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Service Oriented Fuzzy Smart Model for Agriculture Investment of Hydroponic Green-Leaf Vegetable Plant

Service Oriented Fuzzy Smart Model for Agriculture Investmen...

引用

International Conference on ICT For Smart Society (ICISS)

作者： Ditdit Nugeraha Utama Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

An integrated approach is necessary for implementing a model that involves multiple actors. One effective way to achieve this is through a service-oriented approach. The objective of the study was to develop a service-oriented fuzzy model, which combines the functional-structural plant modelling (FSPM) method for the plant computational model (PCM) and the fuzzy logic method for both PCM and the decision support model (DSM). This combined method aims to model the plant behaviour morphologically and propose investment decisions in agriculture, specifically for the hydroponic cultivation of Bok Choy, a green-leaf vegetable. The accuracy of the interconnected model application based on the service concept, known as the service-oriented fuzzy smart model (SOFSM), reached 94.33%.

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Object Oriented Fuzzy Decision Model for Insurance Claim

Object Oriented Fuzzy Decision Model for Insurance Claim

引用

International Conference on Computing, Engineering, and Design (ICCED)

作者： Sumiatie Susanto Ditdit Nugeraha Utama Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

The insurance industry faces a significant challenge concerning insurance claims, particularly due to the prevalence of fraudulent insurance claims. To address this issue, one potential solution is the implementation of a computer-based decision model. This research presents a fuzzy decision model based on object-oriented method development. The study involves seven stages (i.e. case analyzing, parameter analyzing, objects-parameters linking, detail object relation constructing, parameter exchange analyzing, OOFDM constructing, and model verifying and validating), with an object-oriented approach serving as the foundational method for constructing the model, and fuzzy logic as the primary method for assessing claim risks in proposing the best decision. The model has the capability to simulate insurance claims and offers objective decisions based on 19,611 claims data, categorizing them into two decision categories: acceptance and pending.

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An Exploratory Computational Approach for Elucidating a Nonstructural Protein Interaction in Dengue Virus 2 9th

An Exploratory Computational Approach for Elucidating a Nons...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Pardamean, Bens Mathematics Department School of Computer Science Bina Nusantara University 11480 Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

As the dengue infection still impacts hundreds of millions of people globally, unprecedented efforts in dengue drug development have been more progressive in recent decades. Computational methods provide a fast, sustainable, and efficient screening of active compounds and newly created drug molecules, including those specifically targeting nonstructural proteins (NS) of dengue viruses. In this work, protein modeling for the NS proteins of DENV-2/16681 strain was performed using a template-based homology modeling for the NS3 protein and an Artificial Intelligence (AI)-based prediction via AlphaFold for the NS4B protein. Moreover, the protein-protein interaction between the two structures was predicted using the HADDOCK server, which employs information about active and passive residues of the interaction interface to guide the docking process. After the modeling and its respective refinement process, the predicted structures of NS3 and NS4B improved their steric clashing scoring from MolProbity assessment. The refined models were then docked, and the resulting docking pose was analyzed to extract the interacting residues based on the polar contacts within the interface of the two proteins. Our result presents a preliminary study to create a dataset related to in silico molecular interactions of the NS3-NS4B interaction of different DENV types. It is helpful for building a computational pipeline for elucidating protein-ligand problems in dengue drug screenings. © 2024 The Authors.

关键词： Molecular docking

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Cancer Classification in Imbalance Data Using Double Filtering Approach

Cancer Classification in Imbalance Data Using Double Filteri...

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Almuzhidul Mujhid Jeffrey Junior Tedjasulaksana Nikita Ananda Putri Masaling Abba Suganda Girsang Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

Cancer, also known as malignant neoplasm, is a complex and potentially fatal disease characterized by uncontrolled and abnormal cell growth in the body. The main problems with microarray cancer studies are the high curse of dimensionality and small sample size caused by redundant and irrelevant genes. To deal with the number of features that exceed the amount of data, this research purposed double filtering method Lasso-GA, Lasso is used to select the features based on feature correlation while Genetic Algorithm is used to optimize the most important features with accuracy traditional machine learning as its fitness function. The results show how effective the suggested method is; in breast cancer, the linear SVC model achieves excellent accuracy (0.93), precision (0.94), recall (0.94), and F1 score (0.94), while in lung cancer, the linear SVC, random forest, and logistic regression models perform well (accuracy: 0.95, precision: 0.92, recall: 1, F1 score: 0.95). Logistic regression is the most effective method for bladder cancer, with an accuracy of 0.82, precision of 0.77, recall of 1, and F1 score of 0.87.

