Form Of Text Research Articles

Sentiment Analysis on the Impact of Artificial Intelligence (AI) Development to Determine Technology Needs

Artificial Intelligence (AI) has become a hot topic in recent years in Indonesia. To determine the influence of AI developments in determining technology needs, a sentiment analysis needs to be carried out. Sentiment analysis is a process used to help identify the contents of a dataset in the form of opinions or views (sentiments) in text form regarding an issue or event that is positive, negative or neutral. The algorithm applied in this research is the Multinominal Naive Bayes Classifier method. The Multinominal Naive Bayes Classifier method was chosen because it has quite high processing speed and accuracy when used on large, varied and large amounts of data. In this research, the sentiment results were "Negative" for the topic of data security and privacy with a testing accuracy of 75%, "Positive" for Economic Topics with a testing accuracy of 50%, "Negative" for Industrial Topics with a testing accuracy of 58%, "Positive" for Field Topics jobs with a testing accuracy of 75%, “Negative” Transportation Topics with a testing accuracy of 50%, and “Negative” for Education Topics with a testing accuracy of 67%.

Struktur dan Fungsi Mantra Pengobatan Masyakarat Kanagarian Malai V Suku Kecamatan Batang Gasan Kabupaten Padang Pariaman

The purpose of this study was to investigate the structure and function of the treatment mantra of the Nagari Malai V Suku community, Batang Gasan District, Padang Pariaman Regency. This research is categorized as qualitative using descriptive method. The research uses data in the form of texts of community treatment mantras in Nagari Malai V Suku, Batang Gasan District, Padang Pariaman Regency. The instrument of this research is the researcher himself. Data were collected through three stages, namely: (1) literature review, (2) observation and (3) interviews with informants. Data validation was carried out using triangulation techniques. After the data were collected, the data were analyzed with five stages: transcription, transiteration, classification or data analysis, drawing conclusions, and reporting. The results of the research on the structure of the treatment mantra found three parts, namely: opening, content, and closing. The functions of the treatment mantra are divided into five, namely: (a) a system for projection, (b) institutions in cultural institutions, (c) a means to educate, (d) a tool to enforce norms, and (e) a means of treatment. There are three structures in the research, namely the opening, content, and closing structures.

Analysis of Multimodal Metaphor and Values Representation in Children’s Picture Books

Children’s picture books, as a form of text interwoven with vision and language, carry rich cultural information and educational functions. The purpose of this article is to explore the phenomenon of multimodal metaphor in children’s picture books and reveal its influence on ideological construction. Firstly, the article compiles the research background and puts forward the research value of multimodal metaphors in children’s picture books. Then, it constructs an analytical framework for children’s picture books that integrates visual semiotics and metaphor theory, and analyzes the specific use of multimodal metaphors in children’s picture books through example analyses, including the interaction between images and words, the symbolism of colors and shapes, and the construction of metaphors in the narrative structure. These case studies reveal how children’s understanding of the world is shaped by multimodal metaphorical strategies in picture books, which in turn subconsciously convey specific values and ideologies. Future research should explore more deeply the differences of multimodal metaphors in different cultural contexts and their application strategies in children’s education, with a view to cultivating children’s critical thinking and promoting diversified development of children’s picture books.

Transparency Analysis Of Financial Report Preparation And Village Fund Management In Padang Mumpo Village, Pino District, South Bengkulu Regency

Research objectives To determine and analyze the transparency of village financial management in Padang Mumpo Village, Pino District, South Bengkulu Regency. The method used is a qualitative method. Data collection techniques are observation, documentation and interview techniques, then adjusted to the indicators of the Minister of Home Affairs Regulation Number 20 of 2018 concerning Village Financial Management and outlined in text form. The results of this study Padang Mumpo Village always provides information to the public and always reports accountability, and there is auditing by the inspectorate, therefore it can be concluded that the Transparency of Financial Reports and the management of Village Funds in Padang Mumpo Village based on indicators, namely the Regulation of the Minister of Home Affairs Number 20 of 2018 concerning Village Financial Management is in accordance.

Text Mining to Understand Disease-Causing Gene Variants

Variations in the genetic code for proteins are considered to confer traits and underlying disease. Identifying the functional consequences of these genetic variants is a challenging endeavor. There are online databases that contain variant information. Many publications also have described variants in detail. Furthermore, there are tools that allow for the prediction of the pathogenicity of variants. However, navigating these disparate sources is time-consuming and sometimes complex. Finally, text mining and large language models offer promising approaches to understanding the textual form of this knowledge. This review discusses these challenges and the online resources and tools available to facilitate this process. Furthermore, a computational framework is suggested to accelerate and facilitate the process of identifying the phenotype caused by a particular genetic variant. This framework demonstrates a way to gather and understand the knowledge about variants more efficiently and effectively.

