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Enhancing Rice Variety Identification through Fine-Tuning VGG-Based Deep Learning Models 7

Enhancing Rice Variety Identification through Fine-Tuning VG...

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7th International Conference on Informatics and Computational sciences, ICICoS 2024

作者： Prasetyo, Simeon Yuda Lumentut, Jonathan Samuel Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350375886

Rice variety identification is critical for addressing global food challenges, given rice's status as a staple food source for over half of the world's population. Leveraging recent advances in machine learning, particularly deep learning models like VGG16 and VGG19, presents promising avenues for enhancing rice variety classification accuracy. This study delves into the refinement of rice variety identification through fine-tuning VGG-based deep learning models. By conducting a meticulous comparison of VGG16 and VGG19 models across transfer learning and fine-tuning phases, utilizing robust metrics such as accuracy, precision, recall, and F1-score, the research underscores the significant performance enhancements achievable through fine-tuning techniques. Notably, the VGG16 model exhibits exceptional accuracy, surpassing 99.96% after fine-tuning, showcasing a substantial improvement from the transfer learning phase. These findings highlight the efficacy of fine-tuning methodologies in optimizing classification performance and underline the transformative potential of deep learning models in agricultural classification tasks. The improvement observed from the transfer learning to the fine-tuning phase underscores the importance of iterative refinement in maximizing the models' classification potential, indicating a promising direction for further research in agricultural classification methodologies. © 2024 IEEE.

关键词： Contrastive Learning

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Comparison of user interface and user experience between Bukalapak and Blibli in mobile application 9th

Comparison of user interface and user experience between Buk...

引用

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

作者： Nelson Musthafa, Alwi Erdi Rumagit, Reinert Yosua Pratama, Galih Dea Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The marketplace has become focal point in the ecosystem of electronic commerce, particularly in Indonesia, which has seen rapid growth in the usage of online platforms. Two major marketplaces, Bukalapak and Blibli, stand out as leaders in this market. This study aims to compare the features and user experiences between these two platforms. Employing a survey with 50 respondents, the research found Bukalapak to outperform Blibli in all usability and user satisfaction metrics measured by the USE Questionnaire. While the overall UI/UX advantage for Bukalapak was slight (3.13%), Bukalapak was perceived as better in usefulness, ease of use, ease of learning, and user satisfaction. These findings suggest that Bukalapak has a slight UI/UX advantage over Blibli, but further research with a larger sample size is recommended. © 2024 The Authors.

关键词： Marketplaces

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Implementing Domain Adaptation to YOLOv5 Algorithm for Pothole Detection

Implementing Domain Adaptation to YOLOv5 Algorithm for Potho...

引用

2024 International Conference on Artificial Intelligence, Blockchain, Cloud Computing, and Data Analytics, ICoABCD 2024

作者： Meinardy, Darlyn Budianto, Violetta Jeremy, Nicholaus Hendrik Lumentut, Jonathan Samuel School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368451

Potholes are a major concern for both road safety and vehicle maintenance, as they not only increase the risk of accidents but also lead to high repair costs. Traditional methods of detecting potholes manually are inefficient, time-consuming, and prone to errors, which has led to the adoption of ad-vanced machine-learning techniques. In this study, we utilize the YOLOv5 algorithm, well-known for its real-time object detection abilities, to tackle the pothole detection problem under various environmental conditions. One of the key challenges addressed is the algorithm's performance variability when exposed to different weather and lighting conditions, which can significantly impact the accuracy of detection. To mitigate these challenges, domain adaptation techniques were employed, allowing the model to adapt effectively to diverse environmental scenarios and thereby improving detection accuracy. The methodology involved training the YOLOv5 model on datasets that simulated a wide range of conditions and subsequently testing its performance on different datasets. The results indicate that domain adaptation greatly enhances the model's precision, with the highest precision metric achieved being 0.968. This study demonstrates that the integration of domain adaptation with advanced object detection algorithms like YOLOv5 can significantly improve pothole de-tection, contributing to safer roads as well as reducing vehicle damage. © 2024 IEEE.

关键词： Domain adaptation Environmental conditions Object detection Potholes YOLOv5

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Transforming real estate sales and marketing with AReal: Sustainable solutions through augmented reality 9th

Transforming real estate sales and marketing with AReal: Sus...

