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Service Oriented Fuzzy Decision Model for Road Traffic Engineering 9

Service Oriented Fuzzy Decision Model for Road Traffic Engin...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Utama, Ditdit Nugeraha Zaki, Fauzan Affan Munjeri, Intan Juliani Putri, Nidya Utami Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia UIN Syarif Hidayatullah Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350370126

A number of attempts have already been implemented formally to solve road traffic congestion. However, the objective strategy type of road traffic engineering could not be proven truly. Try and error is one inefficient way in road traffic engineering to degrade the level of congestion. The study was conducted to propose a new service-oriented model (SOM) that combines fuzzy-logic and water flow algorithm methods (called FWFA). The method combination was operated as the main method to construct the decision model for selecting the objective strategy in road traffic engineering. Also, service-oriented architecture (SOA) is realized to implement such a constructed model practically. The Model can suggest the most optimal strategy decision in road traffic engineering. Here, a main traffic road of Juanda in area Ciputat, Tangerang Selatan, province Banten, Indonesia;was selected as a research object in this study. The constructed SOM for road traffic engineering was structurally delivered in this paper. © 2023 IEEE.

关键词： Fuzzy logic

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A Decision Model for Smart Village in Measuring Socioeconomic Inequality 11

A Decision Model for Smart Village in Measuring Socioeconomi...

引用

11th International Conference on ICT for Smart Society, ICISS 2024

作者： Utama, Ditdit Nugeraha Binus Graduate Program - Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350389654

Making socio-economic decisions is critical as they profoundly impact communities. It is imperative to approach these decisions with academic rigour and objectivity to effectively address prevalent community-wide issues. One such challenge often encountered by communities is socio-economic inequality. This study attempts to tackle this challenge by creating a decision model to assess socio-economic inequality in a region. The research was performed via four crucial stages, leading to the creation of a fuzzy decision model based on the Williamson Index (WI) method. This carefully developed model was designed and constructed using an object-oriented approach. The simulation results indicated that the integrated inequality index value for Sawah, an urban village in Ciputat, Indonesia, is 0.65. This em-pirical result provides valuable insights, enabling policymakers and stakeholders (as decision-makers) to design targeted policies and programs to reduce inequality and enhance community prosperity. © 2024 IEEE.

关键词： fuzzy logic inequality index object-oriented smart decision model smart village socio-economic

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Abstractive Indonesian News Summarization Using BERT2GPT 7

Abstractive Indonesian News Summarization Using BERT2GPT

引用

7th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2023

作者： Nasari, Maulin Maulina, Ayu Girsang, Abba Suganda Computer Science Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350382266

This study examines the necessity of employing BERT2GPT for single-document summarization in the current age of escalating digital data. The primary focus of this work is on the abstractive technique, which tries to generate versatile summaries that surpass the primary sentences of the original content. This study employs two models, namely BERT and GPT -2, for the purpose of the summarization system. The paper introduces the BERT2GPT model, which merges the bidirectional characteristics of BERT with the generative powers of GPT. The findings indicate that the BERT2GPT method successfully catches significant information and linguistic nuances, hence enhancing the quality of the generated summaries. The corresponding average values for Rl, R2, and RL are 0.62, 0.56, and 0.60, respectively. © 2023 IEEE.

关键词： Abstractive Summarization BERT2GPT single-document summarization

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Implementation of Fine-Tuned BERT for Enzyme Classification Based on Gene Ontology 11

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

引用

11th International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2024

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

ISBN: (纸本)9798331520311

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. © 2024 IEEE.

关键词： Gene Ontology

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Enhancement Design for Smart Parking System Using IoT and A-Star Algorithm 1

Enhancement Design for Smart Parking System Using IoT and A-...

