Analysis of High-Performance Parallel Computing using TOPSIS Method

So far, many ways have been provided to solve multiple criteria decision making problems with interval numbers. Most of these methods rank the alternatives according to two criteria, that is, being close to the positive ideal solution and far away from the negative ideal solution. In this paper, a method is presented for solving multiple criteria decision making problems with interval numbers, such that being close to positive ideal solution and being away from negative ideal solution have the same effect in alternatives ranking. In the proposed method, the first positive ideal solution and negative ideal solution are determined as interval numbers and distance of each alternative from positive ideal solution and negative ideal solution is calculated by extension of Euclidean distance. Then, a compromise index is defined to rank the alternatives. Three numerical examples are given to compare the proposed method with other methods presented in the literature.

This study aims to create a decision support system for home renovation aid recipients that can speed up and simplify the decision-making process in providing home surgery assistance so that home surgery assistance is more objective in accordance with community priorities or on target using the TOPSIS method. The TOPSIS method uses the principle that the chosen alternative must have the closest distance from the positive ideal solution and the longest (farthest) distance from the negative ideal solution from a geometric point of view by using the Euclidean distance (distance between two points) to determine the relative proximity of an alternative to the optimal solution. . The criteria used in the system are land ownership, roof conditions, wall conditions and floor conditions. The data used by the author is 210 original data from the Kotapulu village office. so that the system created by the author can make it easier for the Kotapulu village office to determine the recipient of the house renovation assistance only by inputting population data based on the survey results.

New design concept evaluation method involving customer preferences based on rough distance to redefined ideal solution

Natural Language Processing (NLP) is an essential element of computational linguistics and artificial ‎intelligence, enabling fluid interactions between humans and machines. Social networking networks ‎produce substantial volumes of user-generated text daily, offering both opportunities and challenges for ‎NLP researchers. Social media discourse's informal, dynamic, and context-dependent characteristics ‎necessitate specific NLP techniques for precise processing and analysis. This study thoroughly examines NLP applications in social media, including essential tasks such as sentiment analysis, topic ‎modeling, misinformation detection, and hate speech identification. It examines the influence of machine ‎learning and deep learning methodologies, particularly transformer models, on the advancement of NLP ‎capabilities. This study also emphasizes the ethical issues related to NLP-driven social media apps, ‎including data privacy, algorithmic bias, and the regulation of misinformation. The paper continues by ‎discussing emerging research paths, highlighting the necessity for adaptable and ethical NLP solutions in ‎the changing social media environment‎.

Scholarship is a government program given to high school graduates to be able to continue their studies at the tertiary level. This is as stipulated in the Regent Regulation (PERBUP) No 25 of 2019 concerning "providing scholarships and educational assistance to students of Merauke Regency". In the scholarship acceptance selection activities in 2019, approximately 103 applicants were recorded, and 74 were successfully accepted. The process of selecting prospective scholarship recipients is currently being carried out, namely checking and comparing one by one with the applicant's data. Based on data in 2019, where there are applicants whose report card value data is incomplete but is accepted for a scholarship. This is considered to have an impact on the decision-making results of prospective scholarship recipients in subsequent years. In order to assist KESRA in selecting scholarship recipients, these obstacles can be overcome by using a decision support system. The design of this decision support system is carried out using the TOPSIS method, namely selecting the alternative that is the shortest distance from the positive ideal solution and the furthest distance from the negative ideal solution. The results obtained in this study are a system that can assist the People's Welfare in determining the selection of scholarship recipients based on the three predetermined criteria. Testing the accuracy of the system obtained is 67% and for testing the questionnaire to obtain a percentage of 66.67% which is in the "Agree" category.

Employee performance appraisal is very important, especially for employees in the Bengkulu PDAM environment to encourage employee motivation and professionalism in improving work quality. Constraints faced in conducting performance appraisals are due to the diversity of educational backgrounds, experience, competencies and employee portfolios, so we need a system that can assist in the performance appraisal process, so that Employee Performance Assessment will not take time and get into trouble. In the process of designing a decision support system application for evaluating employee performance using the TOPSIS approach. The TOPSIS method (Technique For Order Performance By Similarity To Ideal Solution) is a multi-criteria decision-making method or alternative choice which is the alternative that has the smallest distance from the positive ideal solution and the largest distance from the negative ideal solution. The implementation of the system uses the Visual Studio 2010 programming language and the method used in this study is the waterfall method. From the results of the tests carried out, it can be concluded that this decision support system can provide the best employee decisions which can later be used as a reference to determine the quality of employee performance.

