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International Conference on Information Management and Technology (ICIMTech)

作者： Marcel Meyliana Harco Leslie Hendric Spits Warnars Tirta Nugraha Mursitama Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Doctor of Research in Management Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

This research develops a conceptual model to understand the influence of digitalization on social learning, especially in the context of community empowerment. This model maps the relationship between factors that influence success, challenges faced, and variations in the use of digital platforms in social learning. By using a literature study approach, the research results show that effective communication, collaboration and empowerment are the main keys to the success of social learning in society. Key challenges identified include limited access to information, resource constraints, and digital literacy barriers, all of which require specific strategies to overcome. By examining the relationship between success factors, challenges, and use of digital platforms, this research provides new insights that support the development of more effective strategies for using digital technology to support social learning that leads to community empowerment. This research has great potential to increase people's access to learning opportunities and information, allowing them to be more active in the social learning process.

关键词： Information resources Collaboration Information management Digital intelligence Context modeling

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Toward Learning Factory for Industry 4.0: Virtual Reality (VR) for Learning Human–Robot Collaboration 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Ariansyah, Dedy Colombo, Giorgio Pardamean, Bens Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta11480 Indonesia Department of Mechanical Engineering Politecnico di Milano Milan20156 Italy Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Industry 4.0 will bring not only transformation to the manufacturing technologies but also to the profile of the workforce. Education system should be revised to prepare the future graduates embracing the knowledge of the ongoing revolution. Initiatives on the modification of curriculum and tools to deliver the concepts of Industry 4.0 must be taken. This paper examines limitations of current engineering tools and proposes a virtual reality (VR)-based learning platform to support the teaching and learning activity for Industry 4.0, focusing on the design of Human–robot Collaboration (HRC). The development process began with the identification of the general Intended Learning Outcomes (ILOs) which were extracted from the review of the HRC design issues, followed by the evaluation of the current teaching tool. From these results, the new requirements of the tools to achieve ILOs are redefined. The implementation of the system is demonstrated to show the feasibility of the proposed learning platform. This workflow can serve as initial development of another learning platform for the other innovative concepts that form the building blocks of Industry 4.0. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Industry 4.0

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AI Chatbot Technology to Predict Disease: A Systematic Literature Review

AI Chatbot Technology to Predict Disease: A Systematic Liter...

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International Conference on Artificial Intelligence and Data sciences (AiDAS)

作者： Vincent Velasco Kevin Dedy Setiawan Renaldo Robert Sanjaya Maria Susan Anggreainy Afdhal Kurniawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Artificial Intelligence (AI) and chatbot technology have emerged as promising solutions to improve healthcare services. AI chatbots can mimic human-like interactions and assist with tasks such as triaging patients and providing medical advice. This review focuses on the use of AI chatbots in predicting diseases, aiming to explore their effectiveness and potential for early intervention and treatment. The purpose of this systematic literature review is to analyze studies related to AI chatbot technology in disease prediction. A systematic literature review was conducted, analyzing a total of 24 selected journals based on predefined inclusion and exclusion criteria. The review protocol involved examining studies published from various years, with a particular emphasis on articles from 2020. The findings indicate that AI chatbots have the potential to play a significant role in predicting diseases and assisting healthcare professionals in making informed decisions. AI chatbot technology shows tremendous potential in disease prediction. By utilizing machine learning algorithms and techniques, chatbots can enhance the accuracy and quickness of disease diagnosis. Continued research and development efforts are necessary to refine AI chatbots' capabilities and revolutionize healthcare delivery for improved patient outcomes and disease management.

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Determining the Main Symptoms of Lung Cancer with Machine Learning Methods

Determining the Main Symptoms of Lung Cancer with Machine Le...

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International Conference on ICT For Smart Society (ICISS)

作者： Belinda Meylia Christina David Gabriel Immanuel Regina Faustine Surya Dharma Maria Susan Anggreany Ivan Halim Parmonangan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

The ability to identify cancer at an early stage is critical to increase the likelihood of effective treatment or stopping the progression of the disease in the body. Lung cancer is one of the most common deadly diseases and quickly kills the patient. The number of deaths caused by lung cancer surpasses those of colon, rectal, breast, and prostate cancers combined. Unfortunately, only two percent of patients with advanced lung cancer survive for five years or more. However, the survival rates are better, with 49 % of the patients surviving for five years or more, if the disease is detected early. On the other hand, in this modern era, machine learning has become one of the most reliable tools in the world for healthcare, as machine learning can learn from the data obtained and process the data that will later be used to help complete certain tasks. Therefore, in this research, machine learning or especially classification algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest, Logistic Regression, and XGBoost are used to identify three main symptoms that serve as markers for early detection of lung cancer. Determining the three main symptoms is done by combining the results of the feature importance score on each model using the rank-averaging method. The result is an average ranking of each feature based on its combined importance with an accuracy of 93.5 %.

