Abdul Fadlil Abdul Fadlil, Imam Riadi, Fiki Andrianto
: The rapid growth of the online market, particularly in the digital realm, has spurred the need for in-depth studies regarding marketing strategies through public opinion, especially on platforms like Twitter. The sentiments expressed in customer tweets hold significant insights into their satisfaction or dissatisfaction levels with a service. Therefore, the use of ML algorithms in sentiment analysis is imperative to detect whether such comments lean towards positivity or negativity regarding a service. This research focuses on sentiment analysis towards three major e-commerce platforms in Indonesia: Tokopedia, Shopee, and Lazada, through the utilization of Twitter. The classification process involves various stages, including preprocessing, feature extraction and selection, data splitting for classification, and evaluation. The selection of both linear and non-linear SVM models as the focus of this research is based on their ability to handle large and complex datasets. The linear kernel is chosen for its proficiency in cases with a linear relationship between features and class labels, while the non-linear SVM provides flexibility in dealing with complex and non-linear relationships. Based on the evaluation results of the SVM model on the dataset, it is found that the polynomial kernel provides the highest accuracy value of 93%, with a training data share of 85%. This model features strong prediction capabilities with a precision of 93% for negative and 93% for positive labels. Although the linear kernel and other kernels showed solid performance, the polynomial kernel provided the most optimal results in the context of online marketplace sentiment analysis using data from Twitter
{"title":"Improving Sentiment Analysis in Digital Marketplaces through SVM Kernel Fine-Tuning","authors":"Abdul Fadlil Abdul Fadlil, Imam Riadi, Fiki Andrianto","doi":"10.12785/ijcds/160113","DOIUrl":"https://doi.org/10.12785/ijcds/160113","url":null,"abstract":": The rapid growth of the online market, particularly in the digital realm, has spurred the need for in-depth studies regarding marketing strategies through public opinion, especially on platforms like Twitter. The sentiments expressed in customer tweets hold significant insights into their satisfaction or dissatisfaction levels with a service. Therefore, the use of ML algorithms in sentiment analysis is imperative to detect whether such comments lean towards positivity or negativity regarding a service. This research focuses on sentiment analysis towards three major e-commerce platforms in Indonesia: Tokopedia, Shopee, and Lazada, through the utilization of Twitter. The classification process involves various stages, including preprocessing, feature extraction and selection, data splitting for classification, and evaluation. The selection of both linear and non-linear SVM models as the focus of this research is based on their ability to handle large and complex datasets. The linear kernel is chosen for its proficiency in cases with a linear relationship between features and class labels, while the non-linear SVM provides flexibility in dealing with complex and non-linear relationships. Based on the evaluation results of the SVM model on the dataset, it is found that the polynomial kernel provides the highest accuracy value of 93%, with a training data share of 85%. This model features strong prediction capabilities with a precision of 93% for negative and 93% for positive labels. Although the linear kernel and other kernels showed solid performance, the polynomial kernel provided the most optimal results in the context of online marketplace sentiment analysis using data from Twitter","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141694091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: Library is a knowledge warehouse and various long past references can be found in it. Students, professors, kids, and adults are regularly encouraged to visit the library as it provides a conducive environment for building the habit of reading books and improving individual critical-thinking skills. As technology is getting more and more advanced nowadays, some common problems faced by the librarians can be replaced by machines. For instance, the librarians may not be available all the time at the counter; reduction of physical contact due to Covid19 infection et cetera, machines can take over the librarians’ roles to handle the tasks. In this paper, an Artificial Intelligence (AI) chatbot is proposed and implemented on mobile application to answer library-related questions. Bidirectional Encoder Representations from Transformers (BERT) algorithm is employed to classify the intent of the user’s messages. Besides, many existing chatbot applications support only the text input. This paper proposes a speech-to-text recognition feature to enable both text and voice input. If there are any queries that cannot be solved by the chatbot system, it will store the queries in the database and the library admins can filter the queries and upload new training data for the AI model to cover a wider range of questions.
