Publication Search

The purpose of this research is to develop a data search system for thesis and internship reports at the Faculty of Engineering Library of Gajah Putih University Takengon (UGP). This search engine will be created and used to help students and library employees access thesis and internship report information. Analysis of user needs, system design, creation of effective search algorithms, and evaluation of system performance are all topics that will be discussed in this thesis. Interviews with potential users, satisfaction surveys, and historical data collection of library usage are the methods used. It is expected that the results of this research will help library users find and retrieve thesis and internship report data and improve the accessibility and availability of academic information at the UGP Faculty of Engineering. When search engine technology is used, it is expected that the time required for Information will increase productivity, improve efficiency, and support the academic development of students at UGP.

Technological advancements have brought significant transformations across various fields, including the application of machine learning in recommendation and classification systems. Machine learning leverages data processing, utilizes algorithms, and efficiently identifies patterns to produce accurate recommendations and predictions. This study aims to review machine learning-based recommendation system approaches, analyze model performance, and compare the algorithms used. A literature review was conducted by examining journals published in the past five years, focusing on algorithm implementation. The findings indicate that the Naïve Bayes algorithm delivers the best performance, achieving an accuracy of up to 97%. This algorithm is particularly well-suited for processing small to medium-sized datasets with high efficiency. The research provides comprehensive insights into the performance and limitations of various algorithms, serving as a valuable guide for future developments in the field.

In the era of artificial intelligence (AI), technology is growing rapidly and changing the way businesses are run. AI enables automation, big data analysis, and increased operational efficiency. However, this development also raises various ethical issues that companies need to pay attention to so as not to harm consumers, society, and the environment. This study explores the paradigm of artificial intelligence in the context of business ethics, focusing on its impacts, challenges, and potential benefits in the business world. This study uses a systematic literature review approach from various reputable sources. The article criteria include literature published from 2019 to 2024 with the theme of business ethics in the AI era, and other articles relevant to this study. Based on the search, 15 articles were determined that were relevant to the predetermined criteria. The results of the study found that in this study, AI is not only a technical tool to improve operational efficiency, but is also able to redefine business ethics in the modern era. AI offers great potential to overcome traditional ethical challenges and create new paradigms in business decision making. However, this opportunity is also accompanied by significant challenges, such as algorithmic bias, data privacy, and lack of regulation. This discussion will review the role of AI in business ethics, the benefits it brings, the challenges it faces, and the steps that can be taken to optimize the ethical use of AI.

This research aims to implement the C4.5 algorithm in predicting bad credit in digital loan systems in the FinTech industry. The C4.5 algorithm was chosen because of its ability to handle numeric and categorical attributes, as well as produce a decision tree that can be interpreted easily. This research uses a dataset containing customer transaction and profile information, such as employment status, income and payment history. Test results show that the C4.5 algorithm is able to achieve an accuracy of 89.6% in predicting the possibility of bad credit, so it can help FinTech companies manage credit risk more effectively.

This community service program is designed to help MSMEs in Kedung Waringin Village utilize TikTok as a creative marketing medium. A total of 22 business actors participated in the training which included theory and practice, including understanding the TikTok algorithm, creating video content, and using features such as hashtags and TikTok Shop. The results of the training showed a significant increase. Before the training, 60% of participants did not understand digital marketing, but this figure increased to 85% after the training. In addition, 90% of participants felt that the training was relevant to their business needs, and 60% of them recorded an increase in sales of up to 30% after implementing the strategies taught. This activity provides direct benefits by improving participants' skills in digital marketing and building their confidence to use technology independently. This program shows the success of a practice-based approach and can be a model for MSME empowerment that can be applied in other areas

In today’s era, social media has become a driving force for increasing digital entrepreneurship. Businesses are utilizing social media sites such as Instagram, TikTok, LinkedIn, or even Facebook to brand their companies or products and interact with clients. This is great news for businesses, especially SMEs, to have low-cost access to key markets worldwide. One evident trend is the emergence of social commerce – business-to-consumer commerce without intermediaries, exclusive of other e-commerce models. However, the adoption of social media in digital entrepreneurship comes with several challenges, such as changes in algorithms that can affect content visibility and risks related to data security and user privacy. Nevertheless, social media remains useful in terms of analytics to support strategic decisions. This study shows the value of social media for entrepreneurship and technologies that help improve content personalization and consumer behavior analysis, such as artificial intelligence and big data. This study attempts to fill the gap in the literature by looking at the differences in the outcomes of social media use in developing and developed countries and the outcomes of new technologies on digital business ventures.

This qualitative literature review examines the impact of FinTech innovations on small business financing, focusing on credit lines and customer satisfaction. The review explores how FinTech platforms have transformed traditional lending by enhancing accessibility, efficiency, and flexibility for small businesses, particularly those underserved by traditional banks. Key findings show that FinTech innovations, such as the use of alternative data for credit assessment and automated risk management, have significantly improved customer experiences by offering faster, more tailored financial solutions. However, challenges remain, including regulatory concerns, the risk of algorithmic bias, the lack of personalized human interaction, and the potential for over-leveraging among small businesses. Despite these limitations, FinTech continues to evolve, offering promising avenues for improving financial inclusion and borrower experiences. The review concludes that while FinTech has positively influenced the small business financing landscape, further research is needed to address regulatory gaps and ensure the sustainable growth of digital lending systems.

