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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 40 Documents
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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

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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

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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

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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

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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

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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.
DistilBERT-Based E-Commerce Sentiment Analysis Zahri Aksa Dautd; Aviv Yuniar Rahman; Fitri Marisa
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 3 No. 2 (2026): Vol. 3 No. 2 (2026): June
Publisher : Lumina Infinity Academy Foundation

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Abstract

The rapid advancement of digital technology has driven significant growth in Indonesia’s e-commerce sector, with Shopee emerging as one of the largest platforms generating millions of product reviews daily. These reviews contain valuable consumer opinions that can be analyzed to assess customer satisfaction, yet their massive volume makes manual analysis inefficient and subjective. This study aims to develop an automated sentiment analysis model using DistilBERT to classify Shopee product reviews into positive and negative sentiments. The dataset comprises approximately 1 million Englishlanguage reviews covering various product categories, including electronics, fashion, beauty, and household items. The research methodology involves text preprocessing, tokenization using DistilBertTokenizerFast, and fine-tuning of the DistilBERT model under multiple data-split ratios (90:10, 80:20, 70:30, 60:40). Experimental results demonstrate that DistilBERT achieved the highest accuracy of 94.8%, outperforming baseline models such as Naïve Bayes (88.4%) and SVM (89.6%). These findings confirm that DistilBERT effectively maintains a balance between accuracy, precision, and recall while offering high computational efficiency. This research contributes both methodologically and practically by establishing DistilBERT as a scientifically robust and resource-efficient solution for large-scale sentiment analysis in Indonesia’s e-commerce environment.
Implementation of The Analytical Hierarchy Process (AHP) Algorithm to Support Decision Making For Determining Superior Orange Comodities Orange in Selorejo Village Yuni Agung Nugroho; Hanifatus Sahro; Rangga Pahlevi Putra; Hasbibullah
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 3 No. 2 (2026): Vol. 3 No. 2 (2026): June
Publisher : Lumina Infinity Academy Foundation

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Abstract

This study aims to explore the selection of orange varieties as the primary crop for farmers in Selorejo Village, Sengkaling, Malang Regency. Oranges have high economic value and are important for meeting the community’s nutritional needs; however, selecting the right variety must take into account factors such as cultivation expertise, environmental influences, and income potential.The research question posed is: “Which orange variety is most suitable to be the primary crop in Selorejo Village?” The study employs the Analytical Hierarchy Process (AHP), enabling a systematic analysis of criteria and alternatives. The research findings are expected to provide new insights into the selection of optimal orange varieties and to integrate environmental and economic factors that are often overlooked. Data analysis using AHP includes initial data collection, applying AHP to define criteria and alternatives, determining the priority of criteria and alternatives, and ensuring consistency in the analysis. Focusing on Selorejo Village, this study aims to make a tangible contribution to strategies for more sustainable orange cultivation and to improve farmers’ welfare. The results of the study indicate that income criteria are the primary factor in selecting orange varieties for farming in Selorejo Village, followed by cultivation practices and environmental influences, while the main orange varieties chosen are siem oranges, followed by mandarin oranges and sweet oranges.
The Implementation of the apriori algorithm to increase sales of clothing stores based on purchase patterns Khoirul Muhtadin; Dwi Anggarani; Khojanah Hasan; Survival Survival; Anastasia L Maukar; Irfan Faton
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 3 No. 2 (2026): Vol. 3 No. 2 (2026): June
Publisher : Lumina Infinity Academy Foundation

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Abstract

In the growing digital era, retail industries face significant challenges and opportunities. Clothing stores, as one type of retail industry, need to adapt to changes in consumer behavior that are increasingly complex. With the increasing variety of choices and easy access to information, understanding customer purchasing patterns is key to gaining a competitive advantage. Purchasing patterns reflect consumer preferences, not only that but can also reveal something hidden that if analyzed properly, can be utilized for a more effective marketing strategy. By applying a data-driven approach, it is hoped that clothing stores can formulate more targeted marketing strategies, improve customer satisfaction, and ultimately, drive sustainable sales growth. This research approach is exploratory quantitative, which aims to find customer purchase patterns from clothing store transaction data using the Apriori algorithm. The results of data exploration are used to develop data-based sales strategies.
The Student Mental Health Pattern Using Clustering and Classification Approaches Audrey Suitela; Silviana Silviana; Fahmi Bahaluan; Maurecia Tima; Indah Dewi Nurhayati; Zaenuddin Zaenuddin
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 3 No. 2 (2026): Vol. 3 No. 2 (2026): June
Publisher : Lumina Infinity Academy Foundation

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Abstract

Students mental health is a key factor in their academic and social development. However, the patterns and factors that influence mental health in college students are still not fully understood. This study utilizes machine learning-based clustering and classification techniques to identify hidden patterns in college students’ mental health data, focusing on social and demographic factors. Using the K-Means algorithm for clustering and Random Forest for classification, we group college students based on their mental health conditions and analyze the associations between variables such as age, marital status, anxiety, and medical history. The process begins with data exploration, followed by data cleaning and feature transformation to ensure optimal input quality. In the clustering stage, we find three main groups of college students with different mental health patterns, which are then used as the basis for a classification model. A Random Forest model is built to predict potential mental disorders, such as depression and anxiety, by identifying the features that have the most influence on the prediction results. The model evaluation shows significant performance with adequate accuracy, where the importance of social factors such as marital status and history of visits to medical professionals is clearly revealed. The results of this study not only offer important insights into students’ mental health patterns, but also provide recommendations for university policies in creating an environment that supports students’ mental well-being. This combined approach of clustering and classification opens up new opportunities in the application of machine learning for more precise and data-driven mental health analysis.
Application of Data Mining with Apriori Algorithm and FP Growth on Cafe Bread Sales to Support Business Intelligence Muhammad Auzhar Rafli Ramadhani; Shaifany Fatriana Kadir; Niken Paramita; Wiwin Purnomowati; Tshering Peldon; Farrel Muhammad Raihan Akhdan
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 3 No. 2 (2026): Vol. 3 No. 2 (2026): June
Publisher : Lumina Infinity Academy Foundation

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Abstract

This study aims to apply the Apriori and FP-Growth algorithms in analyzing sales transaction patterns in a bakery Cafe, with a focus on developing a business intelligence strategy. The data used includes 20,507 transactions from January 11, 2016 to December 3, 2017. The results of the analysis show that items (coffee and bread) are the most frequently purchased, with the highest support values of 26.67% and 32.72%, respectively. In addition, several significant association rules were found, such as a positive relationship between (hot chocolate and coffee). This study provides insights that can be used to design more effective marketing strategies, including bundling promotions and more efficient stock management, so as to increase sales and customer satisfaction.

Page 4 of 4 | Total Record : 40

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