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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 99 Documents
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Modeling the dynamics of misinformation spread on social media platforms Arisman Arisman; Hasanal Fachri Satia Simbolon
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 6 (2024): January : 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.Vol15.2024.718.pp297-305

Abstract

This study employs the SEIRS (Susceptible-Exposed-Infectious-Recovered-Susceptible) model to investigate the dissemination dynamics of misinformation within a community. Utilizing a population of 100,000 individuals and a time frame of 500 units, the model incorporates parameters such as transmission rate, recovery rate from the exposed and infectious stages, and the rate of returning to susceptibility. Simulation results demonstrate the fluctuating patterns of individuals across stages, depicting an initial surge in exposure followed by a gradual decline as individuals transition into recovery or awareness of misinformation. This research underscores the SEIRS model's utility in comprehending misinformation spread and highlights the potential for behavioral shifts and societal awareness in mitigating its effects. Furthermore, it emphasizes the importance of interdisciplinary approaches, blending epidemiological, psychological, and sociological perspectives, to devise effective interventions combating misinformation dissemination. Ultimately, fostering digital and critical literacy alongside sustained educational efforts emerges as a crucial strategy in fostering healthier, more trustworthy information environments.
A Improve refinement approach iterative method for solution linear equition of sparse matrices Desi Vinsensia; Yulia Utami; Fathia Siregar; Muhammad Arifin
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 6 (2024): January : 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.Vol15.2024.721.pp306-313

Abstract

In this paper, systems of linear equations on sparse matrices investigated through modified improve method using Gauss-Seidel and successive overrelaxation (SOR) approach. Taking into adapted convergence rate on the Improve refinement Gauss-seidel outperformed the prior two Gauss-Seidel methods in terms of rate of convergence and number of iterations required to solve the problem by applying a modified version of the Gauss-Seidel approach. to observe the effectiveness of this method, the numerical example is given. The main findings in this study, that Gauss seidel improvement refinement gives optimum spectral radius and convergence rate. Similarly, the SOR improved refinement method gives. Considering their performance, using parameters such as time to converge, number of iterations required to converge and spectral radius level of accuracy. However, SOR works with relaxation values so that it greatly affects the convergence rate and spectral radius results if given greater than 1.
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
Classification of eucalyptus leaves: Combining color histogram feature extraction and decission tree algorithm Agustiani, Sarifah; Hidayat, Rahmat; Arifin, Yoseph Tajul; Haryani; Marlina, Siti
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 2 (2024): May: 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.Vol16.2024.731.pp58-69

Abstract

This research proposes an automatic approach to identify eucalyptus species based on leaf images using color histogram feature extraction and the Decision Tree algorithm. Eucalyptus is known as one of the most productive plants in the world with various uses in the timber, biofuel and pharmaceutical industries. However, its wide environmental adaptability and rapid growth pose challenges in identification and management. The proposed approach focuses on the use of Artificial Intelligence (AI) technology and image analysis to solve the identification problem. The color histogram feature extraction method is used to extract visual information about the color distribution of eucalyptus leaves. The Decision Tree algorithm is used to build a classification model based on the extracted features. Model evaluation is carried out using accuracy, precision, recall and F1-score metrics. The results showed that this approach was effective in identifying eucalyptus species, with a high level of accuracy. In addition, the development of this method offers opportunities for further applications in various fields, including forest mapping, mobile applications, and the timber industry. By combining advances in AI and image analysis, this research has the potential to become an important cornerstone of nature conservation and environmental sustainability efforts, and help strengthen natural resource management globally
Leveraging the BERT Model for Enhanced Sentiment Analysis in Multicontextual Social Media Content Saragih, Hondor; Manurung, Jonson
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 2 (2024): May: 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.Vol16.2024.766.pp82-89