This research explores increasing the efficiency of network resource use through load balancing techniques. Load balancing distributes workload evenly among servers to optimize resource usage, maximize throughput, and minimize response time. We compare various load balancing algorithms, such as round-robin, least connections, and least response time, in various network scenarios. Experimental results show that proper load balancing techniques can reduce response time by up to 30% and increase resource usage efficiency by up to 40%. This study highlights the importance of selecting appropriate algorithms based on network traffic and workload characteristics. Implementing effective load balancing strategies can improve the quality of network services and ensure even distribution of workloads, providing practical guidance for network administrators to optimize network performance and efficiency.

Computational thinking is the ability to solve problems and design systems using concepts and techniques generally associated with computers and computer programming. The aim of this research is to conduct a literature review, namely to determine the application of computational thinking in mathematics learning. The technique used is the Systematic Literature Review (SLR) strategy. SLR is a research method that aims to identify, study and interpret data in journals systematically with specified stages. The conclusion obtained from the research results is that the application of computational thinking in mathematics learning can be done using four foundations of computational thinking, namely: (1) Decomposition, namely the problem is divided into small parts, (2) Pattern recognition, namely the process of identifying patterns or sequences of problems, (3) Abstraction, namely consideration of the important parts of a problem, and (4) Algorithm, namely a series of instructions for solving the problem. Examples of its application include the use of interactive mathematics software, mathematical simulations, problem-based learning with technology, adaptive learning and the use of educational games.  

The rapid advancement of quantum computing has significantly impacted data security, as classical cryptographic algorithms such as RSA and ECC are increasingly vulnerable to quantum attacks. This study aims to evaluate the performance of classical and post-quantum cryptographic algorithms in a quantum simulation environment, focusing on stability, efficiency, and computational time. The research method employed experimental simulations using Qiskit, where cryptographic algorithms were modeled into quantum circuits and tested across varying qubit sizes of 128, 256, 512, and 1024. The simulation results indicate that classical algorithms face substantial limitations, with exponentially increasing computational time and drastically reduced stability beyond 512 qubits. In contrast, post-quantum algorithms demonstrated superior performance, maintaining high stability up to 1024 qubits, achieving greater quantum efficiency, and showing resilience against quantum attacks such as Shor’s and Grover’s algorithms. These findings highlight the urgent need to transition toward post-quantum cryptography as a more adaptive and reliable approach to safeguarding data in the quantum era. Although post-quantum algorithms still face certain challenges, such as larger key sizes and slightly higher computational costs at smaller scales, their overall benefits are far more significant in ensuring sustainable information security. Therefore, adopting post-quantum cryptography represents a strategic step that must be prioritized to address the evolving risks posed by quantum computing technologies.

This study aims to review the use of generative artificial intelligence (AI) in strategic decision evaluation, with a focus on consistency and bias in business decision making. Through a qualitative literature review approach, this study analyzes various studies that examine how AI technology, such as the GPT model, can improve decision quality by providing more objective and consistent data analysis. Although it has great potential in reducing human bias, this study also shows the risk of algorithmic and data bias that can affect decision outcomes. Therefore, the use of AI in decision making must be accompanied by strict human supervision to ensure the quality and fairness of the resulting decisions. The results of this study provide an important contribution to the understanding of the challenges and opportunities of AI in strategic decision making in the business world.

Abstract.  The purpose of this research is to identify the difficulties experienced by students in understanding the concepts, principles, and procedures in problems related to the material of lines and angles. This research is a qualitative study with a descriptive method. The subjects of this study were students of class VIII-1 at SMP Negeri 29 Medan. Data collection techniques included tests, interviews, and documentation. All students of class VIII-1 took a test on the understanding of concepts, principles, and procedures. Afterward, the test results were analyzed and categorized into three groups: high, medium, and low ability. Subsequently, two students from each ability group were selected as interview subjects. The results of this research indicate that the most common conceptual difficulty experienced by students is classifying objects according to their concepts in determining the type of angle according to its measurement. The most common principle difficulty experienced by students is connecting mathematical concepts in solving problems in the material of the relationship between angles formed by two parallel lines intersected by a transversal line. Meanwhile, the procedural difficulty experienced by students is that all students still make mistakes in explaining, planning, and applying the correct steps or algorithms in mathematical problems, both in the material of the relationship between lines, determining angles, and the relationship between angles formed by two parallel lines intersected by a transversal.  

With the rapid development of information technology, Tiktokshop has become an increasingly popular e-commerce platform in Indonesia. However, selling on Tiktokshop does not always guarantee success, especially for small and medium enterprises (MSMEs) that focus on selling special products such as CustomKids. This research aims to determine the impact of Tiktokshop's inactivity on sales of MSMEs, especially the custom children's industry in Indonesia. This research analyzes various factors that might influence Tiktokshop's inactivity, such as lack of presence, lack of user interaction and algorithm changes. We also assessed the direct impact of the suspension on sales of CustomKids products, taking into account variables such as decreased sales, decreased market share, and the psychological impact on MSME owners. The results of this research will provide insight to MSME owners, the government and stakeholders to help them develop strategies to advance Tiktokshop activities and increase the competitiveness of MSMEs, with a special focus on CustomKids products. It is hoped that the implications of this research finding will help in decision making to improve the performance of MSMEs in the digital era.