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

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

引用

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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Indonesian Tweet Emotion Detection Using IndoBERT

Indonesian Tweet Emotion Detection Using IndoBERT

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International Conference on Information Management and Technology (ICIMTech)

作者： Nikita Ananda Putri Masaling Ricky Reynardo Siswanto Abba Suganda Girsang Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

This study investigates emotion detection on tweets written in the Indonesian language, using the IndoBERT model that has been trained for 50 epochs. The examination conducted in this study provides a comprehensive analysis of the performance dynamics shown by the model. The assessment results provide an accuracy score of 0.74, indicating the model's proficiency in identifying sentiments within the collection of tweets. By using assessment criteria such as accuracy, recall, and the F1-score, the model's comprehensive analytical ability is validated, emphasizing its effectiveness in deciphering various emotional circumstances. The accuracy, recall, and F1-score of the model, with average values of 0.79, 0.78, and 0.78 respectively, combined demonstrate its resilience in accurately capturing subtle emotional subtleties. This study enhances our comprehension of language model applications in the field of emotional intelligence by exploring the complexities of sentiment analysis. It emphasizes the practical value of these applications in real-world scenarios.

关键词： Analytical models Sentiment analysis Emotion recognition Accuracy Prevention and mitigation Stability criteria Ecosystems Robustness Information management Resilience

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

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

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

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

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

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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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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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Synthetic Batik Pattern Generator Using Wasserstein Generative Adversarial Network with Gradient Penalty 6

Synthetic Batik Pattern Generator Using Wasserstein Generati...

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6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023

作者： Andriadi, Kus Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Yogyakarta55281 Indonesia

ISBN: (纸本)9798350358346

Batik is an Indonesian world cultural heritage. Batik consists of many kinds of patterns depending on where the batik comes from, Batik-making techniques continue to develop along with technology development. Among the batik making techniques that are widely used are hand-written, stamping, and printing. Batik motifs have been widely used as research material, especially in the field of artificial intelligence. The diverse appearance of batik motifs has attracted many researchers to carry out research on making synthetic batik patterns, one of which uses a Generative Adversarial Network. This paper presents a synthetic batik pattern model based on the Wasserstein Generative Adversarial Network with Gradient Penalty. This model has been proven to create new synthetic batik patterns quite well and almost identical with images provided in the dataset, with the notes if the dataset provided is large. © 2023 IEEE.

关键词： Generative adversarial networks

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Aspect-based Sentiment Analysis in Tourism Industry for Tourism Recommender System 5

Aspect-based Sentiment Analysis in Tourism Industry for Tour...

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

作者： Huda, Choirul Heryadi, Yaya Lukas Budiharto, Widodo Bina Nusantara University Doctor of Computer Science Graduate Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Jakarta Indonesia

ISBN: (纸本)9781665455121

The improvement of some aspects in tourism industry needs further study through aspect-based sentiment analysis based on tourist experience. The aim of this study is presenting the empiric results of aspect-based sentiment analysis to extract some useful aspects of services for leveraging tourism industry development based on user's feedback. The study uses customer review data from TripAdvisor website for developing classification model using machine learning algorithms including Decision Tree (DT), Random Forest Classifier (RFC), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) models. Each machine learning model under study is trained using K-Fold Cross Validation. The empiric results found that accuracy of RFC achieved the highest result (99.5%) followed by XGBoost (87.3%), DT (82.9%), and SVM (80.5%). The extracted meaningful aspects from customer feedback give many valuable insights for service quality improvement in tourism industry in the effort to strengthening the rising-up of sustainability economic growth through tourism industry. © 2022 IEEE.

关键词： Decision trees

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The Learning Outcomes Achievement and User Experience Analysis of a Cryptography Education Web Game

The Learning Outcomes Achievement and User Experience Analys...

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HCI and UX Conference in Indonesia (CHIuXiD), International

作者： Helmi Yusuf Susanto Yulyani Arifin Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350380439

ISBN: (纸本)9798350380446

The rising threat of cybercrimes in Indonesia, including a significant breach of the national data center, reveals the urgent need for effective cybersecurity education. Cryptography, a key component of cybersecurity, is often difficult for beginners to grasp. To address this, we developed an interactive web game that blends puzzle-solving with a visual novel to make cryptographic concepts more accessible. This study analyzes the game’s user experience (UX) using the ARCS model—Attention, Relevance, Confidence, Satisfaction—and assesses learning outcomes by comparing pre-test and post-test results. The findings show significant improvements in participants’ understanding and high levels of engagement, demonstrating a strong correlation between positive user experience and enhanced learning outcomes. However, some aspects of the educational content still need refinement. Future updates will offer multilingual support and personalized learning paths, with pre-and post-tests targeting specific cryptography and cybersecurity sub concepts, and analysis of UX features through targeted surveys and in-game analytics to enhance learning experiences.

关键词： Surveys Visualization Analytical models Data centers Correlation Education Games User experience Multilingual Cryptography

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Applications of Artificial Intelligence and Big Data for Strengthening the Role of Intelligence in Human Security 8

Applications of Artificial Intelligence and Big Data for Str...

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8th IEEE International Conference on Computing, Engineering and Design, ICCED 2022

作者： Mubarok, Ahmad Shahrul Susandi, Armi Mantoro, Teddy State Intelligence College Medical Intelligence Post-Graduate Program Bogor Indonesia Sampoerna University Media-Tech Lab Departement of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665453899

The state, through the government, should ensure national security and stability through the policies taken. For this reason, fast and accurate information and data are needed to form the basis for policymaking. Intelligence actors have an essential role in gathering information to identify and prevent threats within and outside the country. Technological advances affect changes in threats and increase the classification of these threats. Covid-19 is a real example of the threat in this era because almost all countries feel its impact. In addition to affecting health, the Covid-19 pandemic also impacts the global economic sector. The economic impact is felt by small and medium industries, goods and services products, and also the tourism sector. The method that is used in this study is a literature review from various national and international journals. AI and Big Data can be used to track viruses in real time, collect adequate public health data, and integrate vaccine use. Also, AI and Big Data can provide local decision and policy-makers with informed, evidence-based predictions. the threat of the Covid-19 pandemic and other infectious diseases that disrupt public health and have a broad impact must be anticipated and addressed in order to achieve the safety and existence of the nation and state. The author wants to emphasize the importance of the role of intelligence in anticipating threats from the visible to the invisible. © 2022 IEEE.

关键词： Artificial intelligence

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