cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Fitri Marisa
Contact Email
fitrimarisa@gmail.com
Phone
+6281555862223
Journal Mail Official
journaliteea@gmail.com
Editorial Address
Perum IKIP Tegalgondo blok 2J no 20 Malang
Location
Kota malang,
Jawa timur
INDONESIA
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
 1   2   3   4 
Supply Chain Optimization in the Retail Industry by Integrating Apriori Algorithms and Time Series Forecasting in Business Intelligence Putra, Gusty Nanda Kharisma; Silviana, Silviana; Riyadi, Agung; Praseptiawan, Mugi
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 3 No. 1 (2026): Vol. 3 No. 1 (2026): February
Publisher : Lumina Infinity Academy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study investigates the integration of the Apriori algorithm and time series forecasting within a Business Intelligence (BI) framework to optimize supply chain operations in the retail industry. The Apriori algorithm was utilized to identify significant purchasing patterns, enabling strategic decisions such as product bundling and cross-selling. Concurrently, time series forecasting, with an ARIMA model achieving a mean absolute percentage error (MAPE) of 8%, provided accurate demand predictions, supporting improved inventory management and resource allocation. The integration of these methods into a BI dashboard facilitated real-time monitoring and data-driven decisionmaking, leading to enhanced operational efficiency and reduced costs. While challenges such as data quality, computational resource demands, and user adaptability were observed, this research underscores the transformative potential of analytics in retail supply chain management. Future advancements in machine learning and IoT integration are recommended to further enhance system performance. Overall, this study demonstrates a pathway for retailers to achieve operational excellence and superior customer satisfaction through data-driven strategies.
Prediction of Informatics Engineering Student Graduation using Naïve Bayes Method Koten, Antonius Suban; Bouk, Anggela M; Rozi, Fatchulloh Reza Ar; Salisu, Imam Auwal
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 3 No. 1 (2026): Vol. 3 No. 1 (2026): February
Publisher : Lumina Infinity Academy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Student graduation is one of the indicators of the success of the educational process in higher education. This study aims to predict the graduation of students in the Informatics Engineering study program using the Naive Bayes method, by considering the Final Semester Exam (UAS), Mid-Semester Exam (UTS), assignments, and attendance as the main variables. The Naive Bayes method was chosen because of its simplicity in handling multivariable data and its ability to produce accurate classification models.
Implementation of Apriori Algorithm on Wet Cake Sales Darmawan, Firmansyah Aji; Marisa, Fitri
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 3 No. 1 (2026): Vol. 3 No. 1 (2026): February
Publisher : Lumina Infinity Academy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research implements the Apriori algorithm on wet cake sales data to identify frequent purchase patterns. In an era of intense business competition, efficient inventory management and sales strategies are essential, especially for perishable products. Daily sales data is analyzed using a quantitative approach, focusing on support and confidence as the main parameters. The analysis results show product combinations that are often bought together, such as {Bolu Pisang, Martabak}, with a support value of 42.86% and confidence of 75.00%. The findings provide valuable insights for designing marketing and stock management strategies, which can improve business competitiveness and sustainability. This research also encourages the application of similar techniques in other business sectors to improve operational efficiency.
Utilization of Artificial Intelligence in Consumer Sentiment Analysis on Social Media to Support Marketing Strategy Suprapto, Muchammad Zhulfikar; Marisa, Fitri; Andarwati, Mardiana; Puspitarini, Erri Wahyu
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 3 No. 1 (2026): Vol. 3 No. 1 (2026): February
Publisher : Lumina Infinity Academy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The rapid growth of social media platforms has transformed how consumers express their opinions, making sentiment analysis a critical tool for understanding consumer behavior. This research explores the use of Artificial Intelligence (AI) in sentiment analysis, specifically through Natural Language Processing (NLP) techniques, to analyze consumer sentiment on social media platforms such as Twitter and Instagram. By employing sentiment classification models, including BERT (Bidirectional Encoder Representations from Transformers) and Logistic Regression with TF-IDF, the study aims to uncover patterns in consumer sentiment and provide insights to businesses for developing effective marketing strategies. The results demonstrate that BERT outperforms Logistic Regression, offering higher accuracy, precision, recall, and F1-score in sentiment classification. Additionally, sentiment trend analysis highlights how consumer opinions fluctuate over time in response to marketing campaigns, while sentiment distribution analysis provides an overview of the general attitude toward products. This study offers a comprehensive AI-driven framework for businesses to improve customer satisfaction, optimize marketing efforts, and enhance brand loyalty through real-time sentiment insights.
Hybrid Clustering with Deep Learning in E-commerce for Customer Segmentation: A Data-Driven Approach for Business Strategy Optimization Sidharta, Robertus; Riyadi, Agung; Hanfiro, Pauline; Handini, Mia
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 3 No. 1 (2026): Vol. 3 No. 1 (2026): February
Publisher : Lumina Infinity Academy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Customer segmentation is a strategic approach to understanding customer needs and preferences, especially in the dynamic e-commerce industry. Traditional clustering methods, such as k-means, are often used for this task, but have limitations in handling complex and high-dimensional data. In this research, we use a hybrid clustering approach that integrates deep learning for feature extraction with traditional clustering algorithms for customer segmentation. Uses Mall Customers Dataset from Kaggle, which includes customer demographic and shopping behavior data. Experimental results show that this approach is able to produce more accurate and meaningful segmentation. The visualization of the results shows significant patterns that can be used to develop more personalized and effective marketing strategies.

Page 4 of 4 | Total Record : 35

 1   2   3   4 

Filter by Year

2024 2026


Reset
Filter By Issues
All Issue Vol. 3 No. 1 (2026): Vol. 3 No. 1 (2026): February Vol. 2 No. 3 (2025): October Vol. 2 No. 2 (2025): June Vol. 2 No. 1 (2025): February Vol. 1 No. 3 (2024): October Vol. 1 No. 2 (2024): June Vol. 1 No. 1 (2024): February