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Informatica (Slovenia) 2024年第20期48卷 71-80页

作者： Alandeta, Vito Rozaan Suhartono, Derwin Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia

This study proposes a gender classification method for Twitter data using a hybrid XLNet-fastText model. The objective is to enhance gender classification accuracy by leveraging the contextual understanding of XLNet and the semantic richness of fastText embeddings. Computational experiments were conducted on a dataset derived from Kaggle, focusing on user account descriptions and Twitter feeds. The proposed method achieved an accuracy of 71.4%, precision of 77%, recall of 60.9%, and F1-score of 68% for gender classification based on Twitter feeds. For user account descriptions, the scores were 72.4% for accuracy, 75.1% for precision, 63.24% for recall, and 68% for F1-score. These results outperform the baseline XLNet model and demonstrate the potential of the XLNet-fastText combination in improving text-based classification tasks. Our approach highlights a viable pathway to enhance gender classification on social media platforms, suggesting further improvements through multi-modal data integration. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Tweets

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Enhancing Customer Segmentation Insights by using RFM + Discount Proportion Model with Clustering Algorithms

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International Journal of Advanced computer science and Applications 2024年第3期15卷 902-911页

作者： Antonius, Victor Hugo Fitrianah, Devi BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta11480 Indonesia

In this digital era, the use of e-commerce has expanded and is widely adopted by society. One of the reasons why people use e-commerce platforms is because of their convenience and ease of use. However, the rapid growth of e-commerce has led to a substantial rise in transactions within the platform, involving various business entities. Therefore, it is crucial to perform customer segmentation to group them based on their purchasing behavior. The implementation of data mining techniques, such as clustering, is highly beneficial in this case. Clustering helps process datasets and transform them into useful information. In this study, transaction data obtained from one of the e-commerce stores, i.e. MurahJaya888 and followed by analysis using various clustering methods such as K-means, K-medoids, Fuzzy c-means, and Mini-batch k-means. We also proposed a new model that will become the attributes cluster, namely, RFM + DP (Discount Proportion). The Discount Proportion Rate will provide more insights for customer segmentation as it helps understand purchasing behavior that is more responsive to discount utilization. Implementing these four clustering methods with RFM + DP model resulted in four clusters based on the optimal elbow method. Furthermore, the evaluation and performance metrics for each clustering algorithm indicate that Mini Batch K-Means achieved the highest silhouette score of 0.50. Meanwhile, K-Means obtained the highest CH index value compared to the other algorithms, which was 1056. © (2024), (science and Information Organization). All Rights Reserved.

关键词： Data mining

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Synonym Replacement Augmentation for Handling Data Imbalance in Personality Classification 8

Synonym Replacement Augmentation for Handling Data Imbalance...

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8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024

作者： Pradana, Rilo Chandra Suhartono, Derwin 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)9798350368970

The Myers-Briggs Type Indicator (MBTI) classification is a widely utilized instrument for personality assessment. However, it frequently encounters challenges due to imbalanced data distributions across personality dimensions. It is paramount to address this issue to enhance the accuracy and reliability of personality predictions. Most current research in this field is focused on data balancing techniques, such as oversampling and undersampling, which have been demonstrated to enhance model performance. Nevertheless, there is a dearth of research exploring text augmentation methods, particularly synonym replacement, for this purpose. This study examines the efficacy of synonym replacement as a data augmentation technique for MBTI classification. Experiments are conducted with varying levels of synonym replacement (10%, 30%, 50%, 70%, and 90%) to assess its impact on model accuracy and F1 scores across the four MBTI dimensions. Our findings indicate that low levels of synonym replacement, particularly at 10% to 50%, can enhance the performance of the model in predicting MBTI dimensions. On the other hand, a higher number of replaced words in the synonym replacement augmentation can harm the model's performance. These observations suggest that synonym replacement can effectively address data imbalance in MBTI classification, although its application must be tailored to specific personality dimensions. © 2024 IEEE.