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Exploring the Effect of Activation Function on Transformer Model Performance for Official Announcement Translator from Indonesian to Sundanese Languages

Exploring the Effect of Activation Function on Transformer M...

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computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Bambang Dwi Wijanarko Dina Fitria Murad Yaya Heryadi Cuk Tho Kiyota Hashimoto Computer Science Department BINUS Online Learning Jakarta Indonesia Information Systems Department BINUS Online Learning Jakarta Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Jakarta Indonesia Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Prince of Songkla University Phuket Campus Phuket Thailand

Automated language translation involving low-resource language has gained wide interest from many research communities in the past decade. One lesson learned from the past COVID-19 pandemic, particularly in Indonesia, is that many local Governments have to release regular public announcements to keep people following health protocol especially when they are in public areas. Many studies showed some evidence that rural people in Indonesia which covers a large proportion of Indonesia’s population, feel more convenience receiving official announcements in their local language. However, translating official announcement from the national language to many local languages in Indonesia require many experienced bilingual translators and time. This paper presents exploration results in developing an automated language translator model to translate texts in Bahasa Indonesia to the Sundanese language. In particular, this study aims to explore the effect of ReLU, Sigmoid, and Tanh activation functions of the Vanilla Transformer Model on its translation performance. The experiment results showed that the activation function under study gives similar training accuracy (0.98). However, ReLU achieves better performance than Tanh in terms of validation accuracy, training loss, and validation loss.

关键词： Training COVID-19 computer science Protocols Pandemics Local government Sociology

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Service Oriented Fuzzy Decision Model for Health Insurance Claim

Service Oriented Fuzzy Decision Model for Health Insurance C...

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Advancement in Data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Ditdit Nugeraha Utama Sumiatie Susanto Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

Insurance claim is a fascinating issue to study. A potential loss for the insurance company is major coming from this issue. Thus, many studies performed already to answer such an issue. This study takes aim to develop a computational decision model based on service technology. Four fundamental methods operated in constructing the model and its implementation in service technology. The analytical hierarchical process (AHP) and fuzzy logic methods are two methods benefited to construct the model; where the AHP used to prioritize thirteen parameters considered and the fuzzy logic with its inference capability operated to generate the decision. Object oriented is an analysis and design method to analyze and design the model implanting it in service oriented architecture (SOA). Then, SOA conception functioned to deploy the model in the service architecture. Ultimately, the suggested framework comprising three layers of service-oriented architecture (SOA), namely business process, service interface, and application, has been established, alongside the integration of eight essential services that connect these three applications. The model demonstrates simulation outcomes indicating that 31.47% of claims are categorized as low risk and have been approved, 17.64% of claims are considered moderate risk with currently pending decision status (requiring additional investigation), while 50.89% of claims are classified as high risk with also pending decision status.

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Social Network Analysis and PowerBI Dashboard to Determine E-Wallet Marketing Strategies

Social Network Analysis and PowerBI Dashboard to Determine E...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Rita Rita Sheila Monica Miracle Aurelia Abba Suganda Girsang Virienia Puspita Global Business Marketing BINUS Business School Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program- Master of Computer Science Bina Nusantara University Jakarta Indonesia Communication Department BINUS Graduate Program - Master of Strategic Marketing Communication Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

The rapid growth of digital payment platforms like OVO, ShopeePay, and GoPay in Indonesia has driven the need for businesses to optimize marketing strategies by analyzing customer interactions through social media. This study leverages Social Network Analysis (SNA) and Power BI to extract insights from Twitter data, offering a comprehensive view of how these platforms engage with consumers and shape brand perceptions. By applying sentiment analysis and classification using IndoBERT results, this study aims to identify key influencers, assess the sentiment around these brands, and visualize marketing communication patterns. The integration of SNA and BI tools provides a detailed, data-driven approach to improving business decision-making in the competitive digital payment market, by utilizing them to evaluate e-wallet tweet activity and user interactions in Indonesia and identifying key periods and regions for targeted marketing. Strategies like loyalty programs and segmented campaigns are recommended to enhance user engagement, address security concerns, and maximize market reach during culturally significant periods such as Eid and the holiday season.