Construction of Expert Image in Genre of Editorial: Discursive Practices

This research focuses on discursive practices employed to construct the expert image within the genre of editorial articles. The expert image aligns with the goals of this textual form. Initially, editorials served as a tool for expressing the publication’s voice and ensuring the status of an independent social institution. While this function has somewhat diminished today, editorials still play a significant role in shaping societal views and values, especially during pivotal political events. Therefore, it is crucial for authors of editorial articles to maintain authority and expert status. Research findings demonstrate that discursive practices such as referencing authoritative sources, highlighting the author’s achievements in discussing the issue at hand, presenting quantitative data, formulating predictions, and recommendations are utilized to shape the expert image in editorial articles. These practices are perceived by readers as elements of expert knowledge, yet they actually belong to distinct realms: expertise and counter-expertise. The author of the article differentiates between expertise, anti-expertise, and counterexpertise.

Educational Technology in Early Childhood Education: A Systematic Literature Review

The development of science and technology in the 21st century is currently digital-based. In line with technological progress and innovation, it also impacts the world of education, such as changes in pedagogy and approaches to facilitating and delivering learning content. The advantages and ease of use of technology in education also have great potential to modernize the methodology of traditional teaching and learning systems. Early Childhood Education is vital in becoming the foundation for children's growth and development towards a successful and highly competitive future so that they continue to develop. Educational technology is an inseparable component in supporting the learning process of early childhood. This research uses Systematic Literature Review (SLR) analysis. This research aims to thoroughly explore how technology is used in Early Childhood Education and what aspects of development occur in children who use EdTech in Early Childhood Education. The data source in this research is the Scopus database of 174 documents published in 2018-2022, screened by applying inclusion and exclusion criteria in three different rounds, 13 articles were analyzed in complete text form. This research shows that technology is used in early childhood education to improve cognitive aspects, language, arts, students' interest in learning, and preschool preparation for children. Types of technology that can be applied include PowerPoint, video, digital storytelling, drawing, digital stories, and so on. This research also explains that the use of technology must be adjusted to the age, education level and targets of students. Improper use of technology can cause problems such as children losing their ability to mingle with society and tend to be comfortable with online life. Aspects of development in children that make good use of technology are cognitive aspects, language, art, and increasing knowledge in children, as well as motivation to learn.

A Content Analysis of English Textbook Pathway to English Phase E Used in First Grade Senior High School

The objective of the study was to identify the content analysis according to Cunningsworth’s theory in the English textbook entitled “Pathway To English Phase E” used in first grade senior high school by Th.M. Sudarwati and Eudia Grace. This study was a descriptive qualitative research with a documentation technique as a data collection method. This study also used triangulation namely triangulation of data. This study used content analysis according to Cunningsworth’s theory to analyzed the data. According to the criteria for selecting a good textbook which are: 1) aims and approaches, 2) language and content, 3) skill, 4) topic, and 5) methodology. The research was conducted from Desember 2023 to January 2024. This study was conducted at the library. To analyze the data, the researchers used qualitative analysis data. The results of this study will be the criteria for a good textbook according to cunningsworth 5 out of 8 criteria are present. Among them are aims and approaches, language and content, skill, topic and methodology. The research show aims and approaches get 93,75%, language and content get 70,83%, skill get 95,83%, topic get 87,5 and methodology get 100%. The average points is 89,5%. Additionally, the study shows that data is transmitted in the form of texts, instructions and conversations.

Classroom Teachers Ability to Create Digital-based Learning Media for Students Reading Interest in Elementary Schools in Cilengkrang District, Bandung Regency

Literacy skills are a manifestation of an individual’s ability or quality of being literate, encompassing the ability to read and write, both physically, tactically, and technically, within a unit to carry out a mission or task. In this case, the mission is to enhance the reading interest of elementary school students in the Cilengkrang District, Bandung Regency. In line with Cope and Kalantzis (2000), multiliteracy pedagogy has evolved to encompass multilingual and multimodal dimensions of literacy[1]. This has implications for schools and communities that need to develop practices and skills in using various ways to express and comprehend ideas and information through conventional text forms as well as innovative text forms, symbols, and multimedia [2]. Literacy skills can be enhanced by adapting current learning media to be digitally based to attract the literacy interest of students. Through engaging learning media, the literacy movement can be successful. The method used is digitizing conventional learning media and introducing innovative new learning media to enhance reading interest in students. Based on these considerations, training on creating digital-based learning media is necessary as an innovative effort in preparing effective learning media with the goal of improving the literacy skills of elementary school students. The training, followed by mentoring, will be conducted over a three-month period, from February to April 2023, involving 36 teacher participants. The outcomes of this training and mentoring, students interest in elementary schools in Cilengkrang District has increased because learning media is no longer conventional and digital based. This increase was achieved after teachers attended training and were able to create and apply digital-based learning media in the classroom. Keywords: literacy skills, learning media, elementary school

Evaluating accuracy and reproducibility of ChatGPT responses to patient-based questions in Ophthalmology: An observational study.

