检索结果-内蒙古大学图书馆

7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Ramadhan, Andhika Naafi Pane, Kharisma Naufal Wardhana, Kun Rifki Suharjito Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Industrial Engineering Department BINUS Graduate Program-Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia

Technological developments have resulted in a trend of cryptocurrencies that use a technology called blockchain to create and record all transactions made into a digital ledger. Along with the emergence of the trend of cryptocurrencies, this development has also resulted in crimes that hit the digital world, such as data leakage and cyber espionage. This threat can be prevented by applying blockchain technology to the database that has been used. Therefore, we need a system that can facilitate the use of blockchain technology and can process data from an existing relational database into a blockchain-based database. The system developed in this study was built with the FastAPI framework that uses the Python programming language and the React framework that uses the Javascript programming language. This system was tested using Katalon and Wireshark software to perform throughput testing and man-in-The-middle attacks. Evaluation of this system is assessed based on the average throughput time and also the results of Wireshark packet capture. The system designed in this research is expected to help overcome interoperability problems when using blockchain and improve relational database integrity. The results of the test show that the system is safe from man-in-The-middle attacks while sending data through API and has a faster throughput time than BigchainDB system by 4.151 seconds. © 2022 Elsevier B.V.. All rights reserved.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Mitigating Berkson's Paradox with Neural Propensity Score Matching in E-Commerce Deals

Mitigating Berkson's Paradox with Neural Propensity Score Ma...

引用

International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Nicholas Dominic Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Within observational Data science workloads, Berkson's paradox can lead to false causal inferences. One of the prominent quasi-experimental methods to mitigate this selection bias is Propensity Score Matching (PSM). An approach called Neural PSM (NPSM) was developed to overcome the drawbacks of conventional regression-based PSM, including its limited flexibility to model high-dimensional data and non-linear relationships that could cause imperfect covariate balance. In this study, a three-layer depth of Deep Neural Networks was designed to estimate propensity scores and finally balance both control and treatment groups of the Groupon dataset. An unsupervised k-Nearest Neighbor algorithm then helped the model to efficiently detect and cluster similar matching points. From the five salient features presented, NPSM successfully achieved lower differences in Cohen's d effect size, i.e., 0.313 for coupon duration, 0.017 for promotion length, 0.425 for quantity sold, -0.199 for limited supply, and 0.395 for Facebook likes. While these results mostly outperformed Linear Regression (LR) and Random Forest (RF) models, further evaluation is needed to verify the true effectiveness of NPSM in mitigating Berkson's paradox in broader e-commerce contexts.

关键词： Radio frequency Social networking (online) High dimensional data Linear regression Nearest neighbor methods Data science Electronic commerce Intelligent systems Cybernetics Context modeling

来源：评论

学校读者我要写书评

暂无评论

Airline reviews processing: Abstractive summarization and rating-based sentiment classification using deep transfer learning

引用

International Journal of Information Management Data Insights 2024年第2期4卷

作者： Syed, Ayesha Ayub Gaol, Ford Lumban Boediman, Alfred Budiharto, Widodo Department of Doctor of Computer Science – BINUS Graduate Program Bina Nusantara University Jakarta Indonesia Department of Econometrics and Statistics - The University of Chicago Booth School of Business Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Opinion summarization and sentiment classification are key processes for understanding, analyzing, and leveraging information from customer opinions. The rapid and ceaseless increase in big data of reviews on e-commerce platforms, social media, or review portals becomes a stimulus for the automation of these processes. In recent years, deep transfer learning has opted to solve many challenging tasks in Natural Language Processing (NLP) relieving the hassles of exhaustive training and the requirement of extensive labelled datasets. In this work, we propose frameworks for Abstractive Summarization (ABS) and Sentiment Analysis (SA) of airline reviews using Pretrained Language Models (PLM). The abstractive summarization model goes through two finetuning stages, the first one, for domain adaptation and the second one, for final task learning. Several studies in the literature empirically demonstrate that review rating has a positive correlation with sentiment valence. For the sentiment classification framework, we used the rating value as a signal to determine the review sentiment, and the model is built on top of BERT (Bidirectional Encoder Representations from Transformers) architecture. We evaluated our models comprehensively with multiple metrics. Our results indicate competitive performance of the models in terms of most of the evaluation metrics. © 2024 The Author(s)

关键词： Airline reviews Domain adaptation Opinion summarization Review rating Sentiment classification Two-stage finetuning

来源：评论

学校读者我要写书评

暂无评论

Personalized Recommendation System for Online Learning: An Opportunity

Personalized Recommendation System for Online Learning: An O...

