Journal of Computer Science Artificial Intelligence and Communications
Journal of Computer Science Artificial Intelligence and Communications is a multidisciplinary, peer-reviewed journal dedicated to advancing research in computer science, artificial intelligence (AI), and communication technologies. The journal publishes high-quality original articles, reviews, and case studies that explore the latest innovations, theories, algorithms, and applications shaping the digital world. Focused on the intersection of computational systems, intelligent automation, and seamless communication networks, JOCSAIC aims to foster collaboration and knowledge exchange among researchers, practitioners, and academics working across diverse sectors such as data science, machine learning, telecommunications, and intelligent systems. The journal is a key resource for cutting-edge developments and trends in these transformative fields. Journal Name : Journal of Computer Science Artificial Intelligence and Communications Initial : JOCSAIC Frequency of Publication : 2 Times a Year (May and November) DOI Prefix : https://doi.org/10.64803/jocsaic ISSN: 3108-9828 (Online Media) p-ISSN: 3109-3981 (Print Media) OAI: https://journals.raskhamedia.or.id/index.php/jocsaic/oai Citation Analysis: Google Scholar, Garuda
Machine Learning and Deep Learning Approaches for Weather Forecasting in IoT-Based Systems: A Review
Weather forecasting is important across many fields, including agriculture, transportation, energy, and disaster management. Because meteorological data is not linear or dynamic and changes over time, it is hard to predict the weather. Technological advances have made machine learning and deep learning methods more common for improving the accuracy of weather forecasts. Additionally, connectivity with the Internet of Things (IoT) enables real-time data collection through various environmental sensors. This study conducted a comprehensive literature review of machine learning, deep learning, and hybrid methodologies for IoT-based weather prediction systems. The methodologies analyzed included Random Forest, Support Vector Machine, Artificial Neural Network, Long Short-Term Memory, Gated Recurrent Unit, Convolutional Neural Network, and Transformer. The results showed that deep learning and hybrid models performed better than traditional methods, especially for finding temporal patterns and non-linear correlations. Still, other issues need to be addressed, such as data quality, model complexity, high processing requirements, and limitations on how quickly it can adapt. As a result, combining AI and IoT has significant potential to make weather forecasting systems more accurate, flexible, and timely, especially for early warning systems based on data.
Copyright (c) 2026 Hasanur Mohammad Firdausi (Author) https://creativecommons.org/licenses/by-sa/4.0
Evaluation of the Usability of the Academic Information System Using the System Usability Scale (SUS) Method
The increasing reliance on digital platforms in higher education necessitates the evaluation of system usability to ensure effective user interaction and satisfaction. This study aims to evaluate the usability of the Academic Information System (AIS) at [University Name] using the System Usability Scale (SUS) method. SUS is a reliable, standardized tool for measuring the usability of interactive systems, providing a quick and quantitative assessment. Data were collected from a sample of 100 students and academic staff who frequently use the AIS for various academic activities, including course registration, grade checking, and academic planning. The results of the SUS analysis yielded an average score of 72.5, indicating that the system falls within the “Good” usability category. However, several usability issues were identified, such as navigation complexity and visual layout inconsistencies, which slightly reduced user satisfaction. These findings highlight the importance of continuous usability testing and user-centered design in the development of academic systems. The study recommends specific design improvements to enhance user experience and system performance. Overall, the SUS method proved effective in identifying usability strengths and weaknesses, offering valuable insights for future system optimization.
Copyright (c) 2025 Rifky Lana Rahardian, Siti Khodijah, Cindy Atika Rizki (Author) https://creativecommons.org/licenses/by-sa/4.0
Development of an Employee Performance Monitoring Information System Using a Web-Based Interactive Dashboard
This research aims to develop an employee performance monitoring information system that utilizes a web-based interactive dashboard to enhance decision-making and managerial oversight. In many organizations, traditional performance evaluation methods are often time-consuming, static, and lack real-time insight, resulting in inefficiencies in performance tracking. To address these challenges, the proposed system is designed to provide dynamic visualization of key performance indicators (KPIs), attendance records, task completion rates, and other critical metrics through an interactive and user-friendly dashboard interface. The development process follows the Waterfall methodology, encompassing stages of requirements analysis, system design, implementation, testing, and deployment. The system was built using PHP and JavaScript for front-end interactivity, with a MySQL database to manage data storage. The dashboard includes various visual tools such as graphs, charts, and progress bars to facilitate real-time monitoring and performance analysis. Testing results indicate that the system performs effectively, offering accurate and timely information that supports employee evaluation and organizational planning. User feedback also reveals a high level of satisfaction due to the dashboard's ease of use and responsiveness. Overall, the implementation of this web-based performance monitoring system is expected to improve transparency, accountability, and productivity within the organization.
