Analysis Techniques Research Articles

The role of attitude mediates the effect of brand awareness and brand image on product purchase intention

This study aims to analyze the role of attitude in mediating brand awareness and brand image on purchase intention. The population in this study were consumers who had never used the Carasun brand sunscreen in Denpasar City. The number of samples used in this study were 160 samples. The method used in sample selection is purposive sampling, which is a sampling technique with certain considerations. The analysis technique used is Structural Equation Modeling (SEM) analysis based on Partial Least Square (PLS). The results showed that brand awareness and brand image have a positive and significant effect on purchase intention, brand awareness and brand image have a positive and significant effect on attitude, attitude has a positive and significant effect on purchase intention, and attitude has a role in partially mediating brand awareness and brand image on purchase intention. The results of the study can be concluded that the higher the brand awareness and the creation of a positive brand image in the minds of consumers, the positive consumer attitudes will emerge which encourage making purchases. The higher the brand awareness and the creation of a positive brand image in the minds of consumers, the higher the consumer's purchase intention. The creation of a positive attitude from consumers will create purchase intentions. The higher the brand awareness and the creation of a positive brand image will create a positive attitude from consumers, so that in the end the consumer's purchase intention will increase.

Integrating Sentiment Analysis and Machine Learning for Improved Film Recommendation Systems

Abstract: To do so, especially when it comes to digital entertainment platforms, which are evolving rapidly, giving users options for having what they want, and the importance of personalization has simply gone up only. This project focuses on movie recommendations using sentiment analysis, which increases the accuracy of recommendations by adding review sentiments based on movie ratings and reviews. This project presents a movie recommendation system that leverages collaborative filtering and sentiment analysis to deliver more accurate suggestions to movie enthusiasts, specifically targeting "Cinemaniacs" and "Film Junkies.” The primary components of our approach include collaborative filtering, such as taking movie preferences from the user and performing natural language processing (NLP) techniques for sentiment analysis of user-generated reviews and ratings. By integrating user preferences, such as selected genres and viewing habits, with sentiment-driven insights from movie reviews, the system extends beyond traditional numeric ratings to capture nuanced satisfaction and dissatisfaction. The platform also includes genre-specific recommendations and predictive models that forecast upcoming movie releases based on individual tastes, trends, and pre-release sentiments. This innovative combination provides users with highly customized movie and web series suggestions, keeping them updated on new releases, while offering trailer predictions to preview potential interests. Through this multi-layered approach, the project aims to deepen viewer engagement by closely aligning content recommendations with emotional responses and genre preferences

RELATIONSHIP BETWEEN ATTITUDE TOWARD STEM AND COMPUTATIONAL THINKING ABILITY IN MATHEMATICS LEARNING OF ELEMENTARY SCHOOL STUDENTS

Computational Thinking (CT) plays an important role in Science, Technology, Engineering, Mathematics (STEM) fields. However, whether Attitude toward STEM is related to CT skills is still unknown. The purpose of the study was to determine whether there is a relationship between Attitude Toward STEM and Computational Thinking skills in mathematics learning of elementary school students. This research is a correlational quantitative research that measures the relationship between two variables. The research was conducted at SDN Gandaria Utara 03 in class IVA and IVB. Simple random sampling technique was used to determine the research sample. The instruments in this study used a questionnaire to measure Attitude Toward STEM and a test instrument to measure Computational Thinking ability. The instruments in this study were measured for validity and reliability before use. Data analysis techniques using linear regression statistical tests. The results showed that Attitude toward STEM is related to Computational Thinking ability. From the results of data analysis, the regression model equation is Y = 19.635 + 0.387x. The coefficient of determination or R2 of 0.805 means that the Attitude Toward STEM variable simultaneously has an influence of 80.5% on the Computational Thinking ability variable

Machine learning insights into scapular stabilization for alleviating shoulder pain in college students