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1st International Conference on computer science and Artificial Intelligence, ICCSAI 2021

作者： Stevanus, Briant Suharjito Sukmandhani, Arief Agus Bina Nusantara University Binus Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Online Learning Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Finding parking slot inside packed parking area can be frustrating sometimes. Multiple cars chasing single parking space phenomenon often happens, and numbers of cars wasting vehicle distance without knowing where to go, and hopefully found empty parking slot, or a car that about to leave the occupied parking slot. This study will focus to increase IoT environment functionality on parking area with the help of A∗ path finding algorithm to accurately pinpoint driver to vacant parking slot, determined by nearest building entrance, and remotely reserved the parking slot prior when the vehicle got the parking ticket. In this study we also discuss about few possible scenarios, further discuss about the sensors, and the system architecture. © 2021 IEEE.

关键词： Internet of things

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Machine Learning Methods for Predicting the Necessity of Caesareans Section of Childbirth 5

Machine Learning Methods for Predicting the Necessity of Cae...

引用

5th IEEE International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2021

作者： Sunge, Aswan Supriyadi Heryadi, Yaya Abdurachman, Edi Kartowisatro, Iman H. Bina Nusantara University BINUS Graduate Program- Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty Of Engineering Computer Science Departement - BINUS Graduate Program Computer Science Computer Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9781665401968

In the process of delivery usually the baby comes out of the vagina but under some circumstances a cesarean section is performed. Caesarean section, on the one hand can have short-term and long-term effects for the mother but on the other hand it is a life-saving procedure for both mother and child. The purpose of this study is to predict whether or not a caesarean section is necessary for a pregnant mother using a machine learning classifier model and some features from medical records as input data. In this study, some machine models were explored including Logistic Regression, K-Nearest Neighbor, Random Forest, Decision Tree, Gradient Boosting and Support Vector Machine. The experiment found that Logistic Regression model achieved the highest performance as measured by average accuracy 95%, average precision 86%, average recall 95%, and average F1 86%. © 2021 IEEE.

关键词： Forecasting

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Decision Support Model for Supplier Selection Using Fuzzy Logic Concept 3

Decision Support Model for Supplier Selection Using Fuzzy Lo...

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3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Gunawan, Ferdi Wang, Gunawan Utama, Ditdit Nugeraha Komsiyah, Siti Suharjito Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Information Systems Management Department Bina Nusantara University BINUS Graduate Program-Master of Information Systems Management Jakarta11480 Indonesia Computer Science Department Bina Nusantara University BINUS Graduate Program-Master of Computer Science Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781538658215

Improving the company's performance to compete globally, in rapid growth market economy condition, is practically required. One of the important things is right decision in issues of supplier selection. The improper supplier affects consequence negatively to the quality of company's business process. Thus, the right way to answer the issues is incredibly crucial to be proposed. This study aims to design an application to determine the sub-criteria weight with Fuzzy-AHP method, select the best supplier via Fuzzy-TOPSIS method, and calculate the allocation of quota to each supplier thru Fuzzy-MOLP method. The study was conducted on high class store by taking the object of material gymnastic product (spandex). Simulation confidently demonstrates that the Fuzzy-AHP approach is able to be operated to generate parameter Quality Product as the best sub-criteria (with value 0.221), the Fuzzy-TOPSIS technique can be used to ring out the supplier (where CV. Maju Jaya Abadi as the best supplier), and the method Fuzzy-MOLP is exploited to calculate the allocation of quota (where CV. Maju Jaya Abadi as a supplier with the highest percentage of 44.40%). © 2018 IEEE.

关键词： Fuzzy logic

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Transformer and CNN Comparison for Time Series Classification Model 15

Transformer and CNN Comparison for Time Series Classificatio...

