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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. 2 (2024): June" : 5 Documents clear
Application of the Naive Bayes Algorithm to Predict The Purchase Decisions Puspitarini, Erri Wahyu; Masdiyanto, Andreas; Kiyosaki, Robert Baz; Hakiki, Sudrajad; Conteh, Alusine; Wafa, Fachrian Muhammad Ahzami
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 1 No. 2 (2024): June
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.
Clustering Of Informatics Students Based On Understanding The Material Using The K-Means Method Irmayanti, Meiselina; Prasetyo, Naufal Ibra; Bria, Dionisia Kasilda; Paratu, Jeki Bani; Hanfiro, Pauline
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 1 No. 2 (2024): June
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.
PREDICTION OF INFORMATICS ENGINEERING STUDENTS' GRADUATION USING THE NAIVE BAYES METHOD BASED ON VALUES ASSIGNMENTS AND ATTENDANCE Akhdan, Farrel Muhammad Raihan; Koten, Antonius Suban; Bouk, Anggela M; Rozi, Fatchulloh Reza Ar; Agustina, Rini
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 1 No. 2 (2024): June
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.
Enhancing Scholarship Selection Process with a Simple Additive Weighting-Based Decision Support System Munir, Misbahul; Rahardiyanto, Panca; Sandi, M. Daryl Bey
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 1 No. 2 (2024): June
Publisher : Lumina Infinity Academy Foundation

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Abstract

Scholarships play a critical role in supporting students' educational pursuits, particularly those from financially disadvantaged backgrounds. The increasing number of applicants, however, poses challenges for fair and efficient scholarship selection. This study proposes a Decision Support System (DSS) utilizing the Simple Additive Weighting (SAW) method to streamline the scholarship recipient selection process. The system evaluates applicants based on seven criteria, including GPA score, SKKM (Student Activity Credit Unit), Total Parent's Income, Number of siblings, Status of Receiving Scholarship, Employment Status, Age. Data normalization was implemented to standardize criteria with varying scales, ensuring fairness and comparability. The system was tested on real-world data, demonstrating an effective ranking mechanism with high consistency compared to expert evaluations (Spearman’s rs=0.92). Key findings highlight the system's transparency, flexibility in adjusting weights, and efficiency in handling large datasets. This research contributes to the development of equitable scholarship distribution mechanisms by offering an objective, data-driven approach to decision-making. Future enhancements may include integrating machine learning techniques to improve predictive capabilities.
Android-Powered Home Lighting: A Control and Monitoring System for Smart Living Sobri, Arifian; Mualim, Wildan; Riswandha, M. Noval
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 1 No. 2 (2024): June
Publisher : Lumina Infinity Academy Foundation

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Abstract

This research aims to address the manual control and maintenance of community lights by developing a remote light controller and detector using the Internet of Things (IoT) concept. The process involves the use of an Android-based smartphone application to control and monitor the lights. The system utilizes a lamp current, which is charged on the ledge, and a relay driver to turn the lights on or off. The Wemos microcontroller, with the ESP8266 Wi-Fi module, serves as the link between the smartphone and the server. The results of the research show that the photodiode light sensor operates effectively when activated, and the current sensor can determine the number of lights that are turned off. Users can access the control and monitoring of the lights through the Android application, allowing them to control the lights remotely and keep track of the number of lights that are functioning properly

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