Abstract Non-specific shoulder pain is a common musculoskeletal condition, especially among college students, and it can have a negative impact on the patient’s life. Therapists have used scapular stabilization exercises (SSE) to enhance scapular control and mobility. This study investigates the prediction of the impact of scapular stability exercises in treating non-specific shoulder pain, leveraging advanced machine learning techniques for comprehensive evaluation and analysis. Using a diverse range of regression models, including Gamma Regressor, Tweedie Regressor, Poisson Regressor, and others, the study examines the relationship between the effectiveness of various exercises and their impact on shoulder pain management. Furthermore, the study employs optimization techniques, such as Hyperopt, scikit-optimize, optunity, GPyOpt, and Optuna, to fine-tune the exercise protocols for optimal outcomes. The results reveal that scapular stabilization exercises, when optimized using machine learning algorithms, significantly contribute to reducing shoulder pain in college students. Among the optimization techniques, scikit-optimize demonstrated the best performance, resulting in a mean squared error of 0.0085, a mean absolute error of 0.0712, and an impressive R2 score of 0.8501. This indicates that the scikit-optimize approach yielded the most accurate predictions and effectively captured the relationship between the exercises and shoulder pain management. The findings highlight the critical role of scapular stabilization exercise interventions in ameliorating non-specific shoulder pain and underscore the potential of machine learning techniques in optimizing therapeutic strategies for musculoskeletal health management. The utilization of scikit-optimize, in particular, showcases its effectiveness in fine-tuning the exercise protocols for optimal outcomes. The study’s results serve as a crucial stepping stone in developing personalized rehabilitation programs for non-specific shoulder pain, emphasizing the importance of integrating machine learning methodologies in the assessment and treatment of musculoskeletal disorders among college students.

Computational transformations of early childhood education: Pathways toward child‐centred computing in two educational change projects

AbstractProgramming is becoming a key subject in early education globally, with surging problems of how computer science can become a subject for children of all ages and backgrounds. Problems of implementing new technologies in the old curricula have long been noted, and lately, concern over computer science education goals is often too narrow and skills‐based without concerns for the critical educational potential. This study follows the change process of two case preschool departments implementing programming with a floor robot across a seven‐month design process. Mixed analytical techniques based on activity theory are used to examine the tensions, conflicts and development of pedagogical alternatives. The studies show the strenuous change processes involving local transformations toward a child‐centred, inclusive early computer science education and development of projects centring making, children's exploration, tinkering and play as key parts of the learning process. It discusses how the considerable change processes enabled pedagogical solutions coherent with important concepts in computer programming and how computing education can build on child‐centred pedagogies through local adaptations and age‐appropriate designs. Practitioner notesWhat is already known about this topic Computer science and programming are becoming part of early education worldwide. Less is known about how to implement computing with ever‐younger age groups, especially for those under three. That technological change can be a strenuous change process building on societal, historical and educational tensions, requiring individual learning and organisational change. What this paper adds Two cases of change designing computing education for 1–2‐ and 3–5‐year‐olds. Design processes raising critical questions about current standard childhood programming materials being resolved to enable a child‐centred computing education. Challenges and potentials specific to early childhood in adopting computer science education. Implications for practice and/or policy Computing education challenges pedagogical notions, such as the role of failure in education. Computing education can be developed for very young children. However, there are considerable critical challenges to resolve in developing a child‐centred approach. Childhood fundamentals such as play‐based pedagogy can work synergistically with computer science activities, providing educational potential, also relevant for computer science education at large.

Panning for gold: Comparative analysis of cross-platform approaches for automated detection of political content in textual data

To understand and measure political information consumption in the high-choice media environment, we need new methods to trace individual interactions with online content and novel techniques to analyse and detect politics-related information. In this paper, we report the results of a comparative analysis of the performance of automated content analysis techniques for detecting political content in the German language across different platforms. Using three validation datasets, we compare the performance of three groups of detection techniques relying on dictionaries, classic supervised machine learning, and deep learning. We also examine the impact of different modes of data preprocessing on the low-cost implementations of these techniques using a large set (n = 66) of models. Our results show the limited impact of preprocessing on model performance, with the best results for less noisy data being achieved by deep learning- and classic machine learning-based models, in contrast to the more robust performance of dictionary-based models on noisy data.