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15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Sonata, Ilvico Heryadi, Yaya Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350383829

In real life, many activities are performed sequentially. These activities must be carried out sequentially, such as the assembly process in the manufacturing production process. This series of activities cannot be reduced or added so that the main goal of the series of activities is achieved. Apart from that, there are also time series events that occur naturally, such as rainy and hot conditions in a certain area. The classification process of time series activities is very important to see the possibility of anomalies occurring. The significant development of machine learning models in recent years has made the process of classifying time series data increasingly researched. Several previous studies stated that deep learning models were more accurate in classifying time series data. In this paper, we will compare Convolutional Neural Network (CNN) and Transformer deep learning models in classifying time series data. Experimental results using the same public datasets for CNN and Transformer model show that the CNN model is more accurate than the Transformer model. The results of measuring accuracy using confusion matrix show that CNN has an accuracy of 92% and Transformer has an accuracy of 80%. © 2023 IEEE.

关键词： Time series

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Risk Assessment Analysis on Mobile Banking Using Cobit 5 Framework

Risk Assessment Analysis on Mobile Banking Using Cobit 5 Fra...

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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Utamajaya, Joy Nashar Ramadhan, Arief Abdurachman, Edi Trisetyarso, Agung Zarlis, Muhammad Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department West Jakarta Indonesia

ISBN: (纸本)9781665468763

In general, public or private organizations or companies have used information-based technology as a support to improve business performance to be more effective and efficient in order to achieve a company's business goals. Banking companies include companies that use information technology such as Mobile Banking in their services. Mobile banking is a banking facility or service using mobile communication tools such as mobile phones, with the provision of facilities for banking transactions through mobile applications. Even though they have used a good SOP (Standard Operating Procedure), there are still many obstacles that occur, especially system problems and human errors. This can result in a high risk if it occurs continuously and will be fatal to the company's business processes. Therefore, COBIT 5 can be used as a benchmark for the IT assessment process on local bank mobile banking services. The purpose of the study is to determine the level of capability and strategy for improving risk management. The method used in this study is to focus on IT risk management with the COBIT 5 framework in the Evaluate Direct Monitoring (EDM) 3 domain, Align Plain Organize (APO) 12. From the results of the analysis, the calculation of the capability level is at Level 1 Performed Process with a score of 82.04%, thus the status has evidence as well as a systematic approach and this achievement is obtained significantly through the assessment of process attributes. © 2022 IEEE.

关键词： Risk management

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Detection of Narcotics Money Laundering in Suspicious Financial Transactions Using Convolutional Extreme Gradient Boosting (ConvXGB) 4

Detection of Narcotics Money Laundering in Suspicious Financ...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Winarno, Leonardus Verrell Sayoga Kinantra Wibowo, Antoni School of Computer Science Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331506490

With the development of narcotics problems that continue to increase, the Indonesian Government responds through the Badan Narkotika Nasional (BNN) with data showing the condition of narcotics tends to grow every year and is followed by the number of assets of money laundering (ML) crimes against narcotics cases. One of the banks in Indonesia as a reporting party for suspicious financial transactions (SFT) has obstacles in detecting narcotics ML due to complex and rarely found patterns. Some previous studies conducted experiments using Convolutional Neural Network (CNN), Extreme Gradient Boosting (XGBoost), and even a combination of both into Convolutional Extreme Gradient Boosting (ConvXGB), and improved model performance in several datasets. This paper designs a model using the ConvXGB algorithm by adopting the CNN architecture, LeNet-5, by applying several convolution layers and pooling layers as a baseline model for feature learning, and the XGBoost as feature classification. Three phases of research are the preprocessing phase by collecting data, transforming data, balancing data with a hybrid sampling technique, splitting data, and scaling data, followed by the implementation phase by creating a ConvXGB model, training and testing the model, then finally the evaluation phase by analyzing results and hyperparameter tuning. The dataset used is SFT from the bank during 2023. This ConvXGB has three convolution layers, a pooling layer, and a flattened layer. The performance test results are the accuracy value and F1-Score value of 99.11% each after hyperparameter tuning. By performing a hybrid model, the model performance results are better. © 2024 IEEE.

关键词： Laundering

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