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JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Published by Lumina Infinity Academy
ISSN : -     EISSN : 30903939     DOI : -
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering
JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture The Journal of Information Technology Applications in Education, Economy, Health and Agriculture (JITEEHA), published by the Lumina Infinity Academy Foundation, was established in January 2024. JITEEHA is a rigorously reviewed, double-blind peer-reviewed journal committed to publishing high-quality articles. The focus of the journal encompasses the innovative application of information technology across various sectors including educational technology and management, economic systems, business, finance, healthcare, and agriculture. JITEEHA is published triannually, with issues released in February, June, and October each year. The journal aims to provide a platform for academics, researchers, and practitioners to disseminate their findings and contribute to the advancement of knowledge in these critical fields. This journal is published three issues per year, in February, June, and October.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 3 (2024): October" : 5 Documents clear
Determining Potential Players For The Indonesian Senior National Team In The 2026 World Cup Qualifications Using K-Means Risnanto, Slamet; Alfian, Fikri; Faiz, Moh Imam; Nizar, Moh.; Widarti, Dinny Wahyu
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 1 No. 3 (2024): October
Publisher : Lumina Infinity Academy Foundation

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Abstract

Football is a very popular sport, and the Indonesian National Team is the pride of the Indonesian people. In an effort to improve team performance, especially in facing the 2026 World Cup qualifiers, optimal player selection is a major challenge. This study applies data mining technology to determine potential players who can strengthen the Indonesian Senior National Team. Player data is taken from the Transfermarkt site which includes attributes such as player market value, club, and league. The methods used include data collection, data cleaning and normalization, and analysis using the K-Means clustering algorithm. The analysis process successfully grouped players into four clusters based on their potential. Players in clusters 1 and 3 have high potential to fill the main lineup, while players in cluster 0 show long-term development prospects. Visualization and manual evaluation support the interpretation of the results for strategic decision making. This study shows that the use of data mining can improve efficiency and accuracy in player selection, providing a more objective data-based approach. However, this study has limitations, such as the lack of consideration of non-technical factors. With the addition of data from other sources and the use of additional algorithms, this method can be further developed to support the performance of the Indonesian National Team optimally in the future.
Sentiment Analysis of Comments on Higher Education Social Media Using Naïve Bayes Algorithm Salisu, Imam Auwal; Ramadhan, Irzal Raisya; Matdoan, Sakina; Arifin, Zainal; Praseptiawan, Mugi
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 1 No. 3 (2024): October
Publisher : Lumina Infinity Academy Foundation

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Abstract

The rapid development of information technology has driven the widespread use of social media across various aspects of life, including the academic environment. Social media platforms, such as Instagram, have become popular channels for disseminating information and fostering interactions between individuals and groups. With the growing number of users, sentiment analysis on social media is essential to understand public perceptions and responses to specific issues. Higher education institutions play a strategic role in creating a positive image through social media. Social media provides opportunities for universities to convey achievements, academic activities, and other information effectively to a broader audience, enhancing their reputation in the public eye. Moreover, Instagram serves not only as a communication tool but also as an educational medium capable of increasing student engagement through relevant and informative content. Technically, the Naïve Bayes algorithm is well-known for its speed and efficiency in sentiment analysis. This probability-based method leverages historical data to predict positive, negative, or neutral sentiments, offering competitive accuracy even when handling large datasets. This study aims to apply the Naïve Bayes algorithm for sentiment analysis of comments on the Instagram account of Widyagama University (@uwg.malang) as a case study. The research is expected to provide valuable insights for developing effective communication strategies and serve as a reference for other higher education institutions or organizations in utilizing analytical technologies for strategic purposes.
Fruit Segmentation and Identification through Image Processing with K-Means and MobileNet V2 Nurhaliza, Siti; Atoilah, Faizun; Alimin, Alimin; Selviana, Renita; Muhimah, Ni'matul
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 1 No. 3 (2024): October
Publisher : Lumina Infinity Academy Foundation

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Abstract

This study presents the development of an application that integrates the K-Means Clustering algorithm and the MobileNetV2 pre-trained model to enhance image segmentation and object identification processes. Employing an experimental approach, the research incorporates Mini Batch K-Means technology to streamline image segmentation, significantly reducing computational overhead. Additional functionalities, including grayscale conversion, thresholding, and FAISS (Facebook AI Similarity Search)-based matching, are implemented to improve efficiency. The application features a user-friendly Tkinter-based GUI, enabling real-time image data upload and processing. The primary objective of this research is to optimize the accuracy and efficiency of segmentation and object identification for diverse practical applications. Experimental results demonstrate that the proposed algorithms and models achieve robust performance, establishing a foundation for the future advancement of more sophisticated technologies in this domain
Implementation of a Banknote Watermark Detection Application Leveraging Superior Segmentation Methods Fitriani, Adinda Nur; Rafif, Muhammad; Roji, Mukhamad Fatkhur; Alimin, Alimin; Khotimah, Khusnul
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 1 No. 3 (2024): October
Publisher : Lumina Infinity Academy Foundation

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Abstract

Detecting watermarks on banknotes is crucial for verifying authenticity and combating counterfeiting. This study focuses on developing a desktop-based application that leverages OpenCV and PyQt technologies to detect watermarks on banknotes effectively. The application incorporates five advanced segmentation methods: Otsu Thresholding, Adaptive Thresholding, Thresholding, Canny Edge Detection, and K-Means Clustering, aiming to enhance the accuracy of watermark identification. The development process involves digital image processing to extract watermark features and evaluate the performance of each segmentation method based on accuracy and efficiency. Testing results demonstrate that these methods achieve high accuracy in identifying watermarks across various banknote types. This application provides a practical and accessible solution for the public to verify the authenticity of banknotes swiftly and reliably.
Utilizing Datamining to Predict Sales Trends Based on Historical Data Junda, Alby Afifuddin; Trisna, Maria Rosalina; Genohon, Yustino Prami; Akhdan, Farrel Muhammad Raihan; Salisu, Imam Auwal
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 1 No. 3 (2024): October
Publisher : Lumina Infinity Academy Foundation

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Abstract

This study aims to compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms in predicting sales trends based on historical data. The results of the study show that SVM is more effective than Naïve Bayes with an accuracy of 34.74% compared to 15.49%. This study helps companies in making strategic decisions and improving operational efficiency. Data Mining is an important tool in predicting sales trends and improving prediction accuracy.

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