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Hate Speech Text Classification Using Long Short-Term Memory (LSTM) 4

Hate Speech Text Classification Using Long Short-Term Memory...

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4th IEEE International Conference of computer science and Information Technology, ICOSNIKOM 2022

作者： Ariwibowo, Suminar Girsang, Abba Suganda Diana Bina Nusantara University Binus Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350399073

This paper aims to build hate speech text classification model by applying a combination of LSTM and FastText. The features of hate speech & non-hate speech, target hate speech, and categories of the hate speech. Dataset of those features taken from previous research by Okky Ibrohim. FastText word embeddings is used for formation of text vectors that will be used as input of the LSTM training model. The evaluation results obtained by getting the level of accuracy using confusion matrix. The accuracy value of text classification in this study is 83.52% on the classification of hate speech, 78.44% on the classification of target labels for hate speech, 82.75% on the classification of the label for category of hate speech. © 2022 IEEE.

关键词： Long short-term memory

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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...

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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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News Classification Based On News Headline Using SVC Classifier 16

News Classification Based On News Headline Using SVC Classif...

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16th International Conference on Telecommunication Systems Services and Applications, TSSA 2022

作者： Leonard, Goldius Sisnadi, Fukriandy Wardhana, Nicholas Vigo Aziz Al-Ghofari, Muhammad Abdul Girsang, Abba Suganda School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665455947

News gives new insight and information from all over the world. News has many categories, such as politic, economy, science, and other common news categories. Every news will have their own category based on its content. The classification of news is usually done manually by inputting the category during the news posting. Some of the categories may be inputted incorrectly. The news classifier can be the solution for problem, but the news classifications out there are usually based on the news content. The classifier will receive the word vector inputs that are taken from the news content and try to classify it into one of certain categories. Unfortunately, news contents can be longer and harder to be processed rather than processing the news headline. The news headline is shorter and packs a decent information for the classifier to find out what category it is. Besides the news headline usage, the classifier also needs to be chosen correctly. In this paper, the SVC model will be tested using the news headline data to classify the news and compare with several other models, such as Linear Regression, Multinomial Naive Bayes, Decision Tree, and Random Forest. The common variables to be compared are the accuracy, recall, and precision to evaluate the SVC model. © 2022 IEEE.

关键词： Classification (of information)

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Trends of Digital Transformation in the Property Management Industry: A Systematic Literature Review

Trends of Digital Transformation in the Property Management ...

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2023 IEEE International Conference on Smart Information Systems and Technologies, SIST 2023

作者： Saputra, Prasetya Cahya Prabowo, Harjanto Ramadhan, Arief Madyatmadja, Evaristus Didik Binus University Binus Graduate Program-Computer Science Computer Science Department Jakarta Indonesia Binus Business School Doctor of Research in Management Binus University Management Department Jakarta Indonesia Telkom University Bandung Indonesia School of Information System Binus University Jakarta Indonesia

ISBN: (纸本)9798350335040

After the Covid 19 pandemic, the property management industry is slowly starting to revive. Even though the occupancy rate has not returned to normal like the Covid19 pandemic, its growth is starting to move in a positive direction. The Covid19 pandemic has provided many lessons for all stakeholders in the property management industry. Digital transformation has become a part that has received important attention in the property management industry worldwide. With digital transformation in the property management industry, all stakeholders are expected to receive various benefits, directly or indirectly. The implementation of digital transformation will involve 6 components, namely: Strategy, Organization, Culture, Customer, People, and Technology. The implementation of digital transformation in the property management industry will require support from many parties, not only internal parties but also external parties. Technological developments, IoT, and demands from residents and property owners are challenges that must be answered by property managers. This systematic literature review will examine articles that have been published in the Scopus database regarding the development of digital transformation in the property management industry from various countries. The purpose of this SLR is to be able to see how the digital transformation trend and the application of its components are in the current property management industry. © 2023 IEEE.