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Variational quantum neural network with optimized ansatz for predicting malignant ventricular arrhythmias

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Procedia computer science 2024年 245卷 109-116页

作者： Nicholas Dominic Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Preventive strategies should be the utmost priority when dealing with diverse patients suffering from malignant ventricular arrhythmia (MVA) that can lead to sudden cardiac death (SCD). Electrocardiogram (ECG) data is commonly used as a predictor for MVA predictive models. In this study, all ECG signals from MIT-BIH databases were fragmented into five-minute durations with a frequency sampling of 128 Hz. To solve the absence of hybrid optimizations in Machine Learning (ML) models, a novel Variational Quantum Neural Network (VQNN) was invented. Empowered by deep learning capabilities and optimized quantum circuits design, VQNN achieved remarkable performances designated by an accuracy of up to 95.1%, a perfect 100% recall, and a 95.2% score of the area under the Receiver Operating Characteristic curve (AUC ROC) with Conjugate Gradient as an optimizer and EfficientSU2 as a quantum ansatz. Despite the susceptibility to quantum noise, this research settles a new trajectory of utilizing quantum variational algorithms to predict and expand its applicability for MVA cases.

关键词： Malignant Ventricular Arrhythmia Sudden Cardiac Death Variational Quantum Algorithm Quantum Computing Neural Network

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Fuzzy Decision Support Model on Virtual Plant Computational Model of Water Spinach (Ipomoea aquatica. Forssk) in Farming Business 6

Fuzzy Decision Support Model on Virtual Plant Computational ...

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6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

作者： Utama, Ditdit Nugeraha Agustia Rachmawati, Ika Diah Purwantono, Heraldo Yusron Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350399615

Using soil as a planting medium (conventional system) raises several problems, such as the need for large agricultural land, but the available land is limited. This problem is triggered by an increase in demand for nutrients needed by the body to support daily activities, one of which can be met by consuming green vegetables, such as Water Spinach. To solve this problem, a hydroponic wick system or a wick system can use that is simple and easy to care for. Water Spinach (Ipomoea aquatica. Forssk) vegetables are easy to grow and care for. Nutrients contained in 100 grams of Water Spinach, energy: 29 kcal, protein: 3 grams, Fat: 0.3 grams, carbohydrates: 5.4 grams, calcium: 73 mg, phosphorus: 50 mg, iron: 3 mg, vitamin A: 6300IU,vitamin B1: 0.07 mg, vitamin C: 32 mg. In addition, a combination of disciplines such as mathematics, computer science, plant science, biology, and statistics can be used. This combination of knowledge is known as Plant Computational Modeling (PCM), which uses the Growth Grammar Interactive Modeling Platform (GroIMP) with the Functional Structural Plant Modeling (FSPM) method. The virtual plant model was successfully built, the results were divided into two parts, such as single plant and multi-plant. Plant computational modeling and DSM can help deal with agricultural problems. By using a plant dataset, the GroIMP-FSPM platform, and fuzzy logic, the model could help researchers in making a better decision based on BEP evaluation. The minimum numbers of plants cultivated were 650 plants in one harvest period in order to gain a desirable profit. We recommend to cultivate even more plants in one harvest period to gain even higher profits. © 2022 IEEE.

关键词： Decision Support Model Functional Structural Plant Modelling Fuzzy Logic Growth-Grammar Interactive Modelling Platform Ipomoea aquatica Forssk Plant Computational Modelling

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GAN.s Approach On Generating Synthetical Skin Image Dataset for Imbalance Dataset

GAN.s Approach On Generating Synthetical Skin Image Dataset ...