Copyright (c) 2025 Abil Alwi Prayoga, Muhammad Hasanuddin, Siti Khodijah, Cindy Atika Rizki, Dahrim PA (Author) https://creativecommons.org/licenses/by-sa/4.0
Analysis of the Effectiveness of Implementing a Queue Algorithm-Based Leadership Scheduling Information System in Government Agencies
This study analyzes the effectiveness of implementing a leadership scheduling information system that utilizes queue algorithms in government agencies. The main objective is to evaluate how the integration of algorithm-based scheduling systems improves efficiency, accuracy, and transparency in managing executive-level appointments and meetings. The research adopts a mixed-method approach, combining quantitative analysis through system performance metrics with qualitative feedback from end-users, including administrative staff and decision-makers. Findings indicate a significant improvement in scheduling efficiency, with reduced conflicts, optimized time slots, and better coordination between departments. Furthermore, the system minimizes manual intervention, thus decreasing administrative errors and enhancing data integrity. The queue algorithm enables a first-come-first-served mechanism that ensures fairness while allowing for priority-based modifications in urgent cases. The implementation also receives positive responses in terms of user satisfaction and perceived usefulness. However, challenges such as user adaptation and technical limitations were identified, suggesting a need for continuous training and system updates. Overall, the integration of a queue algorithm-based scheduling system proves to be an effective solution for improving leadership-level administrative processes in government institutions.
Copyright (c) 2025 Mardiah, Nuranisah, Theodora MV Nainggolan (Author) https://creativecommons.org/licenses/by-sa/4.0
Identification of Book Cover Titles Using the Natural Language Processing (NLP) Method
In the digital era, the identification of book titles on covers has become a crucial requirement in digital library management, archiving systems, and book e-commerce platforms. The main challenges lie in the limitations of manual methods and traditional pattern-matching techniques, which are inefficient, as well as in the complexity of processing the Indonesian language, which exhibits diverse morphological variations and syntactic structures. To address these issues, this study proposes the integration of Optical Character Recognition (OCR) with the Natural Language Processing (NLP) method. OCR is utilized to extract textual information from book cover images, while NLP is applied to recognize and classify the extracted text to identify the main book title. The implementation results demonstrate that this approach significantly improves title identification accuracy compared to traditional methods, particularly through the application of Named Entity Recognition (NER) techniques and modern NLP models such as BERT and LSTM. The developed system proves effective in accelerating the book digitalization process, enhancing information management efficiency, and contributing to the advancement of Indonesian language processing technology.
Copyright (c) 2025 Rianti afifah Gultom, Munjiat Setiani Asih, Ade Zulkarnain Hasibuan (Author) https://creativecommons.org/licenses/by-sa/4.0
Classification of Customer Credit Risk Levels Using the Random Forest Method: A Case Study on Microfinance Institutions
Credit risk classification plays a crucial role in supporting financial institutions, especially microfinance institutions, in assessing the ability of customers to repay loans. This study aims to develop a credit risk classification model using the Random Forest method, which is known for its accuracy and robustness in handling classification problems. The research uses a dataset obtained from a microfinance institution consisting of various customer attributes such as income, age, loan amount, repayment history, and employment status. The dataset is preprocessed and divided into training and testing sets to evaluate model performance. The Random Forest algorithm is then applied to build a classification model that categorizes customers into three credit risk levels: low, medium, and high. The results show that the Random Forest model achieves a high level of accuracy, with a classification precision of 89%, recall of 87%, and F1-score of 88%. These findings indicate that Random Forest is an effective technique for credit risk classification and can be implemented by microfinance institutions to support better decision-making in credit approval processes. This research also highlights the potential of machine learning techniques in enhancing credit risk management and minimizing non-performing loans.