{"title":"Lib-Bot: A Smart Librarian-Chatbot Assistant","authors":"Tong-Jun Ng, Kok-Why Ng, S. Haw","doi":"10.12785/ijcds/160101","DOIUrl":"https://doi.org/10.12785/ijcds/160101","url":null,"abstract":": Library is a knowledge warehouse and various long past references can be found in it. Students, professors, kids, and adults are regularly encouraged to visit the library as it provides a conducive environment for building the habit of reading books and improving individual critical-thinking skills. As technology is getting more and more advanced nowadays, some common problems faced by the librarians can be replaced by machines. For instance, the librarians may not be available all the time at the counter; reduction of physical contact due to Covid19 infection et cetera, machines can take over the librarians’ roles to handle the tasks. In this paper, an Artificial Intelligence (AI) chatbot is proposed and implemented on mobile application to answer library-related questions. Bidirectional Encoder Representations from Transformers (BERT) algorithm is employed to classify the intent of the user’s messages. Besides, many existing chatbot applications support only the text input. This paper proposes a speech-to-text recognition feature to enable both text and voice input. If there are any queries that cannot be solved by the chatbot system, it will store the queries in the database and the library admins can filter the queries and upload new training data for the AI model to cover a wider range of questions.","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"57 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141689459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Secure Self-Embedding Technique for Manipulation Detection and Correction of Medical Images","authors":"Afaf Tareef","doi":"10.12785/ijcds/160140","DOIUrl":"https://doi.org/10.12785/ijcds/160140","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"302 1‐2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141708163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rabie Madani, Abderrahmane Ez-Zahout, F. Omary, Abdelhaq Chedmi
{"title":"Advancing Context-Aware Recommender Systems: A Deep Context-Based Factorization Machines Approach","authors":"Rabie Madani, Abderrahmane Ez-Zahout, F. Omary, Abdelhaq Chedmi","doi":"10.12785/ijcds/160128","DOIUrl":"https://doi.org/10.12785/ijcds/160128","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"9 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141694011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Roseline Obatimehin, Micheal Ogayemi, Martins Osifeko, A. Oyedeji, Abisola Olayiwola, Olatilewa R. Abolade
{"title":"A Crop Adaptive Irrigation System for Improving Farm Yield in Rural Communities","authors":"Roseline Obatimehin, Micheal Ogayemi, Martins Osifeko, A. Oyedeji, Abisola Olayiwola, Olatilewa R. Abolade","doi":"10.12785/ijcds/160134","DOIUrl":"https://doi.org/10.12785/ijcds/160134","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141702776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Disaster Event, Preparedness, and Response in Indonesian Coastal Areas: Data Mining of Official Statistics","authors":"Gunawan Gunawan","doi":"10.12785/ijcds/160120","DOIUrl":"https://doi.org/10.12785/ijcds/160120","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"16 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141696988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deepa Pavithran, C. Shibu, Sudheer Madathiparambil
{"title":"Enhancing Trust between Patient and Hospital using Blockchain based architecture with IoMT","authors":"Deepa Pavithran, C. Shibu, Sudheer Madathiparambil","doi":"10.12785/ijcds/160123","DOIUrl":"https://doi.org/10.12785/ijcds/160123","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"85 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141700977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Deep Learning Based Hyperspectral Image Classification: A Review For Future Enhancement","authors":"Anish Sarkar, Utpal Nandi, Nayan Kumar Sarkar, Chiranjit Changdar, Bachchu Paul","doi":"10.12785/ijcds/160133","DOIUrl":"https://doi.org/10.12785/ijcds/160133","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"67 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141714787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Andreas Christianto, Jovito Colin, I. G. Putra, Kusuma Negara
