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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis 6

Mapping the Scientific Landscape of Integrated Scheduling To...

引用

6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Putri Ardyanti, Anak Agung Ayu Xie, Zhiqiang Karnyoto, Andrea Stevens Pardamean, Bens Estiyanti, Ni Made School of Computer Science and Technology Harbin University of Science and Technology Harbin150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, computer science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions. © 2025 The Authors. Published by Elsevier B.V.

关键词： Job shop scheduling

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Restaurant Customer Feedback Sentiment Analysis using Aspect Embedding Long Short-term Memory Model

Restaurant Customer Feedback Sentiment Analysis using Aspect...

引用

2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Heryadi, Yaya Wijanarko, Bambang Dwi Fitria Murad, Dina Tho, Cuk Hashimoto, Kiyota BINUS Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Information Systems Department Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket83120 Thailand

ISBN: (纸本)9798350320954

In the past ten years, there has been a proliferation of unstructured textual data from a variety of fields, allowing for the analysis of the sentiment polarity of the text authors in each of those datasets. To analyze sentiment polarity from unstructured data, machine learning tasks such as aspect-based sentiment analysis are used. The public dataset of restaurant patron feedback was used as the input for this study's empirical results of aspect-based sentiment analysis utilizing the Aspect Embedding Long Short-Term Memory model. The primary experiment's results show that the aspect embedding long short-term memory model can predict aspect sentiment polarization from customer feedback to restaurant service and aspect text representation (aspect text embedding) as input with excellent performance. For example, the Aspect Embedding Long Short-term Memory model, which uses ReLU as its activation function in the Dense layer tends to achieve higher performance (0.97 average accuracies) than the same model equipped with Sigmoid, and Tanh functions which achieve 0.954 and 0.956 average training accuracy respectively. © 2023 IEEE.

关键词： Sentiment analysis

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The Fuzzy-AHP Decision Model for Managing Smart City: An Object-Oriented Model for Bibliometric Based Literature Review

The Fuzzy-AHP Decision Model for Managing Smart City: An Obj...

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

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Operating a smart city is intricate and requires comprehensive management. Implementing a decision model is one effective and efficient method to address this complexity. This study seeks to systematically review articles to see the trends in the utilization of fuzzy logic methods and their combinations, particularly in a decision model that supports smart city operations. Through four stages, the study not only presents the bibliometric-based review findings to address specific inquiries but also produces an object-oriented model for review purposes. Based on 131 articles, the research trend indicates a linear growth in studies focusing on smart cities and decision models, with a particular emphasis on incorporating fuzzy logic models. Additionally, according to 9 deep-reviewed articles, the analytic hierarchy process (AHP) emerges as a popular method often combined with the fuzzy logic method. As well, the object-oriented model for review purposes is developed with 19 interrelated objects. It is the main contribution of the study.

关键词： Fuzzy logic Analytical models Smart cities Reviews Object oriented modeling Bibliographies Bibliometrics Focusing Market research Complexity theory

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Implementation of Light Gradient Boosting Machine (LGBM) for Customer Services Feedback Classification

Implementation of Light Gradient Boosting Machine (LGBM) for...

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International Conference on Artificial Intelligence and Data sciences (AiDAS)

作者： Maria Susan Anggreainy Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331528553

ISBN: (纸本)9798331528560

Customer complaints are an important aspect in the business world. Every company must face complaints from its customers, whether related to unsatisfactory products, services or experiences. Customer complaints are a sign of unmet needs or expectations. A customer service person is tasked with serving questions asked by customers or consumers. The purpose of this research is to classify types of complaints to help customer service. The classification carried out is in binary form, namely disputed or not. This research compares several machine learning algorithms for classification of customer questions into appropriate categories for more efficient responses. Based on evaluation metrics, the F1 Score light gradient boosting machine (LGBM) is the best model compared to Multinomial Naive Bayes (MNB), Logistic Regression (LR), SVM (Support Vector Machine), and EXTC (Extra Tree Classifier).