Copyright (c) 2025 Fera Damayanti, Arief Budiman, Siti Sundari, Theodora MV Nainggolan (Author) https://creativecommons.org/licenses/by-sa/4.0
Optimizing Regional Budget Planning: The Role of Information Systems and Human Resource Competence in Padang Panjang City
This study aims to examine the effect of the implementation of the Regional Government Information System (SIPD) application and human resource (HR) capacity on the process of preparing the Regional Revenue and Expenditure Budget (APBD) of Padang Panjang City. The APBD preparation process must comply with applicable laws and regulations as well as the predetermined schedule to avoid delays in its ratification. The implementation of the SIPD application and the competence of the human resources involved in the APBD preparation process can be considered as crucial factors in the budgeting process of local governments. This research was conducted to ensure that stakeholders involved in the APBD preparation process in Padang Panjang City are aware of and can improve the factors that support the preparation process, so that the resulting APBD complies with statutory regulations and can be ratified according to the established timeline. This research employs a quantitative approach to analyze data by describing and illustrating the collected information. The data analysis technique used is Partial Least Square (PLS), with the population consisting of all Regional Work Units (SKPD) in Padang Panjang City and a sample of 50 respondents. The findings of this study indicate that both the implementation of the SIPD application and HR capacity have a positive and significant effect on the APBD preparation process.
Copyright (c) 2025 Jennisa Dwina Indriani, Muhammad Ihsan, Delfi Hurnis, Sony Irwan (Author) https://creativecommons.org/licenses/by-sa/4.0
Integration of Feed Nutrient Analysis and Predictive Model in Assessing the Performance of Native Chickens fed with Rhizopus sp. Fermented Rice Bran
The study aimed to determine the effect of giving fermented rice bran with Rhizopus sp on the performance of free-range chickens. This study used a non-factorial Complete Random Design (RAL) method with 4 treatments and 5 replicates. The treatment used was as follows: P0 = control, P1 = ration + 10% DPF, P2 = ration + 15% DPF and P3 = ration + 20% DPF. The results of the study showed that the administration of fermented rice bran can affect the performance of free-range chickens, namely ration consumption, body weight gain, and ration conversion. The results of the fingerprint analysis showed that the P2 treatment (ration + 15% DPF) had a conversion of 3.64 and the highest body weight gain of 63.81 g.
Copyright (c) 2025 Risdawati Ginting, Supiyandi, Leni Marlina, Giovany Ginting (Author) https://creativecommons.org/licenses/by-sa/4.0
Analysis of Knowledge Sharing Success in Multinational Firms: Organizational Culture Perspective
This study explores the critical role of organizational culture in shaping the success of knowledge-sharing initiatives within multinational firms. As knowledge becomes a key competitive asset in the global business environment, organizations increasingly rely on effective knowledge sharing to foster innovation, improve decision-making, and enhance operational efficiency. However, in multinational contexts, differences in national and organizational cultures can significantly influence the willingness and ability of employees to share knowledge. Using a qualitative approach, this research examines case studies of selected multinational corporations to identify cultural enablers and barriers to successful knowledge sharing. Key findings reveal that supportive leadership, trust-based relationships, open communication channels, and a shared vision contribute significantly to knowledge sharing success. Conversely, hierarchical structures, fear of judgment, and cultural misalignment across subsidiaries often hinder knowledge flows. The study highlights the importance of fostering a culture that values collaboration, openness, and mutual respect across diverse cultural settings. Practical implications include the need for multinational firms to design culturally sensitive knowledge management strategies and invest in leadership development that promotes inclusive and knowledge-friendly environments. The findings contribute to both academic and practical discussions by demonstrating that knowledge sharing is not solely a technical challenge but also a deeply cultural one, requiring attention to the complex social dynamics within global organizations.
