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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
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Search results for , issue "Vol. 2 No. 1 (2025): February" : 5 Documents clear
Analysis of Online Transportation Customer Satisfaction Using C4.5 Algorithm Irawan, Ryan Avrilio; Marpaung, Fhadillah Ain; Saputra, Idris Ivan; Widarti, Dinny Wahyu; Fairuzabadi, Ahmad
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 1 (2025): February
Publisher : Lumina Infinity Academy Foundation

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Abstract

In the era of increasing business competition, transportation companies are required to enhance the efficiency and effectiveness of their services. One method that can be employed to optimize fleet management is through Data Mining analysis. This study focuses on optimizing Ojek online transportation services using the C.4.5 Algorithm method. The aim of this research is to group customers and areas based on service demand patterns, thus improving fleet distribution and reducing waiting times. The data used in this study includes location, demand, and trip frequency information. The analysis results show that the C.4.5 algorithm method effectively groups the data, providing optimal fleet distribution and enhancing service performance. This research demonstrates that applying data mining through the C.4.5 algorithm method can be an effective strategy for improving management and operational efficiency in Ojek online transportation services, offering competitive advantages in service efficiency and customer satisfaction.
Application of the Naive Bayes Data Mining Algorithm to Predict Used Motorcycle Purchase Decisions Masdiyanto, Andreas; Kiyosaki, Robert Baz; Hakiki, Sudrajad; Akhdan, Farrel Muhammad Raihan; Peldon, Tshering
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 1 (2025): February
Publisher : Lumina Infinity Academy Foundation

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Abstract

This study applies the Naive Bayes algorithm to predict the decision to purchase used motorcycles based on attributes such as model, year of manufacture, price, engine capacity, and transaction results. Utilizing the Gaussian Naive Bayes approach for continuous data, this research aims to develop a reliable predictive model and understand the most significant attributes influencing purchasing decisions. The test results show that the predictive model achieves an accuracy rate of 75%, indicating the effectiveness of the Naive Bayes algorithm in handling data classification. This study provides insights that can help industry players enhance their sales strategies based on accurate data analysis.
Classification of Used Car Prices Using the Naive Bayes Method Abillah, Bintang; Pratama, Djourdi Amrida; Baskara, Rizandi Agung; Praseptiawan, Mugi; Hanfiro, Pauline
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 1 (2025): February
Publisher : Lumina Infinity Academy Foundation

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Abstract

This research uses the Naive Bayes algorithm to predict used car purchasing decisions based on attributes such as brand, year of production, mileage, engine condition, completeness of features, and maintenance history. By applying the Gaussian Naive Bayes approach to handling continuous data, this research aims to develop a reliable prediction model while identifying the attributes that most influence purchasing decisions. The test results show that the prediction model achieved a correct accuracy level of 80%, and an incorrect accuracy of 20%, which indicates the ability of the Naive Bayes algorithm to handle data classification. This research provides insights that can support industry players in designing more effective sales strategies based on accurate data analysis.
Clustering Of Informatics Study Program Based On Understanding The Material Using The K-Means Algorithm Prasetyo, Naufal Ibra; Bria, Dionisia Kasilda; Paratu, Jeki Bani; Wafa, Fachrian Muhammad Ahzami; Salisu, Imam Auwal
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 1 (2025): February
Publisher : Lumina Infinity Academy Foundation

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Abstract

The level of student understanding in coursework is a crucial determinant of academic success, reflecting both teaching quality and the effectiveness of applied learning methods. In the context of Informatics, challenges often stem from the complexity of subjects such as algorithms, programming, and data analysis, which require analytical and in-depth comprehension. However, differences in learning abilities, backgrounds, and styles often result in varying levels of understanding among students. This study investigates the application of k-means clustering as an innovative method to analyze academic data and classify students based on their understanding of course materials. By utilizing data such as exam scores, quiz results, and classroom engagement, k-means clustering identifies patterns in students’ comprehension levels, offering educators insights to tailor teaching strategies effectively. The findings of this study are expected to aid educators in designing targeted interventions, enhance learning processes, and support an inclusive and effective academic environment.
E-commerce Transaction Fraud Detection Using the Naive Bayes Algorithm Dautd, Zahri Aksa; Aqmal S, M Fauzan; Sugiarta, Achmad; Rahman, Afida
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 1 (2025): February
Publisher : Lumina Infinity Academy Foundation

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Abstract

This study utilizes the Naive Bayes algorithm to detect fraudulent transactions occurring on e-commerce platforms by analyzing several key attributes, including the transaction time, transaction amount, the user's geographic location, and the payment method used. This algorithm was chosen due to its advantage of simplicity in handling probabilistic-based classification, which facilitates the analysis of complex data. Based on the study's findings, the Naive Bayes model demonstrates a commendable ability with an accuracy rate of 80% in identifying transactions categorized as fraudulent activities. This research contributes valuable insights that can be applied to enhance the security and trust in online transaction systems.

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