关键词： Support vector machines Measurement Logistic regression Machine learning algorithms Customer services Companies Learning (artificial intelligence) Boosting Telephone sets Random forests

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Object Driven Fuzzy Decision Model for Evaluating the Cryptocurrency's Performance

Object Driven Fuzzy Decision Model for Evaluating the Crypto...

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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

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

The assessment of cryptocurrency performance has been subject to criticism due to the lack of a standardized approach, leaving the evaluation process without clear guidance. In this research, an innovative object-driven decision model based on fuzzy logic (OFDM) conception was developed to systematically analyze cryptocurrency performance. Object-oriented operated as the main method in analyzing and designing the model. In this context, the model elements deeply observed and functioned to become fundamental thinking for constructing the model. Then, fuzzy logic with curve membership function form was also used as the primary method to avoid the bias value of three key parameters; trend line (and then used to see the slope distance), price consistency, and transaction volume consistency. The constructed model is a novel decision model that was successfully constructed for assessing the determined cryptocurrency performance. Utilizing six months of real data from three major cryptocurrencies (Bitcoin, Ethereum, and Dogecoin), the model conclusively demonstrates that Bitcoin exhibits the strongest performance, scoring 36.39 performance points.

关键词： Fuzzy logic Analytical models Object oriented modeling OFDM Bitcoin Market research Data models Context modeling

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Detection of Employee Fatigue Based on Image Processing Using Deep Learning Model

Detection of Employee Fatigue Based on Image Processing Usin...

引用

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

作者： Michael Ethan I Gede Putra Kusuma Computer Science Department Binus Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

Fatigue in workplace is a common thing shared by all employees. Continuous exposure of fatigue could lead to negative productivity for companies. Current research on fatigue detection mostly focused to detect fatigues from a single 2D image data, meanwhile research on fatigue detection using each frames from video data is needed for better detection for fatigue process. The main advantage of using each timesteps of frames from video data which makes the model is able to predict long term dependency of fatigue person. To solve this problem, a deep learning model is proposed that could detect employee fatigue based on image processing using video data. Three models are used to train data using Convolutional Neural Network (CNN), which are Time Distributed CNN LSTM, 3D CNN, and 3D CNN LSTM. Out of those three models, the best model to detect fatigue person from video data is Time Distributed CNN Model with F-1 Score value of 0.77 and Accuracy Score of 0.76 for testing data. The model that gives best inference time is also Time Distributed CNN Model with the average value of 382 miliseconds to detect fatigues from 58 testing data with each of the data has duration of 3 seconds and 12 frames.

关键词： Deep learning Three-dimensional displays Image processing Computational modeling Distributed databases Fatigue Data models Convolutional neural networks Long short term memory Testing

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Aspect-based Sentiment Analysis using Long Short-term Memory Model for Leveraging Restaurant Service Management

Aspect-based Sentiment Analysis using Long Short-term Memory...

引用

2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

ISBN: (纸本)9798350320954

In general, the hospitality industry has been acknowledged as a major sector that gives a high contribution to economic development in many countries including Indonesia. For that reason, many initiatives have been implemented to help the growth of the hospitality industry in many countries including Indonesia to rebound from the harsh impact of the Covid-19 Pandemic. One such initiative is improving restaurant services as the main sector of the hospitality industry. This paper presents empirical results of sentiment analysis as a means to assess the quality of restaurant services as the first step to improving service quality. In particular, this study explores the aspect-based sentiment analysis method to identify some aspects of restaurant service which need improvement by learning the polarity of customers toward the restaurant services without having to meet the customers directly. By using the aspect-based sentiment analysis method, the customer sentiments comprising opinions, sentiments, evaluations, attitudes, and emotions from restaurant service can be analyzed using customers' online reviews as input. The main experiment findings showed that the Long Short-term Memory model can achieve high performance in predicting aspect polarization in restaurant service reviews. Other findings suggest that Sigmoid as an activation function achieved 0.97 average training accuracy and 0.69 average testing accuracy giving a better performance to the model in comparison to ReLU, Tanh, and ELU activation functions. © 2023 IEEE.