引用

International Conference on Business and Industrial Research (ICBIR)

作者： Dina Fitria Murad Mohamad Toha Hendra Mayatopani Bambang Dwi Wijanarko Yaya Heryadi Meta Amalya Dewi Riyan Leandros Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Universitas Terbuka Indonesia Department of Information Systems Pradita University Indonesia Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

In the world of education, learning management systems are currently widely used. The massively open online courses are accessible either via the web or mobile. The Learning Management System (LMS) is one of the learning media that is widely used today in online learning. An LMS with the use of a personalized recommendation system can provide items tailored to the interests and behavior of its users. Items in the recommendation system can be in the form of recommendations for learning models, ways of learning, learning patterns, or learning stages according to the characteristics and profile of the user. The purpose of this study is to identify opportunities for better online learning by utilizing a customized recommendation system based on the history of user interaction with the system, using several factors as determinants or variables. The method used is a review conducted from several stages. The result of this is knowing the factors that influence the user, and the opportunities that exist in the system recommendations on personalized development.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Implementation of Fine-Tuned BERT for Enzyme Classification Based on Gene Ontology

Implementation of Fine-Tuned BERT for Enzyme Classification ...

引用

International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA)

作者： Matthew Martianus Henry Christian Kenneth Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331520311

ISBN: (纸本)9798331520328

Enzymes are biocatalysts with vital roles in biological functions and many industrial applications. Diverse enzymes are classified using Enzyme Commission (EC) nomenclature, making differentiation challenging. On the other hand, another biological information, gene ontology (GO), can describe the biological aspects of enzymes, covering related biological processes (BP), molecular functions (MF), and their locations within cells (CC). This study proposes a novel EC class and subclass classification of enzymes within the ontology subclass based on their GO semantics using a Bidirectional Encoder Representation of Transformer (BERT). The BERT model is first fine-tuned using the preprocessed GO term name and definition, with the enzymes in each ontology class (BP, MF, or CC) are also divided based on how the GO assigned, either through manual annotation (NONIEA) or electronically inferred (IEA). BERT successfully obtained 0.93, 0.60, 0.99, 0.90, 0.40, and 0.35 F1 scores during fine-tuning for BP IEA, BP NONIEA, MF IEA, MF NONIEA, CC IEA, and CC NONIEA, respectively. On the test set, the fine-tuned BERT significantly outperformed GOntoSim, a framework to calculate semantic similarity based on classical information theory, in EC class classification across all metrics with less inference time in all ontology subclass. Expanded further to the EC subclass, BERT can classify the enzyme on the EC subclass level in BP IEA and MF IEA ontology subclass. However, longer epochs are needed in fine-tuning. This result shows that the names and definitions of GO terms are distinguishable features in classifying enzymes as an alternative to the information content approach.

关键词： Measurement Enzymes Semantics Bidirectional control Manuals Ontologies Transformers Encoding Biological information theory Informatics

来源：评论

学校读者我要写书评

暂无评论

The Information Systems and Model for Succession Planning in Higher Education Institutions

The Information Systems and Model for Succession Planning in...

引用

computer Applications (ICCA), IEEE Conference on

作者： Devyano Luhukay Ford Lumban Gaol Meyliana Harjanto Prabowo Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331534592

ISBN: (纸本)9798331534608

Succession planning is important in organization management. It is a strategic level management that involves not only finding the best successor but also including the development program for the successor so that they will be ready for the new prospective position. The integration and collaboration between all related stakeholders are important. Specifically, in succession planning at higher education, key positions play an important role so that institutional operations can run well. In this research, a succession planning model in higher education was built using the Systematic Literature Review(SLR), and the Software Development Lifecycle(SDLC) method was used for the application development, also, to be able to evaluate the implementation of one of the steps of the model, the simulation technique used machine learning (ML) to find the best algorithm for predicting the readiness of potential successors at higher education. Six algorithms were used: Logistic Regression, Decision Tree, K-Nearest Neighbor, Linear Discriminant Analysis, Gaussian Naïve Bayes, and Support Vector Machine. The data prepared for the simulation is based on the factors for the best successor candidate. The results obtained in this research are the succession planning model and the application design, as well as the Support Vector Machine (SVM) algorithm as the best algorithm for predicting the readiness status of successor candidates based on SVM’s accuracy score that is compared to the other algorithm in some variance amounts of data. The Succession Planning Model that is actualized in the application design provides a reference for the higher education institution to implement the Succession Model in their institution.

关键词： Support vector machines Machine learning algorithms Systematics Education Software algorithms Machine learning Predictive models Prediction algorithms Planning Information systems

来源：评论

学校读者我要写书评

暂无评论

An Object Driven Fuzzy Decision Model for Sugarcane Yield Prediction in Indonesia's Sugar Industry 7

An Object Driven Fuzzy Decision Model for Sugarcane Yield Pr...