关键词： Apartment Property Management Systematic Literature Review Transformation Digital

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Smart Services Implementation in Smart Cities: A Comprehensive Review of State-of-the-art Technologies 23

Smart Services Implementation in Smart Cities: A Comprehensi...

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5th International Conference on Management science and Industrial Engineering, MSIE 2023

作者： Arin, Ikrar Adinata Supangkat, Suhono Harso Gaol, Ford Lumban Ranti, Benny Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Indonesia Smart City and Community Innovation Center Institut Teknologi Bandung Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Indonesia

ISBN: (纸本)9798400708381

This paper discusses comprehensively the advanced technologies used in the implementation of smart services, which are key elements of a smart city. Smart cities are becoming eco-friendly, livable, and sustainable to deliver essential services to their citizens by leveraging internet and communication technology advancements. Starting from a short literature review about smart cities and smart services, a recent trend in the technology used to implement smart services is collected. Smart service can be defined as a service conveyed using intelligent products that possess awareness and connectivity. All kinds of smart services can be connected in smart cities to offer citizens comprehensive solutions. Pre-emptive services, self-services, or highly interactive services, for example, have the potential to be implemented using current technology such as the internet of things, artificial intelligence, cloud computing, big data, machine learning, machine-to-machine communications, and the digital twin. Therefore, there is scope for research and implementation of other technology for smart services in the future. © 2023 ACM.

关键词： Smart city

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Optimizing Government Funding Allocation with Ensemble Machine Learning Models

Optimizing Government Funding Allocation with Ensemble Machi...

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

作者： Budi Prasetyo Suharjito Computer Science Department Binus Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Barat Indonesia

ISBN: (数字)9798331529376

ISBN: (纸本)9798331529383

Given the limited availability of state funds, managing state finances in an effective, efficient, and prudent manner is essential. High demand for additional state funds without sound justification can lead to inefficiencies and poor budget execution. Therefore, decisions regarding the allocation of additional funds should be made selectively and based on historical data reflecting the quality of the work unit in budget management. This research aims to develop a machine learning application to assist decision-makers in recommending additional funding percentages. Utilizing the Budget Implementation Quality Indicator (IKPA) data, we performed feature selection using Principal Component Analysis (PCA), resulting in three selected features. These features were then used to build models with base models (Decision Tree Regression, K-Nearest Neighbor Regression) and ensemble learning methods (Stacking, Bagging, Random Forest, Boosting: AdaBoost, XGBoost, LightGBM). After to-Fold Cross-Validation and hyperparameter tuning, LightGBM demonstrated the lowest error rate with a Root Mean Square Error (RMSE) of 0.0646 and a Mean Absolute Error (MAE) of 0.0520, outperforming XGBoost in predicting additional fund allocation proportions. The application supports informed and accountable financial decision-making, promoting efficient and prudent national financial management. By comparing base models and ensemble techniques, this research provides critical insights into machine learning applications in financial management, driving methodological innovation and advancing the field.

关键词： Biological system modeling Government Machine learning Financial management Data models Resource management Ensemble learning Root mean square Regression tree analysis Principal component analysis

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Performance Analysis of Distributed Database System in Cloud Computing Environment 9

Performance Analysis of Distributed Database System in Cloud...

引用

9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Prasetio, Nicholas Ferdinand, Daniel Wijaya, Brandon Christopher Anggreainy, Maria Susan Kumiawan, Afdhal Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program School of Computer Science Jakarta11530 Indonesia

ISBN: (纸本)9798350370126

This research discusses the performance evaluation of distributed database systems in a cloud computing environment Cloud computing environments allow data and applications to be stored and deployed on infrastructure located in different parts of the world. However, the use of distributed database systems in cloud computing environments can cause performance issues, such as complex data access and factors such as network latency, security, and scalability that affect system performance. Therefore, performance evaluation of distributed database systems is necessary to ensure effective data management across the infrastructure. The purpose of this research is to measure and understand the performance of distributed database systems in a cloud computing environment. This is important because proper performance evaluation is needed to ensure distributed database systems can operate effectively and efficiently in such environments. This research will analyze the features of distributed database systems, factors that affect performance, how to measure and compare system performance, and how to improve system performance. Analyzing the performance of distributed database systems in a cloud computing environment can help users choose the most appropriate and efficient cloud computing platform for their business needs and improve operational efficiency. © 2023 IEEE.