Pengaruh financial technology dan literasi keuangan digital terhadap pengembangan UMKM di Fakultas Ekonomi dan Bisnis Universitas Sam Ratulangi Manado

This study aims to examine the influence of financial technology and digital financial literacy on the development of MSMEs at Sam Ratulangi University. This type of research is quantitative research using a descriptive approach. The sampling technique used was the random sampling method and produced 35 MSMEs as research samples from 50 MSMEs. By using data collection techniques using questionnaires. The data analysis technique in this study is multiple linear regression using the JAMOVI v2.23.28 program. The results of the study partially show that financial technology has a significant and negative effect on the development of MSMEs, digital financial literacy has a significant and positive effect and financial technology and digital financial literacy together have a significant and positive effect on the development of MSMEs at Sam Ratulangi University.

Munawir dan Isu Mawarith: Sebuah Kajian Linguistik Pada Al-Nisā Ayat 11

<p style="text-align: justify;">The law of inheritance is a study that has been stipulated in the Qur'an in detail. This study examines how Munawir Syadali thinks about the division of inheritance and the factors behind the emergence of this thought. This study uses a qualitative approach with a literature study type. The data collection method is document analysis and uses content analysis techniques. The results of this study indicate that H. Munawir Sjadzali's thoughts on verse 11 of Surah An-Nisa' are related to the law of inheritance division, namely to equalize the division of inheritance between sons and daughters. Analysis of Munawir's biography, thoughts, and interpretation methods shows his hermeneutic approach, emphasizing the spirit of justice behind the text of the Qur'an compared to mere literal interpretation. This approach, which takes into account the current social and cultural context, differs from traditional interpretations that adhere to the 2:1 ratio stated in the verse. Several factors have shaped Munawir's views, including his educational background, partnership experience, and commitment to Islamic reform. He prioritizes freedom of thought and contextualization of Islamic teachings, so that they are relevant in the modern world. Although his approach is provocative, he conveys to broader and deeper discussions about the interpretation of the Qur'an and the law of mawarith.</p>

Point-of-Care Diagnostics Using Self-heating Elements from Smart Food Packaging: Moving Towards Instrument-Free Nucleic Acid-Based Detection.

Compromising between accuracy and rapidity is an important issue in analytics and diagnostics, often preventing timely and appropriate reactions to disease. This issue is particularly critical for infectious diseases, where reliable and rapid diagnosis is crucial for effective treatment and easier containment, thereby reducing economic and societal impacts. Diagnostic technologies are vital in disease modeling, tracking, treatment decision making, and epidemic containment. At the point-of-care level in modern healthcare, accurate diagnostics, especially those involving genetic-level analysis and nucleic acid amplification techniques, are still needed. However, implementing these techniques in remote or non-laboratory settings poses challenges because of the need for trained personnel and specialized equipment, as all nucleic acid-based diagnostic techniques, such as polymerase chain reaction and isothermal nucleic acid amplification, require temperature cycling or elevated and stabilized temperatures. However, in smart food packaging, there are approved and commercially available methods that use temperature regulation to enable autonomous heat generation without external sources, such as chemical heaters with phase change materials. These approaches could be applied in diagnostics, facilitating point-of-care, electricity-free molecular diagnostics, especially with nucleic acid-based detection methods such as isothermal nucleic acid amplification. In this review, we explore the potential interplay between self-heating elements, isothermal nucleic acid amplification techniques, and phase change materials. This paves the way for the development of truly portable, electricity-free, point-of-care diagnostic tools, particularly advantageous for on-site detection in resource-limited remote settings and for home use.

Effect of Firm Attributes on Financial Reporting Timeliness of Quoted Oil and Gas Companies in Nigeria

Published information may lose its relevance if there is undue delay in it being reported. However, around the world, delay in the auditing of financial statements have been identified as leading to an overall delay in their publication. The study investigates the effect of firm attributes on financial reporting timeliness of the eleven (11) listed oil and gas firms on the Nigerian Stock Exchange for the period 2011 to 2020. Sample of eight (8) companies were selected using purposive sampling technique after applying a single filter. The study used Ex-post facto research design. The variables considered were firm age, firm size, leverage and profitability. Financial Reporting timeliness was measured using audit report lag. Panel data were collected from the annual reports and accounts of the selected firms. The multiple regression analysis technique was used to analyse the data. The result of the study indicates that firm age, firm size and profitability significantly affect financial reporting timeliness while leverage does not influence financial reporting timeliness. Therefore, the study concludes that firm attributes especially firm age and profitability are good predictors of financial reporting timeliness. The study recommends that firms should grow their assets, strive to make profit and incur less debt as these factors are very important for investment and other decision purposes by the users of the information.