Abstract

The increasing prevalence of social media platforms has led to a surge in user-generated content, necessitating advanced techniques for accurate sentiment analysis. This study investigates the application of the BERT model for sentiment analysis on multicontextual social media content, aiming to enhance sentiment classification accuracy by leveraging contextual embeddings. The research objectives include examining the effectiveness of BERT in capturing sentiments across diverse social media posts and evaluating its performance in comparison to traditional methods. The methodology involves tokenizing text content, converting tokens into contextual embeddings using BERT, and integrating multimedia features for a comprehensive sentiment analysis framework. The results from a numerical example demonstrate that the BERT model achieves a high probability of correctly classifying sentiments, with a notable improvement in accuracy and a low cross-entropy loss. These findings underscore the model's capability to understand contextual nuances and its potential to optimize social media monitoring and analysis processes. The study also highlights limitations such as the need for larger and more diverse datasets and the inclusion of multimedia content to enhance generalizability. Future research should explore hybrid models and address ethical considerations to ensure data privacy and mitigate biases. This work contributes to advancing theoretical frameworks and offers practical implications for businesses and marketers seeking to leverage sentiment analysis for informed decision-making and improved customer engagement strategies.
Graph-based Exploration for Mining and Optimization of Yields (GEMOY Method) Sihotang, Hengki Tamando; Riandari, Fristi; Sihotang , Jonhariono
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 2 (2024): May: 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.Vol16.2024.777.pp70-81

Abstract

This research explores the application of graph-based optimization techniques to enhance yield management and minimize transportation costs in industrial operations, particularly focusing on mining. By representing mining sites and processing plants as nodes and transportation routes as edges in a graph, we formulated an optimization problem aimed at maximizing yields while minimizing associated costs. Utilizing linear programming, we demonstrated significant cost savings, reducing transportation costs from 2100 units to 1700 units through optimized flow distribution. The study integrates elements of graph theory, optimization algorithms, and machine learning, providing a robust framework for efficient resource allocation and operational planning. The numerical example underscores the practical applicability of these techniques, paving the way for further research and refinement to accommodate additional constraints and dynamic changes in resource availability. This research highlights the potential of graph-based methods to achieve substantial economic and operational improvements across various industrial contexts.
Advanced graph neural networks for dynamic yield optimization and resource allocation in industrial systems Pujiastuti, Lise; Wahyudi, Mochamad
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 2 (2024): May: 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.Vol16.2024.785.pp90-102

Abstract

This research explores the integration of Graph Neural Networks (GNNs) and Reinforcement Learning (RL) for dynamic yield optimization and resource allocation in industrial systems. We present a numerical example involving a small manufacturing setup with three machines, where GNNs are employed to model complex interactions and derive meaningful embeddings of machine states. These embeddings are then used to predict yield and cost through linear combination functions. RL is utilized to optimize resource allocation dynamically, balancing yield and cost through a carefully designed reward function. The results demonstrate the effectiveness of GNNs in capturing machine interactions and the adaptability of RL in optimizing operational parameters in real-time. This combined approach showcases significant potential for enhancing efficiency, cost-effectiveness, and overall performance in various industrial applications, providing a robust framework for continuous improvement and adaptive decision-making in dynamic environments.
Toxicity and topic analysis of travel vlog content in digital era: perspective and multilingual embedding model (voyage-multilingual-2) Singgalen, Yerik Afrianto
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 3 (2024): July: 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.Vol16.2024.844.pp199-210

Abstract

This research investigates the complexities of online discourse by conducting a detailed toxicity and topic analysis of travel vlog content on user-generated platforms. By analyzing 1,503 posts using the Perspective API, the study finds generally low levels of toxicity, with an average toxicity score of 0.06995 and a peak of 0.78207, and similarly low average scores for severe toxicity, identity attack, insult, profanity, and threat (0.00654, 0.01237, 0.03778, 0.06241, and 0.01186, respectively). However, the highest recorded values for these measures—0.45895 for severe toxicity, 0.69287 for identity attack, 0.63084 for insult, 0.81864 for profanity, and 0.51957 for threat—highlight the sporadic presence of harmful content. Advanced clustering techniques, such as HDBScan, k-Means, and Gaussian Mixture models, enable a comprehensive examination of thematic diversity and sentiment distribution within the comments, offering valuable insights into audience engagement and perception. These findings underline the critical need for compelling content moderation and community management strategies to mitigate toxic behaviors and promote a positive digital environment. The study concludes that as digital media evolves, further research into toxicity, thematic content, and user engagement is essential for enhancing theoretical frameworks and practical applications in digital communication.

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