关键词： Java Sentiment analysis Negative feedback Social networking (online) Online banking Shape Companies Security Web sites Multimedia communication

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A Smart Fuzzy Decision Model for Anticipating Household Expenditure Shifts in Response to Fluctuating Fuel Prices in Indonesia

A Smart Fuzzy Decision Model for Anticipating Household Expe...

引用

International Conference on ICT For Smart Society (ICISS)

作者： Frans Fericia Jeremy Ansellino Gunawan Ditdit Nugeraha Utama Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

Analyzing the impact of fuel price increases in Indonesia is crucial for making informed strategic decisions. This paper presents a smart fuzzy decision model aimed at predicting changes in household spending in response to fuel price fluctuations. The model utilizes fuzzy logic as its main method, enabling it to capture the intricate relationships between household spending and fuel prices. In addition, the proposed model incorporates various factors that can potentially influence household spending. By simulating prediction results under different fuel price increments ranging from 0% to 30%, the model provides valuable insights for policymaking concerning fuel pricing and offers strategies to mitigate the impact of fuel price fluctuations on household welfare.

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Negation Handling on XLNet Using Dependency Parser for Sentiment Analysis

Negation Handling on XLNet Using Dependency Parser for Senti...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Ferdinand Winaya Abba Suganda Girsang BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

Negation handling is often overlooked in Indonesian sentiment analysis, making it difficult to automatically and accurately determine the polarity of sentences containing negation words. Negation is a challenging issue in natural language processing, as it can drastically alter the meaning of a sentence. For example, common Indonesian negation words such as “tidak” (no/not), “belum” (not yet), or “jangan” (do not) can reverse the sentiment polarity in a text. Previous studies have proposed rule-based approaches, relying on linguistic rules or dependency parse trees to handle negation. However, negation is a complex problem that cannot be effectively addressed by simple rules alone, as they often lack flexibility in handling diverse sentence structures and negation complexities. Therefore, a more dynamic approach is needed, such as combining a dependency parser with an embedding layer, which can map syntactic relationships between words and learn vector representations of negation. This allows the model to flexibly determine the scope of negation words, enabling more accurate sentiment analysis even in complex sentences. This study evaluates the impact of negation handling on a transformer-based model, specifically XLNet, in Indonesian sentiment analysis. Using the proposed method, the model's F1 score increased by 2.13%, from 71.42% to 73.55%, compared to the baseline model. This demonstrates that the proposed negation handling strategy enhances sentiment prediction accuracy, making the model more effective at handling texts with negation.

关键词： Sentiment analysis Analytical models Accuracy Syntactics Predictive models Linguistics Transformers Vectors Complexity theory

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Mobile application for paid road use for the reduction of traffic congestion 5

Mobile application for paid road use for the reduction of tr...

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5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021

作者： Harliano, Muhammad Rizqiko Afif, Mohammad Ramadhani, Hafiz Iwan, Tanto Harsono Koswara, Rian Warnars, Hareo Leslie Hendrie Spits School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665412728

Technology has evolved a lot in the last few years. E-toll becomes the only payment method available for highway roads, especially in Indonesia. This system of payment is expected to enhance the efficiency of payment from using the previous payment method. However, there are still some weaknesses addressed by the implementation of e-toll. It still takes time because the car's owner should tap the card first, and the sequence time between each person to tap is different. Thus, the queue results still long, and the traffic congestion on the highway road is not fully solved. This paper aims to address a better solution by adding a more efficient option of payment. So, traffic congestion is reduced and leads to better operation in toll gates. This research work has proposed a mobile application and route prediction model based on the Dijkstra Algorithm, where the price will be decided on which type of car will be passed by the gate, and the algorithm predicts the best possible routes based on the number of cars on the highway. Payment is conducted on the application using the balance from the application. This application eliminates time consumed during toll payment at any toll gate. © 2021 IEEE.

关键词： Smart city

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