Peran Lembaga Adat Tigo Luhah Semurup dalam Penyelesaian Sengketa Tanah Warisan di Semurup

This research aims to describe the role of the Tigo Luhah Semurup Traditional Institution in resolving inherited land disputes in Semurup, as well as revealing the supporting and inhibiting factors of the Tigo Luhah Semurup Traditional Institution in resolving inherited land disputes in Semurup. The type of research used is qualitative research with descriptive methods. Determining research informants was carried out using a purposive sampling technique, namely informants were determined by researchers to obtain the required information, namely informants who were considered to know, understand and have experience related to this research. Data collection techniques and tools are carried out by means of observation, interviews and documentation. The tools used were observation sheets, interview guides, and cameras. Testing the validity of the data uses source triangulation. Data analysis techniques are data collection, data reduction, data display and drawing conclusions.The results of the research show that the role of the Tigo Luhah Semurup traditional institution in resolving land disputes, it has been very good, this can be seen from the fact that almost all disputes resolved in traditional institutions have been successful, as for the role of the Tigo Luhah Semurup traditional institution as a mediator, facilitator and decision maker. Then there are several factors that hinder the Tigo Luhah Semurup traditional institution in carrying out its role, including; 1) Difficulty in calling/inviting disputing parties to deliberation, 2) Limited budget so that the customary justice process does not run optimally, 3) low commitment from members of traditional institutions in resolving disputes.. However, there are several supporting factors for the Tigo Luhah Semurup traditional institution in carrying out its role, including; 1) the presence of experts and intellectuals in the dispute resolution process, 2) good communication head of traditional institutions, 3) the existence of social legitimacy for the Tigo Luhah Semurup traditional institution.

The Role of the School’s Committee in Implementing P5 in the Era of Independent Learning

The purpose of this study was to determine the role of the school committee and its contribution in improving the quality of education in the era of independent learning. This study was conducted at SMP Negeri West Jakarta Region 1. Interviews, observations, documentation, and literature reviews were the data collection tools used. This study is qualitative research with a descriptive analysis method. The data collection process was carried out through in-depth interviews and questioner. The technique of data analysis was classifying data based on the category, counting the frequency and percentage. This study found that the school committee, as part of the school element, is responsible for the smooth implementation of school programs in the current era of independent learning, the implementation of the Pancasila Student Profile Strengthening Project (P5), building relationships with the school community, improving facilities and infrastructure, and encouraging improvements in the quality of school resources. Here, the result of the percentage of each the categories they are: an advisory agency (consideration) as much as 25%, a support agency (supporter) as much as 25%. a controlling agency (controller) as much as 23% and a mediator as much as 27%.

Impact of tooth loss and patient characteristics on coronary artery calcium score classification and prediction.

This study, for the first time, explores the integration of data science and machine learning for the classification and prediction of coronary artery calcium (CAC) scores. It focuses on tooth loss and patient characteristics as key input features to enhance the accuracy of classifying CAC scores into tertiles and predicting their values. Advanced analytical techniques were employed to assess the effectiveness of tooth loss and patient characteristics in the classification and prediction of CAC scores. The study utilized data science and machine learning methodologies to analyze the relationships between these input features and CAC scores. The research evaluated the individual and combined contributions of patient characteristics and tooth loss on the accuracy of identifying individuals at higher risk of cardiovascular issues related to CAC. The findings indicated that patient characteristics were particularly effective for tertile classification of CAC scores, achieving a classification accuracy of 75%. Tooth loss alone provided more accurate predicted CAC scores with the smallest average mean squared error of regression and with a classification accuracy of 71%. The combination of patient characteristics and tooth loss demonstrated improved accuracy in identifying individuals at higher risk with the best sensitivity rate of 92% over patient characteristics (85%) and tooth loss (88%). The results highlight the significance of both oral health indicators and patient characteristics in predictive modeling and classification tasks for CAC scores. By integrating data science and machine learning techniques, the research provides a foundation for further exploration of the connections between oral health, patient characteristics, and cardiovascular outcomes, emphasizing their importance in advancing the accuracy of CAC score classification and prediction.

