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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 4 Documents
 1 
Search results for , issue "Vol 16 No 2 (2024): May: Intelligent Decision Support System (IDSS)" : 4 Documents clear
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.

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