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Innovative Research in Applied science, Engineering and Technology (IRASET),Conference on

作者： Daniella Tjung Justin Axela Darmanto Jason William Kelanajaya Maria Grace Herlina Karto Iskandar Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

The present study explores into the relationships between authentic leadership, employee well-being, and AI experience among white-collar workers in Greater Jakarta, Indonesia. The study employs a cross-sectional survey and partial least squares structural equation modeling (PLS-SEM) to reveal that authentic leadership significantly enhances employee well-being, subsequently leading to positive interactions between employees and AI systems. The relationship between authentic leadership and AI experience is partially mediated by employee well-being, demonstrating the significance of leadership qualities like transparency, integrity, and self-awareness in creating a supportive organizational culture. The findings highlight the importance of initiatives focused on employees, such as mental health support and work-life balance, in facilitating AI adoption. However, the study also highlights certain limitations, including geographic and methodological constraints. Future investigations should broaden their scope to encompass various contexts, utilize longitudinal methodologies, and integrate supplementary elements such as technological infrastructure and user perspectives to enhance comprehension. These insights contribute to the growing discourse on leadership, well-being, and technology integration in modern workplaces.

关键词： Training Surveys Industries Leadership Technological innovation Employment Organizations Mental health Mathematical models Artificial intelligence

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Acquiring Automation and Control Data in The Manufacturing Industry: A Systematic Review

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Procedia computer science 2023年 227卷 214-222页

作者： Andi Sama Harco Leslie Hendric Spits Warnars Harjanto Prabowo Meyliana Achmad Nizar Hidayanto Computer Science Department Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia 11480 Information System Department School of Information System Bina Nusantara University Jakarta Indonesia 11480 Faculty of Computer Science Universitas Indonesia Depok Indonesia 16424

Industry 4.0 has driven the need for Information Technology (IT) & Operational Technology (OT) convergence to modernize OT by leveraging IT. The challenges for manufacturing operations are utilizing and converting years of scattered data into valuable information integrated into the company's digital transformation strategy. This paper aims to provide a systematic literature review of current evidence in digital transformation for acquiring automation & control technologies data in manufacturing operations, such as programmable Logic Controller (PLC), Supervisory Control and Data Acquisition (SCADA), Distributed Control System (DCS), and Manufacturing Execution System (MES), for analytics purposes and identify the current trends and best practices in this area. The findings cover current information on influential researchers, published journals, research trends, industries, industry types, types of analytics applications, methods, and frameworks for acquiring automation & control technologies data from the legacy OT infrastructure.

关键词： Industry 4.0 Operational Technology PLC SCADA MES Data Acquisition IT/OT convergence Manufacturing

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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...

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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

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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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The University's Integrated ICT Infrastructure: UTAUT2 Implementation

The University's Integrated ICT Infrastructure: UTAUT2 Imple...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Komputerio Akbar Arief Ramadhan Edi Abdurachman Agung Trisetyarso Muhammad Zarlis Computer Science Department BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia

Post-pandemic and globalization have accelerated the implementation of Industry 4.0, aided by rapid technological and information advancements. Industry 4.0 has significant consequences for all institutions in developing ICT infrastructure, one of which is educational institutions. Despite the enthusiasm in all educational institutions for using ICT to improve teaching and learning, particularly in universities, integrating technology is becoming problematic. Integrating ICT infrastructure in universities is a multidimensional process involving technology and curriculum, pedagogy, institutional readiness, lecturer competence, and long-term financial planning. Because of this, it is essential to explain the adoption of technology within the scope of this study to make the most of investments made in the integrated ICT infrastructure of the university using the UTAUT2 method.

关键词： Training Seminars Globalization Pricing Information and communication technology Fourth Industrial Revolution Behavioral sciences

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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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Prototype UX Design: Mobile Augmented Reality Application based on Gamification for Cultural Heritage Tourism

Prototype UX Design: Mobile Augmented Reality Application ba...