Analysis of Mineral Composition and Isotope Ratio as Part of Chemical Profiles of Apples for Their Authentication

One of the consequences of the globalisation of food markets is the effort enabling the control of food security and its origin. This might be traced by using different chemical composition analyses. However, for Central Europe, there is a lack of knowledge about the original reference values as well as their heterogeneity among the lands and countries. This study focused on characterizing the mineral profiles of apple tree fruits and comparing these profiles among different districts in Central Europe. The fruits of the apple cultivars ‘Gala’ and ‘Golden Delicious’ originated in the Czech Republic and Poland. The mineral and isotopic content of the apple fruit flesh was analysed using ICP-MS. The data were processed using the ANOVA test and compositely analysed using the PCA and LDA models. The results show relatively high variation in element distribution, particularly 87Sr/86Sr, Mn, Zn, Cu, Ca, P, and B, ranging between 20.6 and67.9% for both cultivars on average. However, their high variability within particular districts complicates the resolution of the LDA model. The reasons are linked to the geomorphological and pedological heterogeneity of the analysed districts as well as the particular sensitivity of the set of chosen primers to agronomic practices and tree performance. For this region, only partial separation among districts could be obtained by P, Ca, and Cu content, as well as the isotopic ratio of 10B/11B. However, the resolution of the geographical discrimination needs to be improved by an enhanced set of primers, the use of more precise analytical techniques for the Sr isotopic ratio, or by multiple chemical analyses. Furthermore, the heterogeneity of the analysed districts could be tackled by more detailed analyses at the level of micro-regions.

FACTORS INFLUENCING THE PURCHASE INTENTION OF MICRO TRANSACTIONS IN FREEMIUM MOBILE GAMES IN BANGKOK: A CASE OF HONKAI STAR RAIL

Purpose: The purpose of this research is to determine the significant influence of Performance Expectancy, Effort Expectancy, Social Influence, Hedonic Motivation, Price Value, and Game Satisfaction towards purchase intention of microtransactions in freemium mobile games in Bangkok in the case of Honkai: Star Rail. Understanding these influencing factors can help game developers and stakeholders gain valuable insights into player behavior and develop monetization strategies to maximize positive player experiences and revenue. Design/methodology/approach: This research uses primary and secondary data collection methods in addition to descriptive and inferential data analysis techniques to analyze the factors influencing the purchase intention of microtransactions in freemium mobile games. The data was collected from 396 respondents who live in Bangkok, have played Honkai: Star Rail on a smartphone, and have the intention to purchase microtransactions in Honkai: Star Rail. This research referenced twelve theoretical frameworks from previous studies to construct a conceptual framework. Findings: This research shows that Performance Expectancy, Hedonic Motivation, and Game Satisfaction have a significant influence on the purchase intention of microtransactions in freemium mobile games in Bangkok in the case of Honkai: Star Rail. Research limitations/implications: Because of the chosen research approach, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further. Practical implications: This research offers valuable insights for game developers and stakeholders to enhance monetization strategies for freemium games like Honkai: Star Rail. Social implications: This research highlights the growing influence of mobile gaming on consumer behavior. This has broader implications for discussions around digital consumption, addictive behaviors, and the ethics of in-game purchases, prompting stakeholders to consider responsible game design and marketing practices that prioritize player well-being while maximizing revenue. Originality/value: This paper fulfils an identified need to study the factors influencing microtransaction purchase intentions within the Bangkok market.

Improving the Learning Outcomes of Class VIII Students Through the Implementation of the Project-Based Learning (PjBL) Model in Science Learning

This research aims to improve the learning outcomes of junior high school students in science learning.This research is a type of classroom action research carried out in one of the public junior high schools in the Madiun district. The study population was class VIII with class VIII E samples. The data analysis technique used was to compare learning outcomes in the pre-cycle to the next cycle. The results of the analysis showed that the project-based learning (PjBL) learning model was able to improve the cognitive learning outcomes of junior high school students. The success indicator of the study was seen in the improvement in cognitive learning outcomes of students who achieved a minimum completeness criteria (MCC) score of 75. While the tang performance indicator used in this study is the success rate of cognitive learning outcomes up to 75%, in the pre-cycle, as many as 71.43% of the total students have not reached the specified minimum completeness criteria (MCC). While the remaining 38.10% have reached minimum completeness criteria (MCC). The implementation of the project-based learning (PjBL) learning model shows an increase in student success in learning. In cycle I, the percentage of student success reached 61.9%. In the second cycle, the percentage of student success reached 76.1%, so there was an increase of 33.4%.