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

作者： Sekar Ayu, N. Lavender Nathania, A. Tsalasa Kale, S.M. Permana, Frihandhika Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The present paper discusses the adoption of Augmented Reality (AR) technology in the real estate industry focusing on the real estate industry. Along with the times, the marketing tactics of the industry are increasingly developing along with the times. Technology is one of the means in developing marketing strategies for the real estate industry, including AR technology. The purpose of Augmented Reality is to offer innovative means to improve efficiency in the marketing operations of the real estate industry. The waterfall approach was used to develop the Augmented Reality project, which is a linear progression from the beginning of the project to the end, starting with using a good 3D model and user interface, implementing markers, adding interactivity, testing, designing the AR layout, and maintaining After analysing the User Experience Questionnaire (UEQ) as a measurement method, we obtained positive evaluations by entering data from 30 respondents. These positive ratings were obtained for all dimensions of AReal augmented reality with above-average scores. The Perspicuity dimension showed a proficient level of clarity and comfort among users, with an average score of 1.908, which is the highest average score among the other dimensions. In contrast, the novelty dimension had the lowest average score of 1. 850 points, but the average score is above average, indicating a positive evaluation. The UEQ dimensions can be categorized into attractiveness, utilitarian quality, and hedonic quality. Practical quality scored the lowest, hedonic quality scored the highest, and attractiveness was somewhere in between. Overall, the results show that user reviews for AReal Augmented Reality are positive across multiple dimensions, with Perspicuity receiving praise and Novity results indicating that the augmented reality experience is well received overall and provides valuable insights for further improvement and development. © 2024 The Authors.

关键词： Commerce

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Examining the Effectiveness of an AR-Enhanced Waste Sorting Game for Marine Conservation: A UEQ-Based Evaluation 9

Examining the Effectiveness of an AR-Enhanced Waste Sorting ...

引用

9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Wibowo, Aldo Arista Wijaya Pujo Hamada, Kanz Abdillah Maulana, Fairuz Iqbal School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Ocean pollution threatens marine ecosystems around the world. It is estimated 4.8-12.7 million metric tons of new plastic waste goes into the ocean every year. It has brought a huge negative impact to more than 1000 marine species. Therefore, we developed AquaVerse AR, an app that leverages Augmented Reality (AR) technology to provide education about marine life and conservation. This application was done by two developers using the Agile method within three weeks. In technical testing, it was found that the success rate of marker detection was 100% at a distance of 30 cm - 80 cm and a height of 60cm with an angle of 20.56 ° - 63.43 °. The results of usability testing using the UEQ method get excellent benchmarks in all aspects, with the highest aspect being attractiveness with a mean of 2.58 and the lowest being novelty with a mean of 1.80. © 2024 IEEE.

关键词： Marine pollution

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Oil palm tree counting and abnormality assessment using deep learning and index vegetation 9th

Oil palm tree counting and abnormality assessment using deep...

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

作者： Minarto, Angelin Ramadhan, Muhammad Hafizd Lie, Alvin Irwansyah, Edy Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Oil Palm is a prominent and primary plantation commodity in Indonesia, with extensive and expansive plantations making traditional management methods in abnormality assessment impractical and resource intensive. This study contributes to the field by utilizing a more efficient approach using advanced technology. In an attempt to address this issue, this research utilizes a trained YOLOv5 model to perform oil palm tree counting and employs NDVI to assess their abnormalities. The result shows similar or slightly superior performance compared to previous studies, with improvement in F1 score from 0.81 to 0.83 and confusion matrix calculation showing NDVI accuracy of 72.7%. This illustrates the effectiveness of the trained YOLO model for oil palm tree counting and NDVI for oil palm tree abnormalities assessment. © 2024 The Authors.

关键词： Deep learning

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Machine Learning Approaches for Analyzing Bank Customer Complaints

Machine Learning Approaches for Analyzing Bank Customer Comp...

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2024 International Conference on computer Engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Arasy, Dustin Bijou Widjaja, Shamgar Eugene T'sidkenu Edbert, Ivan Sebastian Suhartono, Derwin School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368802

Proper customer service is the key to increasing customer retention and brand credibility in financial banks. This can be achieved by helping solve customers' issues through their complaints. This study explores the application of traditional machine learning models to classify bank customer complaints. Using the dataset from the CFPB database, this research implements four ML models to handle this task: Multinomial Naïve Bayes, Random Forest Classifier, Stochastic Gradient Descent Classifier, and Logistic Regression. The dataset first underwent preprocessing steps to clean irrelevant data using Natural Language Processing and the features were extracted using Term Frequency-Inverse Document Frequency. After evaluating the baseline results, hyperparameter tuning is implemented using GridSearchCV to analyze improvements in performance for each model. The results show that LGR achieves the best performance out of all, with the highest accuracy score: 0.827 and an F1-score: 0.825. In conclusion, traditional machine learning models have the potential to provide a cost-efficient solution for classifying bank customer complaints. © 2024 IEEE.