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HCI and UX Conference in Indonesia (CHIuXiD), International

作者： Siti Elda Hiererra Meyliana Arief Ramadhan Fredy Purnomo Computer Science Department BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665476652

This study aims to identify the processes of UX designing a mobile application, Augmented Reality based on Gamification for Cultural Heritage Tourism, and to provide the prototype design and the testing result; this research employs the Design Thinking Method to design the prototype and uses the System Usability Scale (SUS) to identify and validate the testing result; the author establishes two research questions for this study, then explains the answers through a clear description of the stages of making a prototype which consists of the empathize, define, ideate, prototype, and testing stages, then to find out the testing results, the author uses the SUS method, scoring 88.5 in the excellent and acceptable categories; the originality of the research, this study provides the Design Thinking method to investigate the process of UX designing and the SUS Score method to calculate the resting result, helps build a complete understanding of developing the prototype and validation through testing comprehensively.

关键词： Human computer interaction Prototypes Mobile applications Cultural differences Usability Interviews Augmented reality

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Prediction of DHF (Dengue Hemorrhagic Fever) Severity Using Random Forest, KNN, Decision Tree and Naïve Bayes 7

Prediction of DHF (Dengue Hemorrhagic Fever) Severity Using ...

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7th IEEE International Conference on Information Technology and Digital Applications, ICITDA 2022

作者： Pohan, Husni Iskandar Suparta, Wayan Heryadi, Yaya Wibowo, Antoni Lukas Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Institut Teknologi Nasional Yogyakarta Department of Electrical Engineering Yogyakarta55281 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta12930 Indonesia

ISBN: (数字)9781665461368

ISBN: (纸本)9781665461368

Dengue Hemorrhagic Fever is an acute viral infectious disease caused by the dengue virus. Transmitted through the bite of Aedes Mosquitoes and divided into 4 severity. Severity 1 and 2 are characterized by a decrease in platelet levels below normal and there is a change in hemoconcentration, while Severity 3 and 4 are included in Dengue Shock Syndrome (DSS). The purpose of the study was to classify the severity of dengue, based on age, gender, platelets, body temperature, bleeding, and also doctor's recommendation. The advancement of information technology is the reason for the development of medical science, especially with artificial intelligence. Abundant medical data becomes a source that can be processed in improving the quality of medical information. The method chosen is Random Forest, kNN, Decision Tree and Naïve Bayes. The dataset used is the medical records of all DHF patients for 30 months in the period Jan 2019 to June 2021, at the Nadhifa Al Ghiffari Clinic in Bojongsoang District, Bandung Regency. © 2022 IEEE.

关键词： Decision trees

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Density Based Spatial Clustering of Applications with Noise and Sentence Bert Embedding for Indonesian Utterance Clustering

Density Based Spatial Clustering of Applications with Noise ...

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

作者： Muhammad Fikri Hasani Yaya Heryadi Yulyani Arifin Lukas Wayan Suparta Computer Science Department BINUS Graduate Program Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance.

关键词： computer science Art Text categorization Clustering algorithms Oral communication Chatbots Classification algorithms

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Stemming Algorithm for the Indonesian Language: A Scientometric View

Stemming Algorithm for the Indonesian Language: A Scientomet...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Aries Maesya Arief Ramadhan Edi Abdurachman Agung Trisetyarso Muhammad Zarlis Computer Science Department BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia

Stemming is the process of cutting affixes, both prefixes and suffixes from a term to get the root of the word that has affixes. Stemming can be done in any language, especially in Indonesia Language. Indonesian which is rooted in Malay and Sanskrit has a bigger influence on Language. The algorithm of stemming that has developed is the one that is based on the dictionary of a root word. This kind of algorithm was started by Nazief- Adriani and it grew up to be Confix Stripping (CS), and then was perfected to be Enhanced Confix Stripping (ECS). A scientometric analysis is a mathematical method used to identify academic publications related to citations and scientific matters and is intended for use in libraries or other fields. In this research, the publication of which using stemming algorithm selected and collected using keywords related to Nazief Adriani and ECS. The publications are collected from *** (an online database for advance scientific research). From those publication we did the analysis bibliometric help by the VOS Viewer application using the analysis technique of co-authorship and citation technique, and all done. The result are, from 310 publications that have keywords Nazief Adriani and 119 publications containing the keyword ECS, it was found that the owning documents will be the reference sources of the next research in which the most citation and co-authorship are found in Indonesia.

关键词： Text analysis Dictionaries Databases Citation analysis Natural language processing Libraries Standards

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