关键词： Distributed database systems

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White rice stem borer pest detection system using image-based convolution neural network 9th

White rice stem borer pest detection system using image-base...

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

作者： Saufi, Akhmad Suharjito Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Preventing agricultural resource loss caused by pests remains a crucial issue. While technological advancements are being achieved, the current agricultural management methods and equipment have yet to meet the required level for precise pest control, a huge portion of the pest population analysis process is still conducted manually. As a solution to this issue, the development of a White Rice Stem Borer pest detection system has been conducted by applying Convolutional Neural Network (CNN) technology to calculate the pest population count at the research location. This system has been specifically designed to detect the White Rice Stem Borer using available traps. The method involves training data from a direct dataset obtained from the field, categorized into two positive and negative classes of the White Stem Borer pests. Six models have been trained from this dataset, utilizing two different architectures. Out of the six trained models, four showed potential overfitting, one exhibited underfitting, and one model demonstrated optimal results. The highest accuracy in image detection achieved by the most optimal CNN model was 97.35%, with a training accuracy of 98.54%. This best-performing model utilized an architecture with three Convolution layers, 50 Epochs, and an automatic data split with an 80:20 training-validation data ratio. From the research findings, it is concluded that this study can assist in automatically analyzing the quantity of White Stem Borer pests in a specific area without directly counting the number of pests from existing traps. However, the study still encounters a limitation-the detection process still requires substantial server resources and cannot be directly processed on the Raspberry PI device installed in the trap. Consequently, the detection relies on transmitting image data from the field device to the server before the detection process can occur. © 2024 The Authors.

关键词： Convolutional neural networks

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A Hybrid Inference Engine to Predict Uncertain Sugar Content for Sugar Industry 9th

A Hybrid Inference Engine to Predict Uncertain Sugar Content...

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

作者： Asrol, Muhammad Napthaleni Utama, Ditdit Nugeraha Suharjito Harito, Christian Syafrudin, Muhammad Industrial Engineering Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Department of Artificial Intelligence and Data Science Sejong University Seoul05006 Korea Republic of

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. © 2024 The Authors.

关键词： Prediction models

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Adoption of Digital Marketing Toward Digital Transformation in Indonesian Micro- and Small-Sized Enterprises 8

Adoption of Digital Marketing Toward Digital Transformation ...

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8th International Conference on Business and Industrial Research, ICBIR 2023

作者： Ananda, Artha Sejati Murwani, Ina Agustini Tamara, Dewi Ibrahim, Irma Irawati Binus Business School Master Program Bina Nusantara University Management Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350399646

Digital transformation in various industries has led to considerable changes in organizational strategic behavior. Micro, small, and medium enterprises (MSMEs) in the global sense, have been under pressure to adopt information and communication technology to increase their performance. Digital marketing has started to be recognized as one potential enabler of digital transformation. Managerial practices and academic literature provide limited guidance on how digital marketing can be used as an enabler of digital transformation across enterprises. This is especially in dearth among the study of MSMEs, particularly in emerging countries such as Indonesia where MSMEs have significant major roles in economic growth and workforce absorption. Besides, only a small portion of Indonesian MSMEs has embraced digital technology. Using the case study method, this paper finds that digital marketing adoption of the MSMEs may serve as the first-level enabler of digital transformation by allowing the MSMEs to enhance its dynamic capabilities. This paper contributes to the area of digital transformation through a marketing perspective, something that is novel in the context of MSMEs. For managers, the results of the study provide actionable guidelines for the MSMEs on how to drive digital transformation with digital marketing as the entry-level of adoption. © 2023 IEEE.

关键词： Marketing

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