关键词： Data accuracy

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Service Oriented Smart Model for Flood Controlling 9

Service Oriented Smart Model for Flood Controlling

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

作者： Rachmat Utama, Ditdit Nugeraha Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350370126

Opening or closing dam-gate activities manually conducted in Manggarai dam to control the dam water level. The controlling action operated to avoid the flood possibility occurring in Jakarta city (the Indonesian capital). The study was conducted to develop a smart model for flood controlling based on service or called a service-oriented smart model (SOSM). The water-flow algorithm (WFA), fuzzy logic, object and service-oriented are four main methods operated in the study. The WFA is a central method to model the real water flow in the river coming from Katulampa dam (in Bogor city) until Manggarai dam (in Jakarta). The fuzzy logic functioned to simulate the dam's water level and the gate open/close decision should be decided by avoiding the bias value. The object-oriented model analysis and design approach, where the unified modelling language (UML) tools are operated to analyze and design the constructed model. Then, the service-oriented conception is used to integrate all sides in implementing the model. Finally, the constructed model can simulate the flood status in Jakarta via status value in decimal numbers with 6 numbers behind the point. © 2023 IEEE.

关键词： Floods

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Service Oriented Fuzzy Decision Model for Health Insurance Claim 5

Service Oriented Fuzzy Decision Model for Health Insurance C...

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5th International Conference on Advancement in Data science, E-Learning and Information Systems, ICADEIS 2023

作者： Utama, Ditdit Nugeraha Susanto, Sumiatie Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350303414

Insurance claim is a fascinating issue to study. A potential loss for the insurance company is major coming from this issue. Thus, many studies performed already to answer such an issue. This study takes aim to develop a computational decision model based on service technology. Four fundamental methods operated in constructing the model and its implementation in service technology. The analytical hierarchical process (AHP) and fuzzy logic methods are two methods benefited to construct the model;where the AHP used to prioritize thirteen parameters considered and the fuzzy logic with its inference capability operated to generate the decision. Object oriented is an analysis and design method to analyze and design the model implanting it in service oriented architecture (SOA). Then, SOA conception functioned to deploy the model in the service architecture. Ultimately, the suggested framework comprising three layers of service-oriented architecture (SOA), namely business process, service interface, and application, has been established, alongside the integration of eight essential services that connect these three applications. The model demonstrates simulation outcomes indicating that 31.47% of claims are categorized as low risk and have been approved, 17.64% of claims are considered moderate risk with currently pending decision status (requiring additional investigation), while 50.89% of claims are classified as high risk with also pending decision status. © 2023 IEEE computer Society. All rights reserved.

关键词： Fuzzy logic

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An Object Oriented Floating Fuzzy Decision Model for Evaluating the Student Performance 6

An Object Oriented Floating Fuzzy Decision Model for Evaluat...

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

作者： Utama, Ditdit Nugeraha Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350358346

The concept of object-oriented (OO) serves is a fundamental approach in the development of models. The stages associated with this method contribute significantly to ensuring that the resulting model is both lucid and transparent. The primary objective of the study is to create a decision model for evaluating student performance. Floating fuzzy logic (FFL) is employed as a technique to handle fluctuating data within the model. Moreover, OO conception plays a central role in analyzing, designing, and constructing the model through the utilization of four distinct types of Unified Modeling Language (UML) diagrams: object, activity, state-machine, and sequence diagrams. The model itself is crafted using the Python programming language and executed in the Google Colab platform. Additionally, this model has the capability to simulate changes in students' performance on a semester-by -semester basis, exhibiting a variance of 15 % when compared to the conventional fuzzy logic model. © 2023 IEEE.

关键词： Fuzzy logic

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A Smart Fuzzy Decision Model for Anticipating Household Expenditure Shifts in Response to Fluctuating Fuel Prices in Indonesia 10

A Smart Fuzzy Decision Model for Anticipating Household Expe...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Fericia, Frans Gunawan, Jeremy Ansellino Utama, Ditdit Nugeraha BINUS Graduate Program Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339543

Analyzing the impact of fuel price increases in Indonesia is crucial for making informed strategic decisions. This paper presents a smart fuzzy decision model aimed at predicting changes in household spending in response to fuel price fluctuations. The model utilizes fuzzy logic as its main method, enabling it to capture the intricate relationships between household spending and fuel prices. In addition, the proposed model incorporates various factors that can potentially influence household spending. By simulating prediction results under different fuel price increments ranging from 0% to 30%, the model provides valuable insights for policymaking concerning fuel pricing and offers strategies to mitigate the impact of fuel price fluctuations on household welfare. © 2023 IEEE.