引用

7th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2024

作者： Utama, Ditdit Nugeraha Asrol, Muhammad Suharjito Harito, Christian Napthaleni Syafrudin, Muhammad Bina Nusantara University BINUS Graduate Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Industrial Engineering Department Jakarta11480 Indonesia Sejong University Department of Artificial Intelligence and Data Science Seoul05006 Korea Republic of

ISBN: (纸本)9798331519643

A sugarcane yield of one plantation area depends on several independent variables. Practically it is challenging to predict accurately by using conventional methods. This study aims to develop a decision model based on a combination of fuzzy logic and object-oriented methods to predict sugarcane yield. The research is conducted in four main stages, employing object-oriented methods for model design and fuzzy logic for model construction. Object and activity diagrams are used for the object-oriented model design. The fuzzy membership functions employed are a combination of trapezoidal and triangular shapes. The resulting decision model can simulate 2,225 data from plantation areas in Indonesia. Based on the 10 examples of plantation area data in Indonesia, plantation number one obtained the largest sugarcane yield, which was 4.79%, with a similarity value of 0.90 (when compared to manual calculations as its ground truth). This similarity value is a higher value when compared to the average similarity value, which is 0.89. © 2024 IEEE.

关键词： Sugar industry

来源：评论

学校读者我要写书评

暂无评论

Ensuring Success in Quick Commerce by Evaluating Its Application Development Capability Maturity Using COBIT 2019

Ensuring Success in Quick Commerce by Evaluating Its Applica...

引用

International Conference on Information Management and Technology (ICIMTech)

作者： Chandra Hermawan Heruatmadja Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Digital Communication and Hotel & Tourism Institute of Technology Bandung Bandung Indonesia Computer Science Department Faculty of Computer Science Universitas Indonesia Depok Indonesia

In the digital era, the demand for quick access to goods (q-Commerce) has driven retail companies to develop online shopping applications, aiming to attract more consumers. To cater to the demands of q-Commerce, specialized mobile applications that prioritize fast delivery and user-friendly experiences are crucial. However, developing and maintaining these applications present challenges in terms of speed of development and security. This research focuses on a retail company, PT X, with a q-Commerce application serving 3.5 million active users monthly. The research aims to assess PT X's capability maturity level in application development governance using the COBIT 2019 Capability Maturity Model. Additionally, the study investigates the differences in assessment methods between COBIT 2019 and previous versions. The findings contribute to understanding in which level of capability maturity that companies can achieve in developing quick commerce applications with a large user base. Findings of this research provides an overview for PT X to initiate the necessary initial steps to comply with the good application development according to COBIT 2019 standard. This research also highlights to future researchers that COBIT 2019 is primarily a framework for enterprise-level assessment and need to be adjusted to be suitable for measurements below the enterprise level.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Objective Sleep Quality Measurement based on Fuzzy Logic and Wearable Device

引用

Procedia computer science 2023年 227卷 1153-1162页

作者： Jason A. Wijaya Pauw D. Andersen Nico Surantha Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Department of Electrical Electronic and Communication Engineering Tokyo City University Japan

Sleep is the natural state of relaxation for human being. Sleep quality is an essential yet frequently neglected aspect of sleep in general. Sleep quality is essential because it allows the body to restore itself and prepare for the next day. The standard method for evaluating sleep quality was subjective evaluation. Actigraphy devices, which can measure the sleep cycle, are now widely available. This study developed a method using Fuzzy Logic and an actigraphy device to measure and classify sleep quality. The fuzzy logic method was developed in several stages, which are determining the sleep quality measurement parameters, constructing the fuzzy set for each input variable, and developing the fuzzy rules. To evaluate the proposed fuzzy model, five individuals were invited to participate in the experiment and required to complete the PSQI subjective sleep questionnaire. The evaluation result shows that our proposed Fuzzy model achieves lower error compared to the existing method.

关键词： sleep sleep quality actigraphy artificial intelligences fuzzy logic

来源：评论

学校读者我要写书评

暂无评论

Explainable Tree-Based Boosting Algorithms for Smoker Detection Using Bio-Signals

Explainable Tree-Based Boosting Algorithms for Smoker Detect...

引用

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

作者： Jason Sebastian Sulistyawan Kuncahyo Setyo Nugroho Bens Pardamean Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

Many smoking-related diseases are difficult to treat and often fatal. Rather than treating diseased smokers, preventing the diseased is more achievable, though, many of them deny to being smokers, leading to another problem. Thus, this study aims to detect important aspects that can detect that the person is a smoker or not through their bio-signals through using SHAP, along with a comprehensive analysis of the used methods, gradient-boosting algorithms XGBoost, LightGBM, and CatBoost, known for their efficiency in handling complex datasets and non-linear relationships. The study then found that triglyceride, Gtp, hemoglobins significantly affect the body's responses to smoking, based on the CatBoosts’ results, having an AUC score of up to 0.8612 and an accuracy score of up to 0.7776 with the selected features.

关键词： Seminars Measurement Accuracy Predictive models Feature extraction Boosting Physiology Classification algorithms Reliability Diseases

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：