The increasing demand for cross-lingual communication in a globally interconnected world has spurred significant advancements in Natural Language Processing (NLP), a branch of artificial intelligence aimed at enabling computers to comprehend, generate, and process human language. Over the past few years, NLP has transitioned from traditional rule-based and statistical approaches to deep learning-based methods, with pre-trained models like BERT and GPT demonstrating remarkable performance across a variety of tasks, including text classification and machine translation. However, the linguistic diversity of the world's languages presents ongoing challenges, as many NLP models are primarily developed for high-resource languages like English, leaving other languages underserved. This paper examines the linguistic features of three representative languagesChinese, Japanese, and Englishand explores NLP methodologies tailored to their distinct grammatical, lexical, and structural properties. Through comparative linguistic analysis, this study discusses the challenges of multilingual NLP and the potential for cross-lingual applications. The findings underscore the importance of adapting NLP models to different language characteristics to enhance the effectiveness and inclusivity of cross-lingual processing.

Research on lifestyle changes during the COVID-19 pandemic often relies on Likert-type scale question surveys. Survey participants respond to questions by selecting one of the numerically ordered choices, "Strongly Disagree"=1, "Disagree"=2, "Neutral"=3, "Agree"=4, and "Strongly Agree"=5. Analyzing Likert-type data requires statistical methods beyond approaches like linear regression. First, it is unclear if the distance between choices are truly equal. For example, are "Agree" and "Strongly Agree" more close than "Neutral" and "Agree? Second, summarizing results using traditional means makes little sense. For example, would a mean of 4.5 imply "Agree and a half"? Finally, participants tend to select more central choices and less extremes.

Tobler's First Law of Geography (TFL) is one of the key reasons why is special. The law, which states that is related to everything else, but near things are more related than distant things, is central to the management, presentation, and analysis of geographic information. However, despite the importance of TFL, we have a limited general understanding of its domain-neutral properties. In this paper, we leverage recent advances in the natural language processing domain of semantic relatedness estimation to, for the first time, robustly evaluate the extent to which relatedness between spatial entities decreases over distance in a domain-neutral fashion. Our results reveal that, in general, TFL can indeed be considered a globally recognized domain-neutral property of geographic information but that there is a distance beyond which being nearer, on average, no longer means being more related.

In this chapter, an alternative measure to Euclidean distance measurement is proposed which is used to calculate positive and negative ideal solutions in the traditional TOPSIS method. Lp Minkowski family and L1 family distance measures were used for this purpose. By taking the averages of the distance measurements in the Lq and L1 families, more general and accurate level units were tried to be obtained. Thus, it was shown that TOPSIS method can give different results according to the distance measure used. The importance of the distance measurement unit was emphasized to rank the alternatives correctly. The implementation and evaluation of the proposed method was carried out through the financial performance of the deposit bank operating in the Turkish Banking Sector. It was seen that the rankings of the alternatives changed according to the distance measurements used. By referring to the distance measurements that can be used in the TOPSIS method, it was shown that the rank of the alternatives can vary according to the preferred distance measure.

Efforts to increase the value of the quality of education for students as the next generation of the nation require teachers who have competence in providing education to students. The competence of a teacher can be assessed when a teacher has carried out his duties in accordance with the standard provisions he has. The ranking of potential teachers is used to motivate teachers to be active in carrying out each activity and is expected to have a positive effect on their work to face challenges in this era of globalization. The data that is processed for the ranking of teacher potential is the assessment data of elementary school teachers sourced from the korwildik of the Batang kapas sub-district. This ranking is based on several criteria and weights are determined. Furthermore, this ranking is processed using a system created by the researcher. This ranking uses a method called TOPSIS to assist researchers in ranking. The TOPSIS method is a multi-criteria decision support method with the principle that the chosen alternative must have the closest distance from the positive and negative ideal solutions. The results of ranking with the TOPSIS method are able to support the ranking decisions of potential teachers using predetermined criteria. So that the highest value is found in the 5th alternative with a value of 0.7321 and the lowest value is found in the 1st alternative with a value of 0.2218. The ranking of potential teachers has proven to be able to help the South Coast District Education and Culture Office, especially the korwildik of the Batang kapas sub-district in determining prospective school principal