Copyright (c) 2025 Supiyandi Supiyandi, Muhammad Hasanuddin, Cindy Atika Rizki, Siti Khodijah (Author) https://creativecommons.org/licenses/by-sa/4.0
Evaluating the Impact of Knowledge Management Systems on Organizational Performance: A Technology Company Case
This study aims to evaluate the impact of Knowledge Management Systems (KMS) on organizational performance within a technology company. In the digital era, knowledge has become a critical asset that drives innovation, efficiency, and competitive advantage. By leveraging a case study approach, the research examines how the implementation of KMS influences various performance indicators, including productivity, decision-making quality, employee collaboration, and knowledge retention. Data were collected through interviews, observations, and internal documents, and analyzed using a mixed-method approach. The findings suggest that effective use of KMS significantly improves organizational agility and innovation capabilities. However, the study also identifies challenges such as resistance to change, lack of user training, and insufficient integration with existing workflows. To maximize the benefits of KMS, organizations must foster a knowledge-sharing culture, provide ongoing support, and align KMS strategies with business objectives. The insights from this research are expected to contribute to the development of more effective knowledge management practices in technology-based organizations.
Copyright (c) 2025 Amru Yasir, Deni Apriadi, Muhammad Noor Hasan Siregar, Divi Handoko, M. Arif Rahman (Author) https://creativecommons.org/licenses/by-sa/4.0
The Role of Knowledge Management in Enhancing Innovation in R&D Industry: A Literature and Empirical Review
Innovation is a key driver of competitiveness and sustainability in the Research and Development (R&D) industry. As knowledge-intensive organizations, R&D institutions rely heavily on the effective management of knowledge to accelerate discovery, streamline processes, and create value. This study aims to examine the role of Knowledge Management (KM) in enhancing innovation within the R&D sector by reviewing relevant literature and analyzing empirical findings from selected case studies. The review highlights that KM practices such as knowledge sharing, codification, retention, and collaboration are closely linked to higher innovation performance. Empirical evidence shows that organizations that invest in structured KM frameworks tend to exhibit greater adaptability, faster product development cycles, and improved cross-functional synergy. However, challenges such as cultural resistance, lack of incentives, and inadequate technological support still hinder optimal KM implementation. The findings suggest that integrating KM into the core innovation strategy, supported by leadership commitment and digital infrastructure, can significantly boost innovation capacity in R&D environments. This study contributes to the growing discourse on KM by offering insights into its practical and strategic relevance for innovation management in knowledge-driven industries.
Copyright (c) 2025 Mardiah Mardiah, Furqan Khalidy, Arief Budiman (Author) https://creativecommons.org/licenses/by-sa/4.0
The Use of Knowledge Management Systems to Improve Decision-Making in Local Government
Effective and data-driven decision-making has become an urgent need for local governments in facing the challenges of public service complexity, socio-economic dynamics, and demands for transparency and accountability. One of the strategic approaches to support this process is through the implementation of a Knowledge Management System (KMS). This research aims to explore the role and impact of KMS implementation on the improvement of decision-making quality in regional government organizations. A qualitative approach is used in this study with a case study method on several regional government agencies in Indonesia that have implemented KMS, combined with an analysis of related academic literature. Research results show that KMS is capable of improving the efficiency of storage, distribution, and access to organizational knowledge, both tacit and explicit. KMS supports faster, more accurate, and participatory decision-making because strategic information can be obtained and used promptly by policymakers. The findings also indicate that the success of KMS implementation is greatly influenced by organizational culture, leadership support, and the capacity of human resources in managing and sharing knowledge. This study recommends the comprehensive integration of KMS into the government work system, with an emphasis on training aspects, digital infrastructure, and internal policies that support the knowledge-sharing process. The theoretical and practical implications of these findings are an important contribution to the development of knowledge-based governance at the regional level.