关键词： Fuzzy logic

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Easy Data Augmentation for Handling Imbalanced Data in Fake News Detection 1

Easy Data Augmentation for Handling Imbalanced Data in Fake ...

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1st International Conference on Technology, Engineering, and Computing Applications, ICTECA 2023

作者： Pradana, Rilo Chandra Joddy, Setiawan Girsang, Abba Suganda Binus Graduate Program-Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350360554

Detecting fake news in the digital era is challenging due to the proliferation of misinformation. One of the crucial is-sues in this domain is the inherent class imbalance, where genuine news articles significantly outnumber fake ones. This imbalance severely hampers the performance of machine and deep learning models in accurately identifying fake news. Consequently, there is a compelling need to address this problem effectively. In this study, we delve into fake news detection and tackle the critical issue of imbalanced data. We investigate the application of Easy Data Augmentation (EDA) techniques, including back-translation, random insertion, random deletion, and random swap to mitigate the adverse effects of imbalanced data. This study focuses on employing these techniques in conjunction with a deep learning framework, specifically a Bidirectional Long Short-Term Memory (BiLSTM) architecture. The results of the EDA techniques will be systematically compared to see their effectiveness and their impacts on model performance. This study reveals that various EDA techniques, when coupled with a BiLSTM architecture, yield significant improvements in fake news detection. Among the experiments, it shows that Random Insertion, with an impressive accuracy rate of 81.68%, a precision score of 89.38%, and an F1-Score of 87.77% emerges as the most promising technique. The study also highlights the exceptional potential of Back-translation stands out with an 87.16% recall performance. © 2023 IEEE.

关键词： Long short-term memory

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Light Control and Monitoring System Based on Internet of Things 1

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

作者： Syahroni Kusuma, Gede Putra Pratama, Galih Dea Computer Science Department BINUS Graduate Program—Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Electrical energy is one of the most important human needs. Almost all human work requires electrical energy. Lack of electricity may hinder human activities. Therefore, it is important to maintain the availability of the electrical energy. This paper explores a way to reduce waste of electricity use through the implementation of the Internet of things (IoT). We introduce an IoT system that expands the function of the internet that allows us to control household appliances properly and efficiently, enabling us to monitor the cost of usage every month. The proposed system is implemented at the Campus Academy computer Management and Informatics (AMKI) Ketapang to control and monitor lights in the computer Laboratory and Terrace Campus. This system utilizes Raspberry Pi 3, LDR sensors, Relays, and software like PHP and Raspbian Linux. The evaluation results have shown that the proposed IoT system has reduced the monthly cost of electricity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Microcontrollers

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Website Personalization Using Association Rules Mining 1

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

作者： Soewito, Benfano Johan, Jeffrey Computer Science Department Master of Computer Science BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Many companies are redefining their business strategies to improve the business output. Business over internet provides the opportunity to customers and partners where their products and specific business can be found. Web usage mining is the type of web mining activity that involves the automatic discovery of user access patterns from web servers. A real-world challenging task of the web master of an organization is to match the needs of user and keep their attention in their web site. So, web pages can capture the intuition of the user and provide them with the recommendation list. Personalize e-commerce website is done after knowing the habits and behavior patterns of customers e-commerce website using web usage mining with association rules mining apriori algorithms. The method used is a method of analysis and design. In the method of analysis, research variables are determined, and data of sales are collected. In addition, the method of analysis is also performed to measure the accuracy of the apriori algorithm. Designing apriori, program design, and the design of the screen is done in the design method. Results are achieved in the form of an e-commerce website that is personalized using association rules mining apriori algorithm that can recommend the goods in accordance with the preferences and needs of the user. The conclusion of this study is to obtain patterns of association, it takes the data transactions made by customers and the recommendations given by the apriori algorithm would be more accurate if the transaction data is processed more, the categories of goods are fewer, the limit minimum value of support and the limit minimum value of confidence are high. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Websites

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