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p-Index From 2021 - 2026
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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Pengabdian Masyarakat Eka Prasetya
Joni
STMIK TIME
Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Public Health Other

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Workshop Pembuatan Kain Ecoprint Sebagai Modal Pengetahuan Siswa Menjadi Green Entrepreneur di SMA WR Supratman 1 Medan Elisabeth Nainggolan; Desma Erica Maryati M; Joni
Jurnal Pengabdian Masyarakat Eka Prasetya Vol 2 No 2: Jurnal Pengabdian Masyarakat Eka Prasetya
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat STIE Eka Prasetya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47663/jpmep.v2i2.377

Abstract

Making ecoprint fabric can increase students' insight into how to use the resources in the environment where they live to become a product that can be made easily, environmental friendly and become a product that has economic values if it is marketed and fulfilled the needs of fashion industry. Ecoprint fabric making activities can train students' how to be a creative thinking by arranging leaves as a motif on the fabric and practicing teamwork between other students. With this activity, students have knowledge and understanding that to become an entrepreneur you don't have to exploit nature but you can use nature without destroying it so that the students will be motivated as green entrepreneurs.
Design of 3D Puzzle Game "Moodoria" Using Unity as an Educational Media for Emotional Intelligence Bryan Anderson Basli; Didik Aryanto; Joni
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2312

Abstract

Emotional awareness is crucial for mental health, yet conventional education methods are often less engaging for adolescents. This study aims to design and develop a 3D puzzle game called "Moodoria" using Unity as an interactive medium for emotional intelligence education. The research method used is Research and Development (R&D) with the Game Development Life Cycle (GDLC) model, including needs analysis, literature study, concept design, implementation, and user testing. 3D assets were created using Blender. The game was tested on a small group of users (5–10 people) using a Likert-scale questionnaire. Results show that all main features (menu navigation, character movement, object interaction) function well. User assessments scored high on gameplay (4.87 for challenge) and enjoyment (4.67), and the game was considered feasible as an emotional education medium (average score 4.23). In conclusion, "Moodoria" was successfully developed as an engaging educational game, although sound effects and character expression variations need improvement.
Implementation of the Random Forest Algorithm for Loan Eligibility Prediction and Feature Analysis Based on Financial Data Angel; Joni; Herman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2317

Abstract

The advancement of information technology has led to an increasing demand for loan access, both through banking institutions and online lending platforms. However, the process of evaluating loan eligibility, which is still carried out manually or semi-manually, is prone to human error and decision-making bias, ultimately increasing the risk of loan defaults. This study aims to implement the Random Forest algorithm to predict loan eligibility based on financial data, as well as to evaluate its accuracy. The dataset used in this study is loan_approval_dataset.csv, which is downloaded from Kaggle, utilizing 11 input features. The system is developed as a web-based application using Laravel as the main frontend and backend framework, while Flask is used as a backend API for executing the machine learning processes. The testing results show that the Random Forest model achieves an accuracy of 98.44%, with a precision of 98.14%, recall of 99.37%, and an F1-score of 98.75%. Furthermore, the cibil score feature is identified as the most influential factor in the prediction process, contributing 80.65% to the model's outcome. These findings indicate that the Random Forest algorithm is highly effective for use in a loan eligibility prediction system, as it provides fast, objective, and highly accurate results.
Co-Authors Angel Bryan Anderson Basli Desma Erica Maryati Manik Didik Aryanto Elisabeth Nainggolan Herman