The inspection of documents written in natural language with computers has become feasible thanks to the advances in Natural Language Processing (NLP) techniques. However, certain applications require a deeper semantic analysis of the text to produce good results. In this article, we present an exploratory study of semantic-aware NLP techniques for discovering latent concerns in use case specifications. For this purpose, we propose two NLP techniques, namely: semantic clustering and semantically-enriched rules. After evaluating these two techniques and comparing them with a technique developed by other researchers, results have showed that semantic NLP techniques hold great potential for detecting candidate concerns. Particularly, if these techniques are properly configured, they can help to reduce the efforts of requirement analysts and promote better quality in software development.

Natural Language Processing (NLP) has emerged as a transformative technology within the domain of artificial intelligence, significantly advancing the ability of machines to comprehend and interact with human language. Recent advancements in machine learning algorithms have led to substantial improvements in a variety of NLP applications, including sentiment analysis, machine translation, automated summarization, and question answering. This study presents a novel approach to NLP, which integrates advanced machine learning techniques to enhance performance in these areas. The proposed method achieves a high level of accuracy, with an overall accuracy rate of 97.6%. Additionally, it demonstrates strong performance in error metrics, including a Mean Absolute Error (MAE) of 0.403 and a Root Mean Square Error (RMSE) of 0.203. These results indicate a significant advancement in the precision and effectiveness of language-based tasks. Despite these achievements, challenges such as the management of ambiguous language, the need for extensive annotated datasets, and the demand for real-time processing capabilities remain. This paper aims to explore the current state of NLP driven by machine learning, examining key techniques and methodologies that have propelled its development. The analysis will cover various machine learning approaches applied to NLP, evaluate their impact on different applications, and identify ongoing challenges and future research directions. By providing a comprehensive overview of the interplay between NLP and machine learning, this research seeks to enhance understanding of how these technologies can be leveraged to improve language processing and communication

Natural Language Processing (NLP) has witnessed remarkable advancements in recent years, revolutionizing various fields including voice assistants and chatbots. This paper explores the latest developments in NLP technologies and their applications in enhancing the functionality and intelligence of voice assistants and chatbots. Beginning with an overview of NLP and its core components, such as tokenization, syntactic analysis, semantic understanding, and machine learning techniques, this paper delves into the cutting-edge techniques driving the evolution of NLP. Furthermore, it discusses the challenges faced by NLP systems and the strategies employed to address them. Subsequently, the paper explores the utilization of NLP in voice assistants and chatbots, elucidating how these technologies are reshaping human-computer interaction, improving user experiences, and expanding the scope of applications in various domains. Additionally, it examines the impact of NLP advancements on the future trajectory of voice assistants and chatbots, highlighting potential opportunities and implications for research and industry

In the ever-changing realm of E-Commerce, it is essential for online businesses to comprehend and adjust to shifting consumer behaviour in order to achieve long-term success. In which, Intelligent Recommendation System (IRS) has gained familiarity by suggesting personalized information based on user preference and behaviours. Hence, the review paper primarily aims to analyse significance of the intelligent recommendation system to transform ecommerce field, specifically enrich the user personalisation and satisfaction, and enhance revenue in business. Accordingly, the proposed survey is discussed the traditional system and AI-powered personalization system in ecommerce. AI-powered recommendation system utilize sophisticated algorithms to analyse extensive data, allowing for the provision of highly customized and relevant content, product recommendation, and user satisfaction. Besides, it examines future trends in AI integration within e-commerce, particularly advancements in Natural Language Processing (NLP) and visual search technologies, which are poised to further enrich ecommerce. The paper concludes with a look toward future directions for the integration of AI technologies in e-commerce, anticipating advancements in NLP and visual search capabilities, which promise to further enhance the online shopping experience. Overall, the findings of the article underscores the transformative impact of IRS on the e-commerce sector, advocating for their continued development in response to evolving market demands.