Chat Generative Pre-Trained Transformer (ChatGPT) is an online large language model that appears to be a popular source of health information, as it can provide patients with answers in the form of human-like text, although the accuracy and safety of its responses are not evident. This study aims to evaluate the accuracy and reproducibility of ChatGPT responses to patients-based questions in ophthalmology. We collected 150 questions from the "Ask an ophthalmologist" page of the American Academy of Ophthalmology, which were reviewed and refined by two ophthalmologists for their eligibility. Each question was inputted into ChatGPT twice using the "new chat" option. The grading scale included the following: (1) comprehensive, (2) correct but inadequate, (3) some correct and some incorrect, and (4) completely incorrect. Totally, 117 questions were inputted into ChatGPT, which provided "comprehensive" responses to 70/117 (59.8%) of questions. Concerning reproducibility, it was defined as no difference in grading categories (1 and 2 vs 3 and 4) between the 2 responses for each question. ChatGPT provided reproducible responses to 91.5% of questions. This study shows moderate accuracy and reproducibility of ChatGPT responses to patients' questions in ophthalmology. ChatGPT may be-after more modifications-a supplementary health information source, which should be used as an adjunct, but not a substitute, to medical advice. The reliability of ChatGPT should undergo more investigations.

COMPARISON OF SUPPORT VECTOR MACHINE BASED ON FASTTEXT WITHOUT AND WITH FIREFLY OPTIMIZATION PARAMETERS FOR DISASTER SENTIMENT ANALYSIS IN INDONESIA

Sentiment analysis is a process for analyzing opinions, sentiments, assessments, and emotions from someone's statements regarding a domain or is also a process for entering and processing data in the form of text. Support vector machine (SVM) is a supervised machine learning technique that functions as a separator of two classes of data. SVM aims to obtain numerical vectors using fasttext. SVM cannot choose appropriate parameters so the use of parameters is not optimal. To obtain optimal parameters with better classification results, firefly optimization was carried out. This research compares the fasttext-based SVM method without and with firefly optimization parameters using data from tweets with the keyword "Indonesian disaster" which was crawled using the Twitter application. The results of this research obtained 128 dimensions that form the weight of each word. This means that each word is represented in a 128-dimensional vector space. The evaluation of the SVM classification model with and without firefly optimization provides an accuracy of 89.1% and 61.3% respectively. This shows that the SVM classification method with firefly optimization provides quite good classification performance compared to the SVM model without optimization.

Comparison of the Use of Indonesian, Foreign Languages and Regional Languages in Naming Places in the Mataram Region

This research was motivated by the emergence of various names of places in the Mataram city area with names in foreign languages. On the one hand, the use of foreign languages aims to make it easier for foreign immigrants to understand places in Indonesia, but on the other hand, the use of foreign languages in the public sphere, in the form of advertising and promotion of goods and services, is under the pull of globalization/internationalization interests. Prestige or a sense of pride in using a foreign language is an interesting aspect to study further. This research aims to record the use of language in naming tourist and culinary attractions in the city of Mataram and see the dominance of language. Data was collected using the documentation method, then the data was analyzed using the content analysis method which includes data reduction, data presentation and drawing conclusions. Data is presented in the form of tables, diagrams and narrative text. Based on the results of the analysis, two linguistic features were found in naming places, namely monolingual and bilingual. The monolingual feature is divided into 57 foreign languages, 47 Indonesian languages, and 9 regional languages. Meanwhile, the form of the bilingual feature is divided into two, namely a combination of foreign languages and regional languages with 13 data and a combination of foreign language and Indonesian with 3 data.

Detecting and Grounding Multi-Modal Media Manipulation and Beyond.

