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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 25 Documents
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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
Fruit Segmentation and Identification through Image Processing with K-Means and MobileNet V2 Nurhaliza, Siti; Atoilah, Faizun; Alimin, Alimin; Selviana, Renita; Muhimah, Ni'matul
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 1 No. 3 (2024): October
Publisher : Lumina Infinity Academy Foundation

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Abstract

This study presents the development of an application that integrates the K-Means Clustering algorithm and the MobileNetV2 pre-trained model to enhance image segmentation and object identification processes. Employing an experimental approach, the research incorporates Mini Batch K-Means technology to streamline image segmentation, significantly reducing computational overhead. Additional functionalities, including grayscale conversion, thresholding, and FAISS (Facebook AI Similarity Search)-based matching, are implemented to improve efficiency. The application features a user-friendly Tkinter-based GUI, enabling real-time image data upload and processing. The primary objective of this research is to optimize the accuracy and efficiency of segmentation and object identification for diverse practical applications. Experimental results demonstrate that the proposed algorithms and models achieve robust performance, establishing a foundation for the future advancement of more sophisticated technologies in this domain
Implementation of a Banknote Watermark Detection Application Leveraging Superior Segmentation Methods Fitriani, Adinda Nur; Rafif, Muhammad; Roji, Mukhamad Fatkhur; Alimin, Alimin; Khotimah, Khusnul
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 1 No. 3 (2024): October
Publisher : Lumina Infinity Academy Foundation

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Abstract

Detecting watermarks on banknotes is crucial for verifying authenticity and combating counterfeiting. This study focuses on developing a desktop-based application that leverages OpenCV and PyQt technologies to detect watermarks on banknotes effectively. The application incorporates five advanced segmentation methods: Otsu Thresholding, Adaptive Thresholding, Thresholding, Canny Edge Detection, and K-Means Clustering, aiming to enhance the accuracy of watermark identification. The development process involves digital image processing to extract watermark features and evaluate the performance of each segmentation method based on accuracy and efficiency. Testing results demonstrate that these methods achieve high accuracy in identifying watermarks across various banknote types. This application provides a practical and accessible solution for the public to verify the authenticity of banknotes swiftly and reliably.
Utilizing Datamining to Predict Sales Trends Based on Historical Data Junda, Alby Afifuddin; Trisna, Maria Rosalina; Genohon, Yustino Prami; Akhdan, Farrel Muhammad Raihan; Salisu, Imam Auwal
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 1 No. 3 (2024): October
Publisher : Lumina Infinity Academy Foundation

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Abstract

This study aims to compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms in predicting sales trends based on historical data. The results of the study show that SVM is more effective than Naïve Bayes with an accuracy of 34.74% compared to 15.49%. This study helps companies in making strategic decisions and improving operational efficiency. Data Mining is an important tool in predicting sales trends and improving prediction accuracy.
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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