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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 35 Documents
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Trend Detection and Popular Topics on Social-Media Using a clustering algorithm to find patterns and topics that are going viral on the Instagram platform Jamaq, Evan; Hasan, Abdul Aziz; Kurniawan, M Rizal; Salisu, Imam Auwal
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 2 (2025): June
Publisher : Lumina Infinity Academy Foundation

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Abstract

This study aims to cluster Instagram posts based on hashtags and the number of likes using the K-Means Clustering algorithm. The data used is data that represents various popular topics on social media, such as travel, culinary, fashion, and local coffee. The analysis process involves data preprocessing, clustering algorithm implementation, and result evaluation to identify patterns and trends among users. The results successfully grouped posts into three main clusters, namely clusters with low engagement, clusters related to local food and coffee, and clusters with high engagement on travel and fashion topics. This clustering provides useful insights for marketers, content creators, and researchers in understanding social media user behavior and designing more effective marketing strategies. This research confirms the importance of data analysis as a tool to uncover hidden patterns and support data-driven decision-making.
Analysis of Cigarette Sales Transactions Using Apriori Algorithm at Madura Store Mahendra, Mochammad Augustiar; Sa'adah, Mamba'us; Puspitarini, Erri Wahyu; Rahman, Afida
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 2 (2025): June
Publisher : Lumina Infinity Academy Foundation

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Abstract

Developments in the cigarette industry continue to increase and there are also challenges in classifying cigarette sales. In this case, the method of classifying cigarette sales using the Apriori algorithm can be one way that can be used. The purpose of this study is to identify significant cigarette sales and classify sales transactions based on sales patterns. The method to be used in this study has several stages. First, we collect cigarette sales data from several different cigarette shops. The data includes information such as transaction ID, items purchased, and sales amounts. Then, we pre-process the data to prepare the raw data for further analysis. The results of this study indicate that classifying cigarette sales using the Apriori algorithm is able to identify significant sales patterns and classify transactions with a more adequate level of accuracy. This research provides new insights in analyzing cigarette sales data and can help decision-making in the cigarette industry.
Clustering Wi-Fi Users Based on Activity Patterns Using K-Means Algorithm Agustina, Rini; Dharmawan, Ario Fajar; Putri, Sandra Meylina Alaka; Putra, Yank Rizky Kharisma
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 2 (2025): June
Publisher : Lumina Infinity Academy Foundation

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Abstract

The rapid development of Wi-Fi networks has become a key pillar in supporting the internet needs of modern society. However, the increasing number of users and their diverse activity patterns pose challenges in network management, especially with regard to bandwidth allocation and service quality. Variations in activity patterns, such as social media, streaming, and gaming, create different bandwidth requirements for each user group. This imbalance in resource utilization can result in degraded quality of service, especially during peak hours. This research aims to address these challenges by clustering Wi-Fi users based on their activity patterns using the K-Means algorithm. The data used includes access time, usage duration, connection intensity, and user activity type. After going through the analysis process, users are grouped into several clusters based on the similarity of activity patterns. The clustering results show significant differences between light, medium, and heavy users in bandwidth consumption and duration of use. The results of this study contribute to more efficient Wi-Fi network management, especially in optimizing bandwidth allocation and supporting data-based decision-making. With customized management strategies for each user group, the quality of service can be significantly improved, providing a better experience for Wi-Fi users.
Analysis of Milkshake Beverage Sales using Apriori Algorithm Sujito, Sujito; Idris, Muhammad; Kadir, Shaifany Fatriana; Nurdiyansyah, Firman
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 2 (2025): June
Publisher : Lumina Infinity Academy Foundation

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Abstract

This research discusses the application of Data Mining with the Apriori algorithm on milkshake drink sales to support Business Intelligence. The research process includes collecting sales transaction data, forming frequent itemsets, and analyzing association rules using metrics such as support and confidence. The results show that product combinations, such as Chocolate and Strawberry, have high purchase rates with support reaching 75% and confidence up to 75%. These findings provide important insights for business owners in designing more effective marketing strategies, including promotions and stock management optimization. By utilizing the Apriori algorithm, this research successfully identified significant purchase patterns that can drive growth and improve customer satisfaction in the food and beverage industry.
Analysis of Restaurant Ordering Patterns Using Apriori Algorithm Marisa, Fitri; Badrussalam, Nanda; Ahmad, Sharifah Sakinah Syed; Vitianingsih, Anik Vega; Maukar, Anastasia L
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 2 (2025): June
Publisher : Lumina Infinity Academy Foundation

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Abstract

This study implements the Apriori algorithm to analyze ordering patterns in home-based restaurants, specifically Dapur Mb Yani. Sales transaction data for three weeks shows that the Geprek Sambal Merah, Geprek Sambal Ijo, and Ayam Crispy menus are the most frequently ordered items, both individually and in combination. The combination of Geprek Sambal Merah, Ayam Crispy, and Es Teh has a high association value, making it a candidate for bundling promotions, while the strong relationship between Geprek Sambal Merah and Geprek Sambal Ijo opens up opportunities for special offers involving both menus. These results help restaurant managers design more effective promotional strategies, manage ingredient stocks efficiently, and improve customer experience. The application of the Apriori algorithm proves its relevance in supporting data-based decisions, especially for small businesses, as well as opening up opportunities for further development in the culinary industry.
Application of Apriori Algorithm to Find Flower Purchase Patterns Tusianto, Daffa Yauzan; Fairuzabadi, Ahmad; Sujito, Sujito
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 3 (2025): October
Publisher : Lumina Infinity Academy Foundation