Misinformation has become a pressing issue. Fake media, in both visual and textual forms, is widespread on the web. While various DeepFake detection and text fake news detection methods have been proposed, they are only designed for single-modality forgery based on binary classification, let alone analyzing and reasoning subtle forgery traces across different modalities. In this paper, we highlight a new research problem for multi-modal fake media, namely Detecting and Grounding Multi-Modal Media Manipulation (DGM 4). DGM 4 aims to not only detect the authenticity of multi-modal media, but also ground the manipulated content (i.e., image bounding boxes and text tokens), which requires deeper reasoning of multi-modal media manipulation. To support a large-scale investigation, we construct the first DGM 4 dataset, where image-text pairs are manipulated by various approaches, with rich annotation of diverse manipulations. Moreover, we propose a novel HierArchical Multi-modal Manipulation rEasoning tRansformer (HAMMER) to fully capture the fine-grained interaction between different modalities. HAMMER performs: 1) manipulation-aware contrastive learning between two uni-modal encoders as shallow manipulation reasoning and 2) modality-aware cross-attention by multi-modal aggregator as deep manipulation reasoning. Dedicated manipulation detection and grounding heads are integrated from shallow to deep levels based on the interacted multi-modal information. To exploit more fine-grained contrastive learning for cross-modal semantic alignment, we further integrate Manipulation-Aware Contrastive Loss with Local View and construct a more advanced model HAMMER++. Finally, we build an extensive benchmark and set up rigorous evaluation metrics for this new research problem. Comprehensive experiments demonstrate the superiority of HAMMER and HAMMER++; several valuable observations are also revealed to facilitate future research in multi-modal media manipulation.

Research on named entity recognition method for high-speed railway technology transformation project text

Abstract High-speed railway technology transformation projects are crucial to the layout of high-speed rail transportation production, high-speed rail safety, and high-speed rail operation and management. However, the data of high-speed railway technology transformation projects is mainly stored in the form of unstructured text, and the relevant personnel of high-speed railroad often need to manually go through the form to extract the key information of the project data and enter it into the system, with a low degree of automation and intelligence. Therefore, to improve the efficiency and quality of processing the data of high-speed railway technology transformation projects, and help the intelligent development of high-speed railways, this paper constructs the RoBERTa-BiLSTM-CRF fusion model to automate the identification of key information entities in the data of high-speed railway technology transformation project. The experimental data were selected as technology transformation project documents of a railway bureau from 2013 to 2022, and six defined entity types were identified. Experimental results show that the constructed model can effectively identify six types of defined entities, and the F1 value of each type of entity is above 90%. In addition, compared with other commonly used models, the F1 value has been significantly improved. The fusion model proposed in this paper can be used as a method to automatically extract key information for high-speed rail technology transformation projects.

DataPrice: An Interactive System for Pricing Datasets in Data Marketplaces

With the flourishing of data-driven applications, data marketplaces, which can dramatically facilitate data utilization, have emerged recently. However, determining the appropriate price for datasets presents a significant challenge due to the intangible nature of data. DataPrice alleviates the challenge by providing an interactive system based on a pricing model trained on real datasets collected from commercial data marketplaces. The pricing model can estimate an appropriate price according to the dataset description as a reference for both data sellers and data buyers. By leveraging Shapley values to evaluate the contribution of each attribute in metadata on the estimated price, DataPrice offers an explanation of the pricing process in a text form to enhance user trust.

Research on the Content Validity of the English Reading Test in Harbin Senior High School Entrance Examination

The Academic Test for the Junior High School Students, as a kind of academic level examination, plays a crucial role in the selection and streaming of talents at the secondary school level in China. Reading is an essential skill that language learners need to master, and it has a large proportion in the English test of the High School Entrance Examination (HSEE). Therefore, studying the validity of English Reading Test in the HSEE can not only provide the examiner of the HSEE with data references for judging the validity of the reading test, but also provide references for English teachers to innovate teaching methods, as well as cultivating the students' English reading strategies and achieving the overall improvement of the reading ability. Focusing on English Reading Test in Harbin HSEE (2019-2023), this research selects the framework of test characteristics proposed by Bachman and Palmer and analyzes discourse input and expected response to explore the content validity by using textual, quantitative and comparative analyses. The results show that English Reading Test in Harbin HSEE (2019-2023) has a certain degree of content validity and basically meet the requirements of the exam syllabus, but problems still exist. The presentation form of the test text is monotonous, the difficulty gradient is unreasonable and the assessment of reading skills is insufficient.

