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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 7 Documents
 1 
Search results for , issue "Vol 16 No 3 (2024): July: Intelligent Decision Support System (IDSS)" : 7 Documents clear
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.
Toxicity, social network and topic analysis of digital content: Perspective and multilingual embedding model 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.Vol16.2024.845.pp115-128

Abstract

This research presents a comprehensive approach to analyzing digital content by integrating toxicity analysis, clustering techniques, and Social Network Analysis (SNA) to understand online interactions better. The study finds that, while the average toxicity levels are relatively low, with scores such as 0.06355 for toxicity and 0.00468 for severe toxicity, there are significant spikes, reaching maximum scores of 0.82996 for toxicity and 0.89494 for profanity. These spikes highlight the necessity for continuous monitoring and adaptive moderation strategies to minimize the impact of harmful language. Clustering methods, including K-Means, HDBScan, and Gaussian Mixture models, provide deep insights into the thematic structure of viewer discourse, identifying both prevalent and niche topics. The Gaussian Mixture model identified ten distinct clusters, while HDBScan revealed varying cluster densities, reflecting the diverse range of discussions within the community. In addition, SNA, with 1,716 nodes and 37 edges, offers critical insights into the relational dynamics of the network, pinpointing key influencers and mapping the flow of information between different user groups. By synthesizing these methodologies, the research provides a robust framework for understanding the content and context of digital interactions, facilitating more effective strategies for enhancing community engagement, mitigating toxicity, and promoting a healthier, more inclusive online environment.
Topic modeling using LDA and performance evaluation of classification algorithm: k-NN, SVM, NBC, and DT 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.Vol16.2024.846.pp143-157

Abstract

This research investigates the integration of Latent Dirichlet Allocation (LDA) for topic modeling with the performance evaluation of various classification algorithms—specifically, k-nearest Neighbors (k-NN), Support Vector Machines (SVM), Naive Bayes Classifier (NBC), and Decision Trees (DT)—within the Digital Content Reviews and Analysis Framework. The framework systematically processes and analyzes digital content, including data cleaning, extraction, evaluation, and visualization techniques, to enhance machine learning models' interpretability and predictive accuracy. The study demonstrates that combining LDA with these classification algorithms significantly improves data interpretation and model performance, particularly in handling large-scale textual datasets. Notably, the Decision Tree algorithm achieved a 98.86% accuracy post-SMOTE. At the same time, the Support Vector Machine reached a near-perfect AUC of 1.000, highlighting the efficacy of these methods in managing imbalanced datasets. The findings provide valuable insights for optimizing model selection and developing more robust and adaptive machine-learning models across various applications. This research contributes to advancing the field of artificial intelligence by proposing a comprehensive framework that effectively addresses complex data-driven challenges, encouraging further exploration of more flexible and scalable models to accommodate evolving data environments.
Optimizing supply chain efficiency: Advanced decision support systems for enhanced performance Judijanto, Loso; Lemos, Sgarbossa Carlo; Sihotang , Jonhariono; Sihotang , Hengki Tamando
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.Vol16.2024.857.pp185-198

Abstract

This research investigates the optimization of supply chain efficiency through the application of advanced Decision Support Systems (DSS), focusing on minimizing operational costs while maintaining high service levels. The main objective is to explore how DSS, integrated with real-time data, artificial intelligence (AI), and machine learning (ML), can enhance decision-making processes across production, inventory management, and transportation. The research employs a multi-objective optimization model, developed to minimize production, inventory, transportation, and shortage costs, while dynamically adjusting decisions based on real-time demand and supply data. A numerical example is used to test the model’s effectiveness, revealing significant cost reductions in production and transportation but highlighting challenges in maintaining consistent service levels. The results indicate that DSS can substantially improve supply chain efficiency by enabling data-driven decisions in real time, though its adoption remains limited by technical and scalability challenges, particularly for small-to-medium enterprises (SMEs). This study contributes to the growing body of knowledge on supply chain optimization, offering practical insights into DSS implementation and its potential impact on operational performance. The conclusions suggest that future research should focus on developing more sophisticated DSS models capable of handling uncertainty, sustainability, and resilience, as well as enhancing scalability to make DSS more accessible to a broader range of businesses.
Leveraging AI for optimization in supply chain decision support: Enhancing predictive accuracy Judijanto, Loso; Riandari, Fristi; Marsoit, Patrisia Teresa
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.Vol16.2024.858.pp171-184

