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9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Sonata, Ilvico Computer Science Department Binus Graduate Program Bina Nusantara University Indonesia Jakarta Indonesia

ISBN: (纸本)9798400717161

Human food needs are increasing all the time. This condition is in line with the increasing world population and exacerbated by climate change. Agricultural products are one of the sources of food to meet food needs. The use of technology is one of the keys to success in increasing agricultural production. One of the technologies used in agricultural technology is the Internet of Things (IoT). IoT in agricultural technology can be used to monitor soil and weather conditions to ensure that these conditions are in accordance with the needs of agricultural crops. The use of machine learning technology can be combined with IoT to help predict the types of crops that are suitable for planting based on soil and weather conditions. The results of the experiment show that the machine learning model can be used to predict the right types of plants to plant based on soil and weather conditions obtained through IoT. This method can be used to increase agricultural yields. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Smart agriculture

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Performance Analysis of Heart Disease Prediction using Kernels in Support Vector Machine Model 4

Performance Analysis of Heart Disease Prediction using Kerne...

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4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Anggreainy, Maria Susan Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350376111

Linear data is a collection of data that is linearly separated by a straight line and nonlinear data is a collection of data that is not separated by a line. Usually, non-linear data is separated by irregular curves. However, for non-linear data. Support Vector Machine (SVM) is difficult to classify data for non-linear data. The solution is to use Kernel Trick. Kernel Trick is a simple method used to map low-dimensional nonlinear data and transform it into a higher-dimensional space. The goal is to make it easier to classify data by finding a hyperplane that can separate the dataset linearly well. This research is to create a program that can predict heart disease using the Support Vector Machine (SVM) model with Kernel Tricks. Heart disease prediction is very important because the heart is one of the most important organs in the human body. The results indicated that the SVM model with the Radial Basis Function (RBF) Kernel achieved the highest accuracy compared to the Linear Kernel and Polynomial Kernel. © 2024 IEEE.

关键词： Support vector regression

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Evaluation of REAL-ESRGAN Using Different Types of Image Degradation 11

Evaluation of REAL-ESRGAN Using Different Types of Image Deg...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Andriadi, Kus Zarlis, Muhammad Heryadi, Yaya Chowanda, Andry Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta Indonesia Binus Graduate Program Bina Nusantara University Information System Management Department Jakarta11480 Indonesia School of Computer Science Binus University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350389654

This study evaluates the performance of the REAL-ESRGAN [1] model on images with varying levels of degradation using the DIV2K dataset [2], such as the Wild, the Mild, the Difficult, and the x8 subsets. REAL-ESRGAN was created to solve super-resolution problems and aims to produce high-resolution images from low-resolution images. Experiments were conducted at scales of x2 and x4, and performance was measured using Full-Reference metrics (LPIPS, PSNR, SSIM) and No-Reference metrics (NIQE, MANIQA, CLIPIQA, and PI). The Results were good, especially with the x2 scale;it has higher PSNR and SSIM scores, lower LPIPS and NIQE values, and enhanced visual and perceptual quality. The model faced more significant challenges with the wild and the difficult datasets because they have more complex degradations and compression artifacts;it can be seen with unstable results of Full-Reference and No-Reference metrics. On the contrary, the Mild and x8 datasets yielded better results in both metrics;not only that, even the computational cost for Mild and x8 outperforms the rest of the dataset. This study shows the strengths and limitations of REAL-ESRGAN in handling different levels of image degradation. For future research, the model needs enhancement to tackle the degradation format of the wild and the difficult dataset. It would be good if the REAL-ESRGAN improvement could also maintain the computational cost. © 2024 IEEE.

关键词： Gallium nitride

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A Decision Model for Smart Village in Measuring Socioeconomic Inequality 11

A Decision Model for Smart Village in Measuring Socioeconomi...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Utama, Ditdit Nugeraha Binus Graduate Program - Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350389654

Making socio-economic decisions is critical as they profoundly impact communities. It is imperative to approach these decisions with academic rigour and objectivity to effectively address prevalent community-wide issues. One such challenge often encountered by communities is socio-economic inequality. This study attempts to tackle this challenge by creating a decision model to assess socio-economic inequality in a region. The research was performed via four crucial stages, leading to the creation of a fuzzy decision model based on the Williamson Index (WI) method. This carefully developed model was designed and constructed using an object-oriented approach. The simulation results indicated that the integrated inequality index value for Sawah, an urban village in Ciputat, Indonesia, is 0.65. This em-pirical result provides valuable insights, enabling policymakers and stakeholders (as decision-makers) to design targeted policies and programs to reduce inequality and enhance community prosperity. © 2024 IEEE.