Unconventional philosophies and practices: Vietnamese family firm performance in multiple countries

ABSTRACT The diversity of business philosophies and practices across family firms suggests their performance is influenced by factors that can be hard to isolate or understand. Based on 215 observations of Vietnamese firms operating in Asia, Australia, Europe, and North America, we use fuzzy-set qualitative comparative analysis techniques to discern the configurational relationships underlying their performance pathways. Of the 64 possible configurations, three pathways with a high consistency (95 percent) for high performance are distinguishable. These three pathways are characterized by varying degrees of family labor involvement, social network-based labor sourcing, and capital contributions from partners. They reveal how cultural factors, spiritual beliefs and practices, as well as the strength of firm networks influence the way these firms perform. Taken together, these insights offer valuable contributions to the theoretical understanding of family firm performance with practical and policy relevance.

Javanese and Batak Tribes: The Role of Moderation in Parenting Style on Self-Control in Adolscents

This research aims to determine the influence of parenting styles on self-control which is moderated by ethnicity in adolescents. The research design used is a correlational quantitative design. Sampling technique using method convenience sampling. The research used 2 scales, namely the Parental Style Inventory II (PSI-II) to measure parenting style and the Brief Self Control Scale to measure self-control. The data analysis technique in this research is using moderated regression analysis and simple regression tests. The research results show that ethnicity does not moderate the influence of parenting style on self-control (P Int_1 = 0.5323 > 0.05). Besides that, independent testing obtained significant results (parenting style influence self-control). However, when there is a covariate (another variable present), namely ethnicity and controlled simultaneously, it becomes insignificant (in the test using Hayes).

Analytical Techniques for the Determination of Metformin and its Combinations with Oral Antidiabetic Agents in Pharmaceutical Dosage Forms: Combinations with Sulphonylurea Antidiabetic Drugs

The sulfonylurea class of antidiabetic agents includes a range of closely related compounds, allowing for similar analytical approaches when combined with metformin. This article provides a comprehensive review of published methods for determining sulfonylureas and metformin combinations in bulk and pharmaceutical preparations. Various techniques such as high-performance liquid chromatography (HPLC), thin-layer chromatography (TLC), capillary zone electrophoresis (CZE), and spectrophotometric methods have been widely used for these compounds, yielding reliable results. Additionally, the article discusses the number of citations for each method and its specific purpose, offering critical commentary. Keywords: metformin; sulphonylureas; combination; determination; analytical techniques

Sensitivity of transcriptomics: Different samples and methodology alter conclusions in Gulf pipefish (Syngnathus scovelli).

Transcriptome analysis has become a central tool in evolutionary and functional genomics. However, variation among biological samples and analysis techniques can greatly influence results, potentially compromising insights into the phenomenon under study. Here, we evaluate differences in the brain transcriptome between female and male Gulf pipefish (Syngnathus scovelli). We perform comparisons between results from entire pipelines for brain transcriptome assembly, quantification, and analysis. We also offer a unique biological comparison between two sampling instances (Redfish Bay: n = 15, Port Lavaca: n = 7). Our results demonstrate crucial shortcomings with current experimental approaches. We found high variation within our results that was driven by both technical differences between pipelines and biological differences between pipefish samples. In our analysis of highly expressed genes, we found that the choice of methods influenced the degree of contamination or noise included in the identified genes. Notably, genes identified within the same pipeline were more similar than any other comparison. Our differential expression analysis revealed that both methodology and sampling location influenced the quantity and consistency of statistically significant transcripts. In the context of these results, we offer modifications to current practices that may increase the robustness of transcriptome-based conclusions. In particular, the use of a reference-guided assembly and an increase in sample sizes are likely to improve resistance to noise or error.