关键词： Sales

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Catboost in Fetal Health Classification 11

Catboost in Fetal Health Classification

引用

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

作者： Yulistiani, Risma Kurniadi, Felix Indra School of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350389654

The evaluation of fetal health is vital in safeguarding the overall welfare of pregnant women and their unborn children. Using the Catboost algorithm, we address the difficulty of precisely determining the health status of the fetus in this study. A variety of machine learning models were contrasted, such as AdaBoost, LightGBM, and XGBoost. The outcomes of experiments employing both train-test split and K-Fold cross-validation demonstrate that CatBoost consistently attains commendable levels of accuracy, precision, and recall, with values varying between 0.94 and 0.97 across distinct folds. © 2024 IEEE.

关键词： Electronic health record

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Comparison of a Combined Model (K-Nearest Neighbor Algorithm and Support Vector Machine Algorithm), K-Nearest Neighbor Algorithm, and Support Vector Machine Algorithm to Detect Hate Speech on Social Media 9

Comparison of a Combined Model (K-Nearest Neighbor Algorithm...

引用

9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Mackenzie, Gabriel Meyliana Yunanda, Rezki Suryaningrum, Kristien Margi School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

In this study we delve into the use of a combined machine learning model to counter the growing problems of hate speech on social media. Hate speech can significantly impact individuals, communities, and society. To counteract this, we have proposed a combined K-Nearest Neighbor (KNN) algorithm with a Support Vector Machine (SVM) algorithm called the KNN-SVM. We used a publicly available dataset from Kaggle [18]. The dataset contains labels such as hate speech, offensive language, and normal text. Our aim is to improve the classification metrics for accuracy, precision, recall, and F1-score on detecting hate speech. Then we compared our model's performance to the individual KNN and SVM model. While all models faced significant challenges due to the imbalanced data with fewer hate speech examples, KNN-SVM achieved a promising result against the other models. It demonstrated the highest recall and F1-score for hate speech detection, indicating its effectiveness in identifying these crucial instances. However, limitations exist. The model's overall accuracy was lower than the individual SVM model. Further research is needed to enhance the model's effectiveness through data refinement techniques like balanced labeling and exploring advanced algorithms like deep learning. © 2024 IEEE.

关键词： Adversarial machine learning

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Analyzing Views on Presidential Candidates for Election 2024 Based on the Instagram and X Platforms with Text Clustering 9th

Analyzing Views on Presidential Candidates for Election 2024...

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

作者： Hanifa, Aida Debora, Catheline Hasani, Muhammad Fikri Wicaksono, Pandu Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Due to technological advancements, social media has become a major pawn in building people's opinions and actions, especially ahead of important moments such as the 2024 general election in Indonesia. Having a good understanding of the headlines in social media will have a big impact on the people and candidates in terms of votes. The purpose of this research is to group sentences in clusters and find keywords that can be used as parameters to determine topics in each cluster. Data sources will be taken from Instagram and X And will be vectorized with TF-IDF, SBERT, and clustered with the DBSCAN method. Then, complemented by LDA for result exposure. The results obtained in this research found that the best parameters of DBSCAN with TF-IDF are 0.85 epsilon, 2 minPts, 0.594768 score, 2 number of clusters and 20 outliers. The best parameters of DBSCAN with Sentence BERT are 0.1 epsilon, 2 minPts, -0.293426 score, 4 number of clusters and 937 outliers. The results of DBSCAN with each text embedding from topic modeling with LDA show that it is related to the 2024 presidential candidates such as Anies, Prabowo, gibran, etc. From all topics displayed by LDA, the most frequently discussed presidential candidate pair is pair number 01, namely Anies Baswedan and Muhaimin Iskandar with several words related to their traits or actions. Based on the dataset, the topics formed refer to certain sentiments by the community with predominantly positive or supportive responses such as discussing traits that are suitable as role models or ideas that are considered modern (digital approach) displayed at DBSCAN with Sentence-BERT method. Where most of these responses are sourced from the social media platform Instagram. These indices that most Indonesians view pair 01 as ideal presidential candidate both in traits and actions as a presidential candidate, especially the public opinion from Instagram. © 2024 The Authors.

关键词： Statistics

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