Copyright (c) 2025 Rusmin Saragih, Yuyun Dwi Lestari, Yessi Fitri Annisah Lubis, Divi Handoko (Author) https://creativecommons.org/licenses/by-sa/4.0
Impact of Mobile Technology Use on Knowledge Management in the Education Sector
The integration of mobile technology into knowledge management (KM) practices has reshaped the landscape of information sharing and learning in the education sector. This study explores how mobile devices and applications contribute to the efficiency, accessibility, and effectiveness of KM processes among educators, students, and administrators. With the growing adoption of smartphones, tablets, and mobile learning platforms, educational institutions are experiencing a shift from traditional knowledge repositories to dynamic, real-time knowledge exchange environments. The research employs a mixed-method approach involving surveys and in-depth interviews with teachers, students, and IT staff across several secondary and higher education institutions. The findings reveal that mobile technology enhances knowledge acquisition and dissemination by enabling anytime-anywhere access to learning materials, collaborative tools, and institutional knowledge databases. However, challenges such as data security, digital literacy gaps, and resistance to change remain significant barriers to optimal utilization. Furthermore, the study highlights the role of institutional policies and support systems in facilitating effective mobile-based KM adoption. The results indicate that institutions with clear mobile technology strategies and investments in user training are more likely to achieve improved knowledge-sharing outcomes. This research provides practical insights into leveraging mobile technology to strengthen KM frameworks in education and emphasizes the need for continuous adaptation to technological advancements to sustain knowledge-based performance improvements.
Copyright (c) 2025 Muhammad Eka, Yessi Fitri Annisah Lubis, Divi Handoko, Rismayanti Rismayanti, Supiyandi Supiyandi (Author) https://creativecommons.org/licenses/by-sa/4.0
Copyright (c) 2024 Fera Damayanti, Arief Budiman, Siti Sundari, Theodora MV Nainggolan (Author) https://creativecommons.org/licenses/by-sa/4.0
Analysis of Historical Student Visit Data Using Time Series Algorithm
The analysis of historical student visit data plays a critical role in understanding student behavior, optimizing campus resources, and enhancing service delivery in educational institutions. This study presents an analytical approach to examine patterns and trends in student visitations using a time series algorithm. By leveraging historical datasets from campus access logs, we aim to identify periodic behaviors, peak visitation times, and anomalies that may reflect special events or system irregularities. The research employs time series methods such as moving average, exponential smoothing, and ARIMA (AutoRegressive Integrated Moving Average) to forecast future student visit patterns based on previous trends. Data preprocessing, normalization, and visualization techniques are applied to ensure data quality and interpretability. The results demonstrate that student visits tend to follow specific weekly and monthly patterns, with increased activity near academic deadlines or events. The ARIMA model, in particular, shows strong predictive accuracy with minimal error margin. This analysis not only provides insights for administrative planning—such as scheduling staff, managing facilities, or enhancing security—but also serves as a foundation for developing intelligent decision-support systems. In conclusion, applying time series algorithms to historical student visitation data proves effective in predicting future trends, thereby supporting data-driven decision-making processes within educational institutions.
Copyright (c) 2024 Sri Ramadhany, Sahara Abdy, Alfiarini (Author) https://creativecommons.org/licenses/by-sa/4.0
Visualization and Analysis of Employee Performance Data Using a Power BI-based Business Intelligence Dashboard
In the current digital and competitive era, the utilization of Business Intelligence (BI) technology has become crucial in supporting data-driven decision-making. This research aims to develop and analyze a Power BI-based Business Intelligence dashboard focused on visualizing employee performance. This study was conducted by collecting performance data from the Human Resource Information System (HRIS), which was then processed and visualized in the form of key metrics such as attendance rates, individual target achievements, productivity per division, and periodic performance evaluations. Power BI was chosen for its ability to integrate various data sources and present interactive visualizations that are easy for management to understand. The methodology used involves the ETL (Extract, Transform, Load) process, data model design, and the development of visual reports that support descriptive and comparative analysis. The results of this study indicate that the use of BI dashboards significantly helps the company in monitoring employee performance in real-time, identifying trends in productivity decline, and designing data-driven improvement strategies. In addition, this dashboard also serves as an effective communication tool between management and the HR division. Thus, the use of Power BI as a tool for visualization and performance analysis adds significant value to the strategic and data-driven management of human resources
Copyright (c) 2024 Imeldawaty Gultom, Eka Pandu Cynthia, Maulidania Mediawati Chinthia (Author) https://creativecommons.org/licenses/by-sa/4.0
Sentiment Analysis of Social Media Towards Public Services Using Naive Bayes and Text Mining
