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Jurnal Teknik Informatika C.I.T. Medicom
Published by Institute of Computer Science
ISSN : 23378646     EISSN : 2721561X     DOI : -
Core Subject : Science,
Computer Science & IT
The Jurnal Teknik Informatika C.I.T a scientific journal of Decision support sistem , expert system and artificial inteligens which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "Vol 16 No 1 (2024): March: Intelligent Decision Support System (IDSS)" : 5 Documents clear
Advancing fake news detection: a comparative study of RNN, LSTM, and Bidirectional LSTM Architectures Gregorius Airlangga
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.696.pp13-23

Abstract

In the era of information overload, the exponential growth of digital content has coincided with the proliferation of 'fake news,' posing a critical challenge to online information credibility. This study addresses the pressing need for robust fake news detection systems by conducting a comparative analysis of three neural network architectures: Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM). Our primary objective is to assess their effectiveness in identifying fake news in a binary classification setting. To achieve this goal, we employed advanced neural network models and a dataset of news titles. Our applied research method included data preprocessing and the utilization of RNN, LSTM, and BiLSTM models, each tailored to handle sequential data and capture temporal dependencies. we rigorously assessed the performance of RNN, LSTM, and BiLSTM models using a range of metrics, including accuracy, precision, recall, and F1-score. To achieve a comprehensive evaluation, we divided our dataset into training and testing subsets. Specifically, we allocated 67% of the data for training purposes and the remaining 33% for testing. Our research findings reveal that all three models consistently achieved high accuracy levels, approximately 91%, with slight variations in precision and recall. Notably, the LSTM model exhibited a marginal improvement in recall, which is crucial when the consequences of missing deceptive content outweigh false alarms. Conversely, the RNN model demonstrated slightly better precision, making it suitable for applications where minimizing false positives is paramount. Surprisingly, the BiLSTM model did not significantly outperform the unidirectional models, suggesting that, for our dataset, processing information bidirectionally may not be essential. In conclusion, our study contributes valuable insights to the field of fake news detection. It underscores the significance of model selection based on specific task requirements and dataset characteristics.
Sentiment, toxicity, and social network analysis of virtual reality product content reviews Yerik Afrianto Singgalen
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.716.pp24-34

Abstract

Virtual Reality (VR) technology has garnered significant attention in recent years due to its potential to revolutionize various industries. This study aims to investigate consumer sentiments toward VR products, mainly focusing on Meta Quest 3 in the context of the AI era. The background section outlines the rising popularity of VR products and their impact on consumer behavior, emphasizing the need for a comprehensive understanding of consumer sentiments to inform marketing strategies effectively. Methodologically, the study adopts the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework to guide the analytical approach, which includes sentiment classification, toxicity scoring, and social network analysis (SNA). A dataset comprising 2,115 consumer interactions and evaluations was utilized, with 1,302 interactions for the ALINE tech video and 813 interactions for The Tech Chap video, to derive insights into sentiment patterns and interaction dynamics. The findings reveal a positive reception towards VR products, with Meta Quest 3 particularly well-received. The sentiment classification algorithm achieved an accuracy of 77.92% without SMOTE and 85.66% with SMOTE, demonstrating competency in sentiment prediction. The precision, recall, and f-measure for SVM without SMOTE were 85.78%, 99.83%, and 92.27%, respectively, while with SMOTE, they were 100%, 55.82%, and 71.50%, respectively. Toxicity scoring yielded an average toxicity score of 0.05. Social network analysis (SNA) identified a network diameter of 6, modularity of 0.6072, and a density of 0.002815, highlighting the intricate dynamics of consumer interaction within the VR domain.
Enhancing accommodation selection: an analysis of simple additive weighting and rank order centroid Yerik Afrianto Singgalen
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.726.pp35-44

