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Journal : Journal of Computer Science Artificial Intelligence and Communications

Implementation of Natural Language Processing for Chatbots in Customer Service Iqbal, Muhammad; Siregar, Muhammad Noor Hasan; Rismayanti, Rismayanti
Journal of Computer Science, Artificial Intelligence and Communications Vol 1 No 1 (2024): May 2024
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v1i1.4

Abstract

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.
Utilization of Sales Data Analysis for Product Recommendation Systems in E-Commerce Using the Apriori Algorithm Muhammad Noor Hasan Siregar; Furqan Khalidy; Rismayanti; Khairunnisa
Journal of Computer Science, Artificial Intelligence and Communications Vol 1 No 2 (2024): November 2024
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v1i2.17

Abstract

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.
Evaluating the Impact of Knowledge Management Systems on Organizational Performance: A Technology Company Case Yasir, Amru; Apriadi, Deni; Siregar, Muhammad Noor Hasan; Handoko, Divi; Rahman, M. Arif
Journal of Computer Science, Artificial Intelligence and Communications Vol 2 No 1 (2025): May 2025
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v2i1.27

Abstract

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.