The rapid development of information and communication technology has driven the increased use of social media as a means of interaction between the public and service providers. Social media has become a platform for the public to express their opinions on the quality of services they receive, whether in the form of praise, suggestions, or complaints. Therefore, sentiment analysis of social media data can be a strategic tool in evaluating the performance of public services. This research aims to analyze public sentiment towards public services by utilizing text mining techniques and the Naive Bayes Classifier algorithm. The data used was collected from social media platforms such as Twitter and Facebook, followed by a text preprocessing stage that included tokenizing, stopword removal, and stemming. Subsequently, the data was analyzed to classify sentiment into positive, negative, and neutral categories. The test results show that the Naive Bayes algorithm is capable of classifying data with a satisfactory level of accuracy, making it an efficient method for monitoring public perception in real-time. This research contributes to supporting decision-making by government agencies regarding the improvement of public service quality based on publicly available feedback from social media
Copyright (c) 2024 Rusmin Saragih, Mardiah, Deni Apriadi (Author) https://creativecommons.org/licenses/by-sa/4.0
Utilization of Sales Data Analysis for Product Recommendation Systems in E-Commerce Using the Apriori Algorithm
The rapid development of e-commerce has significantly increased the volume of sales transactions and customer interaction data. This presents an opportunity for businesses to leverage data mining techniques to extract valuable insights that support decision-making processes. One such application is the development of product recommendation systems, which play a crucial role in enhancing customer satisfaction and driving sales. This research focuses on utilizing sales transaction data to build a product recommendation system using the Apriori algorithm, a well-known method for association rule mining. The study begins with the collection and preprocessing of transaction data from an e-commerce platform. Through the application of the Apriori algorithm, frequent itemsets are identified, and association rules are generated based on specified support and confidence thresholds. These rules reveal purchasing patterns and relationships between products that are frequently bought together. The system then uses these patterns to recommend relevant products to users, aiming to improve cross-selling opportunities and personalize the shopping experience. The results demonstrate that the Apriori-based recommendation model is effective in identifying meaningful product combinations and can be implemented as a lightweight, interpretable alternative to more complex machine learning methods. Furthermore, the system helps e-commerce businesses optimize inventory management and marketing strategies by understanding customer buying behavior. This research concludes that the integration of the Apriori algorithm into recommendation systems provides tangible benefits for e-commerce platforms seeking data-driven personalization solutions.
Copyright (c) 2024 Muhammad Noor Hasan Siregar, Furqan Khalidy, Rismayanti, Khairunnisa (Author) https://creativecommons.org/licenses/by-sa/4.0
A Narrative Exploring the Potential of ChatGPT: How AI Models Are Changing the Way We Interact with Technology
This study explores the perceptions, attitudes, and ethical considerations surrounding the use of ChatGPT among university students. By combining quantitative and qualitative research methods, including surveys and a review of existing literature, the study examines how ChatGPT is utilized in academic settings and its impact on learning outcomes, academic integrity, and scholarly achievements. The findings suggest that ChatGPT significantly enhances students' productivity, learning experiences, and writing abilities. However, concerns regarding its potential misuse, particularly about academic integrity, plagiarism, and over-reliance on AI tools, were also identified. The research highlights the importance of establishing clear ethical guidelines and policies to regulate the use of AI in educational settings. Future research should focus on the long-term effects of ChatGPT on students' academic development and investigate strategies for promoting responsible AI usage in higher education.
Copyright (c) 2024 Muhammad Eka, Munjiat Setiani Asih, Fera Damayanti, Rusmin Saragih, Supiyandi Supiyandi (Author) https://creativecommons.org/licenses/by-sa/4.0
Implementation of Natural Language Processing for Chatbots in Customer Service
The development of artificial intelligence technology has driven significant transformations in various sectors, including customer service. One of its increasingly developed applications is the use of chatbots based on Natural Language Processing (NLP). This research aims to explore the implementation of NLP in chatbots to enhance efficiency, accuracy, and customer satisfaction in digital customer service systems. By using descriptive analysis methods and case studies on several customer service platforms, this research examines how NLP components such as natural language processing, sentiment analysis, and context understanding are used to automatically and relevantly respond to customer inquiries. The analysis results show that chatbots equipped with NLP are capable of understanding human language more naturally, answering questions with appropriate context, and significantly reducing the workload of human agents. Additionally, the integration of NLP allows for personalized responses and continuous learning from previous interactions. However, there are also challenges such as limitations in understanding language ambiguity and the need for large training data. This research concludes that the implementation of NLP in chatbots is a strategic step to improve customer service quality, but it must be supported by the design of adaptive and user experience-oriented systems.