Abstract

This research explores the use of AI-driven techniques to optimize supply chain decision-making by integrating demand forecasting, inventory management, and logistics optimization. The main objective is to enhance predictive accuracy while minimizing overall supply chain costs through the application of machine learning and reinforcement learning methods. The research design involves the development of a comprehensive mathematical model that combines AI-based demand forecasting with cost optimization in inventory and transportation. A machine learning model is employed to predict demand, while optimization techniques are used to minimize inventory and logistics costs. Reinforcement learning is introduced as a method for real-time decision-making, allowing the system to continuously adapt and improve. The methodology involves testing the model through a numerical example, where predicted demand is used to optimize inventory and logistics costs. The main results show that the AI-based model achieves a demand forecasting accuracy with a Mean Squared Error (MSE) of 50, resulting in a total supply chain cost of 760 units, which includes both inventory and transportation costs. Despite the initial prediction error, the model demonstrates the potential for cost savings and operational efficiency through better alignment of supply chain components. The research concludes that while the AI-driven approach offers significant improvements in supply chain management, further refinement of the predictive model and the practical application of reinforcement learning are necessary to fully realize its benefits. Future research should focus on enhancing model accuracy and scalability in real-world supply chain environments
Leveraging AI for optimization in decision support systems: Enhancing decision quality Judijanto, Loso; Collins, Cinelli; Iatagan, Qawqzeh
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.Vol16.2024.859.pp158-170

Abstract

This research investigates the integration of multi-objective optimization and reinforcement learning (RL) to enhance decision-making within a Decision Support System (DSS), with a focus on dynamic manufacturing environments. The primary objective is to optimize decision quality by balancing three conflicting objectives: minimizing production costs, maximizing efficiency, and minimizing risk, while adapting to real-time changes in demand. The research employs a hybrid approach, combining static optimization to compute Pareto-optimal solutions with RL to enable the system to learn from feedback and improve over time. The research design involves developing a mathematical model that integrates both techniques, followed by a numerical example to test its effectiveness in balancing the objectives. The methodology includes formulating cost, efficiency, and risk functions, solving the multi-objective optimization problem, and implementing a Q-learning-based RL algorithm to refine decision-making based on real-time data. The model was tested using time-dependent demand to simulate a realistic production environment. The main results demonstrate that the hybrid model effectively balances conflicting objectives, with the RL component adapting production decisions to fluctuating market conditions. The system identified an optimal production level around x=60 units, offering a balance between cost, efficiency, and risk. The findings highlight the model's capability to enhance decision-making adaptability in dynamic environments compared to traditional static approaches. In conclusion, this research provides a novel method for improving decision quality in DSS by integrating multi-objective optimization and RL, offering valuable insights for industries requiring adaptive, real-time decision-making. Future research could extend this model to more complex environments and explore its scalability in larger, real-world applications
Optimizing expert systems: Advanced techniques for enhanced decision-making efficiency Judijanto, Loso; Simatupang, Christine Debora; Doyle, Heckerman
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.Vol16.2024.860.pp129-142

Abstract

This research aims to develop a unified mathematical formulation to optimize expert systems by integrating advanced techniques in knowledge representation, inference mechanisms, machine learning, and parallel/distributed processing. The primary objective is to enhance decision-making efficiency in expert systems by optimizing the interaction between these components. The research design focuses on building a comprehensive model that combines ontology-based and frame-based knowledge representation, forward and backward chaining inference, neural networks, Bayesian networks, fuzzy logic, and parallel computing. The methodology includes defining efficiency metrics for each component and combining them into a single optimization model. A numerical example was tested using simulated data to evaluate the performance of the proposed system. Key results show that frame-based knowledge representation, forward chaining, and parallel processing contribute significantly to overall system efficiency. The neural network's low loss function and the Bayesian network's high likelihood value confirm the effective integration of machine learning into the expert system. The research concludes that the unified optimization framework significantly improves decision-making efficiency, with a total efficiency score of 23.09. This approach fills a gap in previous studies, which often focus on individual components in isolation, by providing a holistic model that optimizes all aspects of expert systems simultaneously. Future research should focus on real-world implementations and fine-tuning the model to handle dynamic environments and complex decision-making tasks.

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