Chatbots and Their Applications in Medical Fields: Current Status and Future Trends: A Scoping Review

Background and Aim: Chatbots are computer programs, which are devised to simulate conversations through voice or textual interactive forms. These applications offer multiple benefits in patient education, clinical decision-making, interpersonal communication, research activities, data analysis and administrative affairs. The present scoping review aims to analyse the current role, pitfalls, challenges and future scope of these modalities in diverse fields of medical science. Methods: Literature search was made on 9 April 2024, on five databases (PubMed, Scopus, Web of Science, Embase and Google Scholar). A narrative approach was used for the synthesis of results. Results: Our literature search yielded 1024 studies. After the de-duplication of manuscripts using Endnote, 342 articles were identified. After title and abstract screening, 74 articles were included for the next round of screening. Finally, 14 articles were selected for this scoping review.Diverse chatbot applications have been developed at a growing rate for use in medical science. There is a gradual shift towards employing machine learning-based strategies to develop chatbot programs. Chatbots have a significant potential to revolutionise diverse aspects of medicine including patient care, academic activities, and research endeavours. Conclusion: Artificial Intelligence can be highly effective in streamlining the routine functions and activities in medical care. Chatbots have the potential to meliorate the accessibility, efficiency and standard of medical care. However, the need for further validation through high-quality studies to ensure patient privacy, strategies to conform to patients’ data security, accuracy and precision of the technology and mitigating potential pitfalls in its routine utilisation globally are warranted.

Wadiah Savings Fund Strategy and Bank Products at Bank Syariah Indonesia

This study aims to find out about the development of Islamic banks in Indonesia. This research is qualitative descriptive. This study included literature to examine the written sources such as scientific journals, books referesni, literature, encyclopedias, scientific articles, scientific papers and other sources that are relevant and related to the object being studied. As for the object of study of this research is in the form of texts or writings that describe and explain about the development of Islamic banks in Indonesia. Results from this study is Indonesian banking now enlivened by the presence of Islamic banks, which offers financial products and investment in a different way than conventional banks, as Indonesia is the largest Muslim country in the world. The development of Islamic banking in Indonesia has become a benchmark for the success of the Islamic economic existence. Bank Muamalat as the first Islamic bank and a pioneer for other Islamic banks in Indonesia.

Medication adherence to sodium-glucose cotransporter-2 inhibitors versus glucagon-like peptide-1 receptor agonists: A meta-analysis.

To perform a meta-analysis comparing real-world medication adherence to sodium-glucose cotransporter-2 inhibitors (SGLT2is) versus glucagon-like peptide-1 receptor agonists (GLP-1RAs). A systematic search of Medline and Embase was conducted through October 2023. To meet inclusion criteria, articles had to be published in full text form and directly compare medication adherence to SGLT2is versus GLP-1RAs in adults. Only studies evaluating real-world data and utilizing the proportion of days covered (PDC) to measure adherence were included. Non-adherence, defined as the proportion of patients with a PDC <80%, was the primary outcome. A subgroup analysis evaluating results among studies conducted in the United States was performed. We identified eight studies evaluating 205 103 patients for inclusion. The most common country from which the data was derived was the United States (n = 5 studies). Upon meta-analysis, we observed no difference in non-adherence (i.e. PDC <80%) to SGLT2is versus GLP-1RAs (relative risk = 0.86; 95% confidence interval = 0.72-1.02). In the analysis, including only US studies, SGLT2i use was associated with a 23% lower risk of non-adherence compared with GLP-1RA use (relative risk = 0.77; 95% confidence interval = 0.72-0.82). In this meta-analysis of eight studies that included approximately 200 000 patients, there was no difference in adherence to SGLT2is versus GLP-1RAs. However, SGLT2i use was associated with higher adherence when the analysis was limited to US studies.

From Form(s) to Meaning: Probing the Semantic Depths of Language Models Using Multisense Consistency

Abstract The staggering pace with which the capabilities of large language models (LLMs) are increasing, as measured by a range of commonly used natural language understanding (NLU) benchmarks, raises many questions regarding what “understanding” means for a language model and how it compares to human understanding. This is especially true since many LLMs are exclusively trained on text, casting doubt on whether their stellar benchmark performances are reflective of a true understanding of the problems represented by these benchmarks, or whether LLMs simply excel at uttering textual forms that correlate with what someone who understands the problem would say. In this philosophically inspired work, we aim to create some separation between form and meaning, with a series of tests that leverage the idea that world understanding should be consistent across presentational modes — inspired by Fregean senses — of the same meaning. Specifically, we focus on consistency across languages as well as paraphrases. Taking GPT-3.5 as our object of study, we evaluate multisense consistency across five different languages and various tasks. We start the evaluation in a controlled setting, asking the model for simple facts, and then proceed with an evaluation on four popular NLU benchmarks. We find that the model’s multisense consistency is lacking and run several follow-up analyses to verify that this lack of consistency is due to a sense-dependent task understanding. We conclude that, in this aspect, the understanding of LLMs is still quite far from being consistent and human-like, and deliberate on how this impacts their utility in the context of learning about human language and understanding.