Natural Language Processing, chatbot, customer service, artificial intelligence, sentiment analysis, natural language processing
Copyright (c) 2024 Muhammad Iqbal, Muhammad Noor Hasan Siregar, Rismayanti Rismayanti (Author) https://creativecommons.org/licenses/by-sa/4.0
Optimization of Computer Network Performance Using Heuristic Algorithms
As the complexity and demand for speed and efficiency in modern computer networks increase, network performance optimization becomes a major challenge in the world of information technology. Issues such as high latency, limited bandwidth, and uneven load distribution often hinder network performance. This research aims to explore and implement heuristic algorithms as a solution to optimize computer network performance. Heuristic algorithms, such as genetic algorithms, ant colony optimization, and particle swarm optimization, offer adaptive and efficient approaches in seeking optimal solutions to complex problems that cannot be solved exactly in a reasonable time. This research conducts simulations of various network scenarios, focusing on optimal route selection, traffic management, and network resource allocation. The simulation results show that the use of heuristic algorithms can increase throughput, reduce delay, and improve bandwidth utilization efficiency compared to conventional approaches. Additionally, the algorithms used are capable of dynamically adapting to changes in topology and network conditions. These findings demonstrate the great potential of heuristic algorithms in managing future networks that are smarter and more responsive. This research provides theoretical and practical contributions to the development of efficient, flexible, and real-time operationally capable network systems.
Computer Network Optimization, Heuristic Algorithms, Genetic Algorithms, Ant Colony Optimization, Particle Swarm Optimization
Copyright (c) 2024 Muhammad Hasanuddin (Author) https://creativecommons.org/licenses/by-sa/4.0
Analysis of the Role of Social Media in Shaping Public Opinion on Social Issues
This research aims to analyze the role of social media in shaping public opinion on social issues that are developing in society. Social media has become the main platform for the dissemination of information and public communication, replacing the traditional role of mass media in shaping public perception. This research uses a qualitative approach with literature study methods and observation of public interactions on various social media platforms such as Twitter, Instagram, and Facebook. The research results show that social media has a significant influence in shaping public opinion, especially through content virality, the role of influencers, and algorithmic mechanisms that reinforce echo chambers. Social issues such as gender equality, the environment, and social justice become active discussion topics and are capable of creating digital social movements. However, the role of social media also brings challenges such as the spread of misinformation and opinion polarization. Thus, a deeper understanding of social media dynamics becomes important for the healthy and constructive management of public issues. This research is expected to contribute to the development of more effective social communication strategies in the digital era
Social media, public opinion, social issues, disinformation, digital communication
Copyright (c) 2025 Muhammad Hasanuddin, Siti Khodijah, Cindy Atika Rizki (Author) https://creativecommons.org/licenses/by-sa/4.0
Integration of Artificial Intelligence in Modern Communication Systems
The integration of Artificial Intelligence (AI) in modern communication systems marks a transformative era in the way data is transmitted, processed, and interpreted. This study explores the role of AI technologies—including machine learning, deep learning, and natural language processing—in enhancing the performance, efficiency, and security of communication networks. By examining various AI-driven applications such as intelligent routing, automated network management, and real-time speech and image recognition, this research highlights the significant improvements in latency reduction, bandwidth optimization, and system adaptability. Furthermore, the paper discusses challenges in AI implementation, including data privacy, computational complexity, and ethical concerns. The findings emphasize that while AI presents vast opportunities for innovation in communication systems, a robust framework is necessary to ensure reliability, transparency, and sustainable development.
Artificial Intelligence, Communication Systems, Machine Learning, Network Optimization, Data Security
Copyright (c) 2025 Siti Khodijah, Muhammad Hasanuddin, Cindy Atika Rizki (Author) https://creativecommons.org/licenses/by-sa/4.0