Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
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This Author published in this journals
All Journal Journal of Information Systems and Technology Research Bigint Computing Journal Indonesian Journal of Sport Management and Physical Education (IJSMPE)
Amir Karimi
Farhangian University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Physics Other

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Leadership and Management Skills Impacting Sports Educators' Performance in Higher Education Institutions Zohaib Hassan Sain; Amir Karimi
Indonesian Journal of Sport Management and Physical Education Vol. 2 No. 2 (2023): August 2023
Publisher : Formosa Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/ijsmpe.v2i2.11596

Abstract

This study investigates how sports educators' leadership and management skills impact their performance in higher education institutions in Pakistan. A cross-sectional survey was conducted with 300 sports educators to collect the data. Descriptive statistics, Pearson correlation, and multiple regression analysis were utilized to analyze the findings. The results indicated a strong positive correlation (r = 0.72, p < 0.01) between leadership skills and performance, identifying leadership as the most significant predictor of educator success (β = 0.58, p < 0.001). Furthermore, the reliability of the scales was confirmed by high Cronbach's alpha values (α = 0.87, 0.82). This study underscores the importance of developing leadership skills among sports educators to enhance program effectiveness and improve student outcomes.
Development of a Web-Based AHP Decision Support System for Foster Child Character Evaluation Yuli Haryanto; Amir Karimi
Bigint Computing Journal Vol 3 No 2 (2025)
Publisher : Ali Institute of Reseach and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/bigint.v3i2.1083

Abstract

In an era of rapidly developing technology - especially among the millennial generation - the need for a fast yet simple and practical system has increased significantly. The Indonesian Independent Golden Generation Foundation, a social institution in Bekasi, has been facing challenges in managing data and determining the character of foster children which is being done manually and without a proper structure. This research aims to develop an application to determine the character of foster childrens using the Analytical Hierarchy Process (AHP) method. This application is designed to overcome problems of efficiency and accuracy in data management and to determine the character of foster childrens. The AHP method is used to break down complex problems into a hierarchically arranged components, using weighted comparisons for decision making. The research results show that this application can simplify the performance of the foundation administrators, speed up the process of determining the character of a foster children more objectively, and reduce subjectivity in decision making. There is criterias considered that includes honesty, independence, respect, creativity and discipline. This application is expected to be able to provide an effective solution for foundations in managing foster child data efficiently and accurately.
Deep Learning-Based Sentiment and Emotion Analysis of Social Media Data to Identify Factors Affecting Healthy Food Choices in Urban Communities Rachmat Rasyid; Muh Rafli R; Faisal Faisal; Suherwin Suherwin; Siti Nur Asia; Amir Karimi
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1288

Abstract

The increasing influence of social media on public perception has made it a powerful driver of dietary behavior in urban communities. Nevertheless, the abundance of unverified health information often obscures individuals’ ability to make informed food choices. This study proposes a deep learning-based framework to analyze sentiment and emotion from social media discourse in order to uncover the key factors affecting healthy food decisions in urban settings. By applying Natural Language Processing (NLP) techniques and advanced deep learning models to a large corpus of user-generated content, the research identifies significant patterns linking emotional expression with food-related decision-making. The results indicate that positive emotions, such as pride and satisfaction, are strongly associated with healthy food promotion, while negative emotions, including frustration, are predominantly tied to affordability, accessibility, and convenience issues. Among these, price and food quality emerge as the most critical determinants shaping consumer preferences. These findings underscore the importance of integrating emotional and socio-economic considerations into public health strategies. Beyond offering empirical insights, this study demonstrates the scalability and effectiveness of deep learning in extracting nuanced perspectives from unstructured social media data, thereby contributing a robust methodological approach for real-time public health monitoring and intervention design.  
Co-Authors Faisal Faisal Muh Rafli R Rachmat Rasyid Siti Nur Asia Suherwin Suherwin Yuli Haryanto Zohaib Hassan Sain