Abstract

This study deploys Simple Additive Weighting (SAW) and Rank Order Centroid (ROC) in selecting accommodations. The research problem investigates the efficacy and applicability of these methods in aiding decision-makers, mainly tourists, in choosing accommodations based on diverse criteria. To address this issue, a comprehensive comparative analysis was conducted utilizing both SAW and ROC methodologies to evaluate a range of accommodations in the vibrant tourism destination of Raja Ampat, Indonesia. The SAW method involved the assignment of weights to various criteria and the subsequent calculation of overall scores for each accommodation. In contrast, the ROC method utilized a centroid-based approach to rank the accommodations. The findings underscore notable distinctions between the two methodologies, with SAW providing a detailed assessment of accommodations based on weighted criteria, whereas ROC offers a simplified ranking system. Additionally, the research identified Nyande Raja Ampat as the top-ranked accommodation with a score of 0.95859128, followed by Raja Ampat Sandy Guest House (score: 0.924445677) and Mambetron Homestay Raja Ampat (score: 0.861666825). Warahnus Dive Homestay and Hamueco Raja Ampat Resort secured the fourth and fifth ranks, with scores of 0.831961086 and 0.827113234, respectively. These findings offer valuable insights for tourists seeking accommodations in Raja Ampat and contribute to the broader understanding of decision-making methodologies in the tourism industry.
Toxicity, topic, and sentiment analysis on the operation of coal-fired power plants content reviews Yerik Afrianto Singgalen
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.728.pp45-57

Abstract

This research addresses the challenge of comprehensively analyzing textual data, emphasizing the prevalence of harmful language, sentiment expression, and thematic content. The research problem centers around interpreting large datasets, prompting a multifaceted methodology. Drawing upon the Cross-Industry Standard Process for Data Mining (CRISP-DM), the study follows a systematic approach involving six key phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Toxicity analysis reveals an average toxicity level ranging from 0.00404 to 0.03878 and maximum values up to 0.66151, highlighting varying degrees of harmful language prevalence. Sentiment analysis identifies that 60% of sentiments expressed are positive, 30% are neutral, and 10% are negative, elucidating prevailing attitudes. Topic modeling extracts twelve distinct themes, enriching the interpretive depth of the dataset. Performance evaluation metrics for SVM using SMOTE indicate an accuracy of 91.41% +/- 1.66%, with 832 true negatives and 689 true positives, affirming the model's reliability. Based on these findings, it is recommended that stakeholders implement robust content moderation strategies to mitigate the dissemination of harmful language, foster a safer online environment, and leverage sentiment and topic analysis insights for informed decision-making. This interdisciplinary approach enhances data analysis capabilities, providing actionable insights crucial for addressing societal challenges and advancing scholarly discourse.
Comparison of data mining algorithms (random forest, C4.5, catboost) based on adaptive boosting in predicting diabetes mellitus Yennimar Yennimar; William Leonardi; Harris Weide; Devin Cantona; Gani Mores Hutagalung
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.730.pp1-12

Abstract

This research aims to evaluate the performance of three algorithms data mining, namely C4.5, Random Forest, and Catboost Classifier, which are strengthened by Adaptive Boosting in predicting diabetes mellitus in humans. Through analysis, it was found that the C4.5 algorithm is based on Adaptive Boosting obtained an average accuracy of 73.74%, precision of 61.39%, and recall amounting to 69.00%. Random Forest algorithm based on Adaptive Boosting shows an average accuracy of 73.52%, precision of 65.79%, and recall amounting to 65.06%. Meanwhile, the Catboost Classifier algorithm is Adaptive based Boosting has an average accuracy of 73.67%, precision of 61.19%, and recall was 69.18%. Thus, although all three algorithms shows similar performance, the C4.5 algorithm based on Adaptive Boosting stands out with better performance in terms of accuracy, precision and recall. The implication of this research is that the use of the C4.5 algorithm is based Adaptive Boosting can be a more effective approach to support early detection of diabetes mellitus in humans

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