关键词： fuzzy logic inequality index object-oriented smart decision model smart village socio-economic

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Detection of Narcotics Money Laundering in Suspicious Financial Transactions Using Convolutional Extreme Gradient Boosting (ConvXGB) 4

Detection of Narcotics Money Laundering in Suspicious Financ...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Winarno, Leonardus Verrell Sayoga Kinantra Wibowo, Antoni School of Computer Science Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331506490

With the development of narcotics problems that continue to increase, the Indonesian Government responds through the Badan Narkotika Nasional (BNN) with data showing the condition of narcotics tends to grow every year and is followed by the number of assets of money laundering (ML) crimes against narcotics cases. One of the banks in Indonesia as a reporting party for suspicious financial transactions (SFT) has obstacles in detecting narcotics ML due to complex and rarely found patterns. Some previous studies conducted experiments using Convolutional Neural Network (CNN), Extreme Gradient Boosting (XGBoost), and even a combination of both into Convolutional Extreme Gradient Boosting (ConvXGB), and improved model performance in several datasets. This paper designs a model using the ConvXGB algorithm by adopting the CNN architecture, LeNet-5, by applying several convolution layers and pooling layers as a baseline model for feature learning, and the XGBoost as feature classification. Three phases of research are the preprocessing phase by collecting data, transforming data, balancing data with a hybrid sampling technique, splitting data, and scaling data, followed by the implementation phase by creating a ConvXGB model, training and testing the model, then finally the evaluation phase by analyzing results and hyperparameter tuning. The dataset used is SFT from the bank during 2023. This ConvXGB has three convolution layers, a pooling layer, and a flattened layer. The performance test results are the accuracy value and F1-Score value of 99.11% each after hyperparameter tuning. By performing a hybrid model, the model performance results are better. © 2024 IEEE.

关键词： Laundering

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Virality Prediction on Twitter Using Combined CNN and BERT Models 4

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

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

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

ISBN: (纸本)9798331506490

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. © 2024 IEEE.

关键词： Prediction models

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Enhancing Software Quality Through Defect Prediction 3

Enhancing Software Quality Through Defect Prediction

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Gita Gayatri, Nyoman Ayu Soeparno, Haryono Gaol, Ford Lumban Arifin, Yulyani Bina Nusantara University Binus Graduate Program Computer Science Dept. of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350367492

Prediction of software defects is an important aspect of software quality assurance, aiming to identify defective modules to improve reliability and reduce costs in the maintenance process. A systematic literature review (SLR) on software defect prediction is presented in this study, focusing on identifying commonly used datasets, methods, and frameworks in this domain between 2014 and 2023. The review follows established SLR guidelines to synthesize data from primary studies, providing insight into research trends as well as methodological advances. The results show a significant reliance on public datasets such as the NASA Metrics Data program (MDP) and the PROMISE repository. Various machine-learning techniques have been used, with Neural networks, support vector machines, and random forests becoming the most popular techniques. This review aims to guide future research by highlighting key trends and gaps in the existing literature. © 2024 IEEE.

关键词： Software quality

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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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A Hybrid CNN-SVM Approach for Malaria in Blood Cell Detection 2

A Hybrid CNN-SVM Approach for Malaria in Blood Cell Detectio...

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2nd International Conference on Software Engineering and Information Technology, ICoSEIT 2024

作者： Karnyoto, Andrea Stevens Isnan, Mahmud Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350317503

There is still a severe malaria problem worldwide, particularly in regions with limited access to diagnostic tools. It is crucial to develop a system for detecting malaria in blood cells. This paper presents a hybrid Convolutional Neural Network (CNN), and as a classifier, we use a Support Vector Machine (SVM) framework for the automated detection of malaria parasites in blood cell images. The proposed system leverages the strengths of CNNs in feature extraction and representation learning from images, combined with the discriminative power of SVMs for classification. Initially, CNN extracts intricate features from blood cell images, capturing essential patterns indicative of malaria infection. Subsequently, the extracted features are used to train an SVM classifier, enabling accurate discrimination between parasitized and uninfected blood cells. Experimental dataset evaluations were obtained from the Lister Hill National Center for Biomedical Communications website of the National Library of Medicine. The proposed model achieves a better f1-score, outperforming individual CNN or SVM models, around 0.015 compared to individual CNN models and 0.27 compared to individual SVM models. This hybrid CNN-SVM methodology offers a promising solution for accurately and efficiently detecting malaria parasites in blood cell images. © 2024 IEEE.

关键词： Support vector machines

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Classifying Argument Component using Deep Learning on English Dataset

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Informatica (Slovenia) 2023年第5期47卷 63-68页

作者： Gunawan, William Suhartono, Derwin Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

The study focuses on the argument component in argumentation mining, specifically examining claim and premise types. Various datasets exist for argumentation components, each with different classes. The study evaluates the performance of deep learning architectures, particularly using contextual embedding as the initial layer. Six datasets with diverse argument components are used for validation. The research provides a comprehensive comparison of deep learning architectures, combining multiple layers such as BERT or word embedding with LSTM, GRU, or CNN. The results and their implications are discussed in the concluding section of the journal. The study demonstrates significant results with the BERT-BiGRU-CRF architecture after conducting several experiments. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Embeddings

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