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

作者： Tri Agus Setiawan Yaya Heryadi Lukas Lili Ayu Wulandari Ilvico Sonata Computer Science Department BINUS Graduate Program -Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia - Atma Jaya Jakarta Indonesia

ISBN: (数字)9798350368970

ISBN: (纸本)9798350368987

The use of balanced medical datasets is essential to improve the precision and accuracy of machine learning models in the healthcare field. However, dataset imbalance often becomes an obstacle, especially in the diagnosis of skin diseases. This research aims to develop a Generative Adversarial Networks (GAN) method that is more effective in generating synthetic skin datasets to overcome problems in integrating medical datasets. The methods used include developing and training a GAN model to produce realistic synthetic skin images, with a focus on improving the quality and diversity of the synthetic data produced. Important results from this research show that the developed GAN model is able to produce synthetic skin images that are not only realistic but also able to balance the original dataset. The implications of these findings include improving the accuracy and performance of machine learning models in the diagnosis of skin diseases, as well as the potential use of these methods in other medical fields that face similar problems with imbalanced datasets.

关键词： Training Accuracy Machine learning Generative adversarial networks Reliability engineering Skin Data models Medical diagnostic imaging Diseases Synthetic data

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A Review on Machine Learning Methods for Water Quality Prediction

A Review on Machine Learning Methods for Water Quality Predi...

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International Conference on Artificial Intelligence and Data sciences (AiDAS)

作者： Vickenzio Leeputra Dharma Nathanael Kenneth Nurtanio Fakhru Sidik Nugroho Maria Susan Anggreainy Afdhal Kurniawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Water is an important substance for the human body. Clean water is important for not just the human body, but also for the environment. In this paper, Prisma is used to filter many of the reference paper where the paper left are used for the research. Machine learning is one of many ways to predict water quality. Using algorithms to process large amounts of data and patterns, water quality can be predicted. Prediction on water temperature, pH, and others can be used to find bad quality water and potentially find the cure for it. There are also different kinds of environment in predicting water quality, where depending on the environment certain machine learning method is better than the others. This paper contains machine learning methods, water parameters, and areas of water for water quality prediction.

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A Hybrid Inference Engine to Predict Uncertain Sugar Content for Sugar Industry

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Procedia computer science 2024年 245卷 480-489页

作者： Muhammad Asrol Napthaleni Ditdit Nugeraha Utama Suharjito Christian Harito Muhammad Syafrudin Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Department of Artificial Intelligence and Data Science Sejong University Seoul 05006 South Korea

The sugar industry is facing challenges in increasing productivity to meet consumer demand. One opportunity for productivity improvement lies in ensuring sugar content. This study proposes a hybrid model to predict sugar content by considering uncertainty factors. A hybrid model combining fuzzy subtractive clustering, and a fuzzy inference system is proposed to predict sugar content. The clustering results using silhouette and fuzzy subtractive clustering successfully identified 6 cluster centres from 2225 datasets collected in a sugar industry in East Java Province. The hybrid inference engine model is designed with fuzzy rules derived from the clustered data. Two inference models are developed: triangular and Gaussian fuzzy numbers. The testing results indicate that the hybrid model with triangular fuzzy numbers shows the smallest error with an R2 value of 0.95. This model is possible to applied in the sugar industry for decision makers in improving productivity with taking attention into uncertain factors influencing sugar content.

关键词： Clustering Fuzzy Hybrid model Inference System Prediction Sugarcane

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Learning the Jawa Culture by using Android Smartphone 2

Learning the Jawa Culture by using Android Smartphone

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Antonio, Andrew Kezyanto, Julio Warnars, Harco Leslie Hendric Spits Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665400527

Javanese culture is a culture that the Javanese own, and the Javanese have started to exist with their culture since before the era of the Majapahit kingdom, and the Javanese are famous for their ability to sail the oceans. In addition, the many temples built, which are currently historical sites, prove that the Javanese are a large ethnic group that inhabits the island of Java. Other Javanese cultures include Javanese handicrafts such as kris, Javanese dance, Javanese batik art, Javanese wayang art, Javanese carving art, Javanese songs, and Javanese writings. All these cultures are part of the wealth of the unitary state of the Republic of Indonesia that must be preserved and passed down from one generation to another, and current technological developments tend to make the current generation less aware of preserving the culture of their ancestors. Therefore, we use technology to create an application for learning Javanese culture by applying game elements as entertainment elements. The business process of this application is modeled using use case diagrams and class diagrams to describe the database relationships. This mobile application is built using android studio and uses a MySQL database. © 2022 IEEE.

关键词： Android (operating system)

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