the circuitry except FUT by passing through the input / output signals of the FUT. As a result, the frequency characteristics of the FUT itself can be accurately measured. The effectiveness is confirmed through evaluation of the fabricated integrated circuit. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Differential amplifiers

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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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The Role of Financial Technology for Development of Micro, Small and Medium Enterprises (Msmes) in Indonesia 11

The Role of Financial Technology for Development of Micro, S...

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11th International Conference on Communication and Network Security, ICCNS 2021

作者： Meiryani, Meiryani Hanna Uli Pakpahan, Noviyanti Wahyuningtias, Dianka Mad Daud, Zaidi Liawatimena, Suryadiputra Accounting Department Faculty of Economics and Communication Bina Nusantara University Jakarta11480 Indonesia Hotel Management Department Faculty of Economics and Communication Bina Nusantara University Jakarta11480 Indonesia Accounting Department Faculty of Economics and Business Universiti Putra Malaysia Malaysia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781450386425

The rapid development has made financial technology/financial technology's innovations more diverse according to the needs and financial problems that arise. With the existence of financial technology (financial technology) it can also make the financial service process faster, more effective and easier. Because of the development in the field of financial technology with the various solutions offered, this study aims to examine the role of financial technology for the development of MSMEs (Micro and Medium Enterprises), this research also includes aspects of the role of financial inclusion for the development of MSMEs to be able to see how much public knowledge is about financial service products and risk knowledge. The case study in this research is MSMEs in the Tangerang area using quantitative methods. The results of research using multiple linear regression analysis found that the role of financial financial technology for the development of MSMEs has a significant positive effect. Meanwhile, financial inclusion for the development of MSMEs has a positive and insignificant effect on the dependent variable. This study concludes that the use of financial technology for MSME businesses, as well as the high reference to financial products, are important factors in encouraging the development of MSMEs. © 2021 ACM.

关键词： Linear regression

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Database management system design for automatic weather information with twitter data collection 6

Database management system design for automatic weather info...

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

作者： Purwandari, Kartika Perbangsa, Anzaludin Samsinga Sigalingging, Join W. C. Krisna, Adhi Asta Anggrayani, Suci Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Meteorological Climatological and Geophysical Agency Database Center Division of Bmkg Jakarta10720 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Improvement of data management about weather in Indonesia is very supportive of the need for weather information. Currently, the weather information that is disseminated to the public is only based on sensor location points that are spread across Indonesia. However, supporting data generated from these sensors is only limited to certain areas. We hence develop a database system that can help collect weather information automatically via Twitter. This facilitates the Indonesian people in knowing weather information based on the completeness of data from sensor data and Twitter. The steps from the database that are built will be very helpful in collecting data about the weather throughout Indonesia. The basic step that we use in our database are conceptual, logical and physical design. The information obtained in this study is very important for efficient and effective data collection to achieve real-time automation of weather information. © 2021 IEEE.

关键词： Database systems

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