: Various document types (financial, commercial, judicial) necessitate signatures for authentication. With the advancements of technology and the increasing number of documents, traditional signature verification methods encounter challenges in facing tasks related to verifying images, such as signature verification. This idea is further reinforced by the growing migration of transactions to digital platforms. To that end, the fields of Machine learning (ML) and Deep Learning (DL) o ff er promising solutions. This study combines Convolutional Neural Network (CNN) algorithms, such as Visual Geometry Group (VGG) and Residual Network (ResNet) or VGG16 and ResNet-50 specifically, for image embedding alongside ML classifiers such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest, and Extreme Gradient Boosting (XGBoost). While the aforementioned solutions are usually enough, real life scenarios tend to di ff er in environment and conditions. This problem leads to di ffi culty and accidents in the verification process, causing the users to redo the process or even end it prematurely. To alleviate the issue, this study employs optimization methods such as hyperparameter tuning via Grid Search and triplet loss optimization to enhance model performance. By leveraging the strengths of CNNs, Machine Learning classifiers, and optimization techniques, this research aims to improve the accuracy and e ffi ciency of signature verification processes while addressing real-world challenges and ensuring the trustworthiness of electronic transactions and legal documents. Evaluation is conducted using the ICDAR-2011 and BHSig-260 datasets. Results indicate that triplet loss optimization significantly improves the performance of the VGG16 embedding model for SVM classification, notably elevating the Area Under the ROC Curve (AUC) from 0.970 to 0.991.
{"title":"Authentic Signature Verification Using Deep Learning Embedding With Triplet Loss Optimization And Machine Learning Classification","authors":"Andreas Christianto, Jovito Colin, I. G. Putra, Kusuma Negara","doi":"10.12785/ijcds/160121","DOIUrl":"https://doi.org/10.12785/ijcds/160121","url":null,"abstract":": Various document types (financial, commercial, judicial) necessitate signatures for authentication. With the advancements of technology and the increasing number of documents, traditional signature verification methods encounter challenges in facing tasks related to verifying images, such as signature verification. This idea is further reinforced by the growing migration of transactions to digital platforms. To that end, the fields of Machine learning (ML) and Deep Learning (DL) o ff er promising solutions. This study combines Convolutional Neural Network (CNN) algorithms, such as Visual Geometry Group (VGG) and Residual Network (ResNet) or VGG16 and ResNet-50 specifically, for image embedding alongside ML classifiers such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest, and Extreme Gradient Boosting (XGBoost). While the aforementioned solutions are usually enough, real life scenarios tend to di ff er in environment and conditions. This problem leads to di ffi culty and accidents in the verification process, causing the users to redo the process or even end it prematurely. To alleviate the issue, this study employs optimization methods such as hyperparameter tuning via Grid Search and triplet loss optimization to enhance model performance. By leveraging the strengths of CNNs, Machine Learning classifiers, and optimization techniques, this research aims to improve the accuracy and e ffi ciency of signature verification processes while addressing real-world challenges and ensuring the trustworthiness of electronic transactions and legal documents. Evaluation is conducted using the ICDAR-2011 and BHSig-260 datasets. Results indicate that triplet loss optimization significantly improves the performance of the VGG16 embedding model for SVM classification, notably elevating the Area Under the ROC Curve (AUC) from 0.970 to 0.991.","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"13 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141715166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Linda Rosita, S. Kusumadewi, Tri Ratnaningsih, N. Kertia, Barkah Djaka Purwanto, Elyza Gustri Wahyuni
{"title":"Ferritin Level Prediction in Patients with Chronic Kidney Disease using Cluster Centers on Fuzzy Subtractive Clustering","authors":"Linda Rosita, S. Kusumadewi, Tri Ratnaningsih, N. Kertia, Barkah Djaka Purwanto, Elyza Gustri Wahyuni","doi":"10.12785/ijcds/160132","DOIUrl":"https://doi.org/10.12785/ijcds/160132","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"17 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141700054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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