关键词： Sentiment analysis

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Twitter-based Sentiment Analysis for Indonesian Drug Products using Supervised Feature Engineering 5

Twitter-based Sentiment Analysis for Indonesian Drug Product...

引用

5th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2022

作者： Purwandari, Kartika Jeremy, Nicholaus Hendrik Arisaputra, Panji Philip, Samuel Suhartono, Derwin School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665455121

Social media communications offer valuable feedback to firms about their products. Twitter users share their opinions about e-commerce products on social networking sites. This paper reports a study in sentiment classification for Indonesian drug products for automatically classifying Twitter data as expressing positive or negative sentiments or opinions. The study investigates the effectiveness of using a Decision Tree, Support Vector Machine, Random Forest, Na rve Bayes, and Logistic Regression, along with the term frequency-inverse document frequency used to classify 531 tweets into recommended (positive sentiment) and not recommended (negative sentiment). Among all traditional machine learning models, the highest average accuracy value is 88.6 in the RF model and the lowest average accuracy value is 80.2 when using the LR model. Judging from the average accuracy obtained, the DT and RF model used has a considerable performance © 2022 IEEE.

关键词： Sentiment analysis

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Virality Prediction on Twitter Using Combined CNN and BERT Models

Virality Prediction on Twitter Using Combined CNN and BERT M...

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

作者： Hilarius Ryan Auxilio Benedecas Abba Suganda Girsang Computer Science Department Binus Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

Nowadays, social media serves as a platform for sharing and receiving information. One indicator of the importance of information is its virality. Virality is an interesting topic in the world of Natural Language Processing (NLP), including the prediction of virality and identifying features that influence it. Research in this area has utilized Bidirectional Encoder Representations from Transformers (BERT) models and yielded promising results. Previous studies aimed to predict virality based on specific features. This research aims to predict virality by combining the abilities of Convolutional Neural Network (CNN) and BERT models. The BERT model transforms textual data into numerical and adds new features, while the CNN model excels in numerical classification. Data for this study is sourced from Twitter and in Indonesian language. The proposed model demonstrates improvements compared to the model from previous research. Specifically, it shows a 7.1% increase in precision, a 9.9% increase in recall, a 3.8% increase in accuracy, and an 8.1% increase in F1-Score compared to the previous model. These findings contribute to future research on similar topics, particularly in leveraging multiple models for prediction tasks. Overall, the study provides insights into effectively utilizing the strengths of various models and combining them in prediction tasks.

关键词： Measurement Social networking (online) Blogs Bidirectional control Predictive models Encoding Data models Natural language processing Numerical models Convolutional neural networks

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Software Quality Assessment Methods and Standards in Weld Defect Detection for Shipbuilding

Software Quality Assessment Methods and Standards in Weld De...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Yonky Pernando Ford Lumban Gaol Haryono Soeparno Yulyani Arifin Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Shipbuilder safety and performance, structural integrity of welded joints in shipbuilding is of utmost importance. Traditional manual inspection methods to detect any potential defect can be time consuming and susceptible to human errors; alternative inspection systems have proven more cost effective and accurate solutions than manual approaches for defect identification. This paper investigates how advanced software quality assessment standards and machine learning techniques such as Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO) object identification framework can enhance weld defect detection. Research undertaken for this thesis addresses three core questions. 1) Which standards provide effective software quality assessments used for weld defect detection systems?; and 2) In what ways will they impact efficiency and reliability. How will implementation of these standards increase accuracy in manufacturing processes? Integrating high-quality annotated datasets, optimizing joint preparation and parameter settings, and using cloud solutions for computing needs can enable industries to achieve more reliable, efficient, and scalable defect detection. This proactive approach not only enhances immediate product quality, but also contributes to long-term durability and performance, decreasing maintenance costs and increasing overall reliability for steel plates used for marine applications. Furthermore, evidence has revealed that adopting software quality assessment standards in weld defect detection significantly enhances structural integrity as well as operational efficiency of shipbuilding operations.

关键词： YOLO Costs Welding Software quality Manuals Inspection Software reliability Convolutional neural networks Standards Defect detection

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