International Journal of Technology and Computer Science

Optimization of BPJS Health Facility Distribution with K-Means Clustering Algorithm

2025-02-01T03:55:35+00:00

This study aims to optimize the distribution of BPJS health facilities in Indonesia using the K-Means Clustering algorithm. The issue of unequal distribution of health facilities across various regions in Indonesia highlights the need for a more systematic and structured approach in planning the distribution of these facilities. The K-Means Clustering algorithm is used to group health facilities based on their location and type, with the goal of identifying uneven distribution patterns and providing recommendations for a fairer and more efficient distribution. The data used in this study includes information about health facilities, such as the type of facility, address, and geographical location. The analysis results show significant disparities in the distribution of health facilities in several provinces and major cities, and demonstrate the potential to improve the equitable distribution of health facilities through better planning. This research provides an important contribution in planning the optimal spread of BPJS health facilities to enhance healthcare service accessibility across Indonesia.

Development of an Intelligent Educational Chatbot Using NLP and Machine Learning

2025-07-29T01:24:33+00:00

This study aims to develop an intelligent educational chatbot using Natural Language Processing (NLP) and Machine Learning (ML) to support independent student learning in English. As digital learning increasingly demands adaptive and responsive tools, chatbots offer the potential to provide real-time, personalized interactions. The chatbot in this research was designed with transformer-based NLP models and trained using supervised learning techniques. The development process followed the Borg & Gall Research and Development (R&D) model, including stages such as needs analysis, system design, prototyping, testing, and refinement. Through the integration of NLP and ML, the chatbot was expected to deliver natural dialogue, contextual understanding, and accurate educational feedback. Testing was conducted with 15 eleventh-grade high school students using pre- and post-tests. Results showed a significant improvement in learning outcomes, with average scores rising from 61.3 to 84.2. In addition to academic gains, students reported increased motivation, confidence, and comfort with self-directed learning. These findings confirm that the developed chatbot is effective not only in delivering knowledge but also in enhancing students' engagement and autonomy. The study concludes that the application of AI technologies, particularly NLP and ML, in education holds great potential as an inclusive, efficient, and scalable solution for the future of digital learning.

A Literature Review on the Role of AI (Artificial Intelligence) in Industry 4.0 Transformation

2025-07-16T03:09:57+00:00

The integration of Artificial Intelligence (AI) has become a fundamental aspect of the technological transformation associated with Industry 4.0. This study presents a systematic qualitative literature review aimed at exploring the multifaceted role of AI in modern industrial ecosystems. By analyzing 25 peer-reviewed articles published between 2019 and 2024, the research identifies key thematic areas in which AI contributes to the evolution of industrial processes, including smart manufacturing, predictive maintenance, supply chain optimization, advanced analytics, and human-machine collaboration. The findings demonstrate that AI significantly enhances operational efficiency, supports real-time decision-making, and enables adaptive production environments. However, several ethical, organizational, and technological challenges remain, particularly in relation to data privacy, workforce readiness, and equitable access to AI technologies across industrial sectors. The study emphasizes the need for context-aware, transparent, and ethically aligned AI systems, and recommends interdisciplinary approaches that integrate legal, educational, and policy perspectives. It also highlights the importance of supporting small and medium-sized enterprises (SMEs) through inclusive strategies and policy incentives. While this review offers a comprehensive overview of the current academic discourse, it is limited by its reliance on secondary sources and English-language publications. Future research should adopt empirical and mixed-method approaches to deepen the understanding of AI’s real-world applications in Industry 4.0.

Analysis of the Effect of Using Artificial Intelligence on Critical Thinking Power of Students in the Digital Age

2025-07-08T00:14:03+00:00

This study examines the effect of digital technology utilization in the learning process on students, especially in developing critical thinking, analytical, and creativity skills. Technological innovations provide various conveniences, such as more focused presentation of materials, learning systems that adjust to individual needs, and automatic responses that support understanding. However, findings show that over-reliance on such technology can inhibit independence of thought, reduce analytical power and creativity, and increase the risk of violating academic integrity. Therefore, the integration of technology in education needs to be done in a balanced manner, so that the benefits continue to support the development of students' thinking competencies amid the challenges of the times.

AI-Based Business Management Transformation: Improving Operational Efficiency through Intelligent Systems

2025-07-11T12:36:51+00:00

Business management transformation through the application of artificial intelligence (AI) technology has become an increasingly relevant topic in this digital era. With increasing complexity and competition in the global market, companies are required to improve operational efficiency in order to remain competitive. This article aims to examine how AI can contribute to operational efficiency in various business sectors, as well as identify intelligent systems that can be implemented. Through an in-depth literature analysis, this article provides insight into the potential impact of AI in business management, as well as the challenges faced in its implementation.