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Abstract

This research aims to apply the Apriori algorithm in analyzing flower purchase patterns at a flower shop. Apriori algorithm is used to identify product combinations that are often purchased together, in the hope of finding purchasing patterns that can be utilized to improve marketing strategies and store operational efficiency. Transaction data from the shop is processed to extract frequent itemsets and generate association rules by setting the right threshold of support and confidence values. The results of this study show that flower combinations such as Tulip and Bougenville frequently co-occur in purchases, with significant support-confidence products. These findings provide insights into consumer purchasing behavior that can be used to recommend product bundling or product rearrangement in stores. This research contributes to the application of data mining in the retail sector, particularly in increasing sales and customer satisfaction in flower shops.
Purchase Pattern Analysis on Komol Kopi Transaction Data Using Apriori Algorithm Pratama, Dafa Septian Putra; Praseptiawan, Mugi; Paramita, Niken
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 3 (2025): October
Publisher : Lumina Infinity Academy Foundation

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Abstract

This research aims to analyze purchasing patterns in Komol Kopi transaction data using the Apriori algorithm. This algorithm enables the discovery of relationships between items in large datasets that can be used to support business decisions, such as bundling promotions and inventory management. The dataset includes 12 transactions with various combinations of items, such as Kopi Hitam, Kopi Tubruk, and Nasi Telur. The analysis results show some significant purchase patterns with high support, confidence, and lift values. An example of an association found is between Kopi Hitam and Es Teh, which provides insights for more effective marketing strategies. This study confirms that the Apriori algorithm is an efficient tool in unearthing purchasing patterns, providing a solid foundation for the development of data-driven business strategies. Further research can integrate this analysis with recommendation systems to improve customer experience.
Analysis of Puchase Patterns on Office Stationery Sales Data using Apriori Algorithm Wahyudi, M. Ilham Setyo; Nurdiyansyah, Firman; Kristianti, Dini
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 3 (2025): October
Publisher : Lumina Infinity Academy Foundation

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Abstract

This study analyzes purchasing patterns in office stationery sales using the Apriori algorithm, a data mining method for generating association rules and frequent itemsets. The research examines transaction data to identify combinations of frequently purchased items, aiming to improve inventory management and marketing strategies. The Apriori algorithm calculates metrics such as support, confidence, and lift to determine strong associations between items. Results indicate key purchasing patterns, such as frequent copurchases of notebooks and pencils, which inform targeted promotions and stock planning. The findings highlight the potential of data-driven decisionmaking to enhance business efficiency and customer satisfaction in the retail sector.
Application of Data Mining with Apriori Algorithm on Furniture Sales to Support Business Intelligence Syamsudin, Mochammad; Nathasia, Novi Dian; Kadir, Shaifany Fatriana
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 3 (2025): October
Publisher : Lumina Infinity Academy Foundation

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Abstract

This study explores the application of Data Mining using the Apriori algorithm in furniture sales to support Business Intelligence. The research process includes collecting weekly transaction data, forming frequent itemsets, analyzing association rules using metrics such as support, confidence, and lift, and integrating the results into business strategies. The findings indicate that tables, wardrobes, and bookshelves have the highest purchase rates at 100%, followed by cabinets at 83.33%, chairs at 91.67%, and sofas at 66.67%. Strongly associated itemsets, such as {Table, Bookshelf} and {Wardrobe, Cabinet}, provide valuable insights for business owners in designing marketing strategies, maintaining stock availability, and enhancing customer satisfaction. Utilizing the Apriori algorithm, this study successfully identifies significant purchasing patterns that can be used to drive sustainable business growth in the furniture industry.
Apriori Algorithm and Business Intelligence Methods for Bookstore’s Customer Preferences Analysis Ramadhan, Silmy Kafi; Septiani, Devi; Rahman, Afida; Handayani, Endah Tri Esti
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 3 (2025): October
Publisher : Lumina Infinity Academy Foundation

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Abstract

This study explores the use of a priori algorithm in analyzing sales transaction data at Rony Jaya Bookstore. By combining data mining and business intelligence, the study successfully uncovered significant customer buying patterns, which were then used to support strategic decision-making. The results of the analysis showed that there was a close relationship between certain book categories, such as Fiction Books and Educational Books with a confidence level of 87.5%, as well as Non-Fiction Books and Educational Books with a confidence level of 88.89%. These findings provide valuable insights into developing marketing strategies, such as creating custom promotional packages and arranging product layouts in stores to make them more appealing to customers. This research also highlights the importance of ensuring data quality so that the resulting analysis is more accurate and relevant. Overall, the study offers a practical guide for Rony Jaya Bookstore and other businesses looking to leverage data mining and business intelligence technologies to improve efficiency and customer satisfaction.

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