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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Nusantara Hasana Journal Indonesian Community Journal
Nasir Usman
STMIK Profesional Makassar, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Environmental Science Nursing Public Health Social Sciences Other

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Microsoft Copilot Training for Monitoring Student Learning: A Case Study Vocational High School Makassar - Indonesia Dikwan Moeis; Nasir Usman; Muhammad Faisal; Andi Harmin; Ida Mulyadi; Musdalifa Thamrin
I-Com: Indonesian Community Journal Vol 4 No 3 (2024): I-Com: Indonesian Community Journal (September 2024)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/icom.v4i3.5134

Abstract

Artificial intelligence (AI) has become an increasingly popular technology and brings significant educational benefits. This technology increases the learning process's efficiency and productivity, allowing for the development of students' abilities in a more focused manner. AI is a catalyst in preparing generations to face future challenges. One example of AI's application in education is Microsoft Copilot, an artificial intelligence model developed by Microsoft in collaboration with OpenAI. Microsoft Copilot is designed to understand and support various academic tasks through human-like interactions. Training on using Microsoft Copilot was carried out for students of SMKS Wahyu Makassar. This training aims to support the learning process, increase learning effectiveness, and assist students in doing academic assignments. The evaluation results showed that Microsoft Copilot provided significant benefits, with positive feedback from participants. Most students found this training useful, easy to understand and improved their knowledge.
COMPARISON OF THE PERFORMANCE OF REGRESSION-SPECIFIC AND MULTI-PURPOSE ALGORITHMS Usman, Nasir; Darniati, Darniati; Rosnani, Rosnani; Musdalifa Thamrin; Nurahmad, Nurahmad; Nurdiansyah, Nurdiansyah; Faisal, Muhammad
Nusantara Hasana Journal Vol. 4 No. 8 (2025): Nusantara Hasana Journal, January 2025
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v4i8.1274

Abstract

Regression is a data science method for evaluating the relationship between independent and dependent variables. This study compares the performance of various regression algorithms using the Boston Housing Dataset, which consists of 506 samples divided into 80% for training and 20% for testing. Performance evaluation was conducted using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). All algorithms were implemented with default hyperparameter settings provided by the Scikit-learn library to ensure fair comparison. The results showed that versatile algorithms, particularly Gradient Boosting Machines (GBM) and Random Forest, achieved the best performance with R² values of 0.92 and 0.89, respectively, and lower errors. Conversely, regression-specific algorithms, such as Linear Regression and Ridge Regression, recorded R² values of approximately 0.67, while the k-Nearest Neighbors algorithm had the lowest performance with an R² of 0.65. Versatile algorithms proved to be more effective for datasets with complex non-linear patterns, while regression-specific algorithms were better suited for linear data patterns. These findings provide guidance for practitioners in selecting algorithms based on data characteristics and analysis objectives.
Machine learning for global trade analysis: a hybrid clustering approach using DBSCAN, elbow, and SOM Thamrin, Musdalifa; Mulyadi, Ida; Made Widia, I Dewa; Faisal, Muhammad; Hi Baharuddin, Suardi; Prihatmono, Medy Wismu; Nurdiansyah, Nurdiansyah; Usman, Nasir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3033-3046

Abstract

Global trade constitutes a highly complex and interdependent system influenced by diverse economic, geographic, and political factors. This study proposes a hybrid clustering framework that integrates density-based spatial clustering of applications with noise (DBSCAN), elbow, and self-organizing maps (SOM) methods to uncover latent structures in international trade patterns. Utilizing averaged trade data from 25 countries spanning the period from 2013 to 2023, the framework identifies distinct clusters based on export-import characteristics. The DBSCAN is employed to detect dense trade hubs and outlier behaviors, the elbow method determines the optimal number of clusters, and SOM facilitates the visualization of non-linear, high-dimensional trade relationships. The analysis reveals three prominent trade clusters: Global Trade Leaders, Emerging Trade Powers, and Niche Exporters, each reflecting varying degrees of trade diversification and dependency. These empirical findings align with established economic theories, including the Heckscher Ohlin model and dependency theory, and provide actionable insights for policymakers seeking to enhance trade competitiveness and regional integration strategies.
Enhancing Faculty Digital Competence through Learning Management System Training: A Case Study at STMIK Profesional Makassar Nasir Usman; Muhammad Faisal; Sri Wahyuni; Lisa Fitriani Ishak; A. Muhammad Syafar; Saharuddin Saharuddin; Nurdiansyah Nurdiansyah; Andi Muhammad Nur Hidayat
I-Com: Indonesian Community Journal Vol 5 No 3 (2025): I-Com: Indonesian Community Journal (September 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/i-com.v5i3.7787

Abstract

The digital transformation in higher education necessitates the enhancement of faculty capacity in leveraging online learning technologies, one of which is the Moodle Learning Management System (LMS). This community service initiative aimed to strengthen the competencies of lecturers at STMIK Profesional Makassar in effectively and independently utilizing Moodle LMS. The training was conducted in two sessions using a hands-on approach, covering the management of instructional materials, discussion forums, quizzes, assignments, and learning evaluations. The results indicated significant improvements in mastering Moodle’s structure and core features, lecturers’ readiness to manage an inclusive, flexible, and adaptive digital learning ecosystem, as well as their ability to develop digital learning materials, manage discussion forums, and design system-based evaluations. These enhancements had a direct impact on the quality of interaction and student learning outcomes. Moreover, this initiative supported sustainable digital transformation at the institutional level, reinforcing STMIK Profesional Makassar’s image as a progressive higher education institution responsive to the challenges of the Fourth Industrial Revolution.
Strengthening AI and DSS Synergy for Sustainable Research: A Community Engagement for Lecturers and Researchers in Palopo Muhammad Faisal; Nasir Usman; Emil Agus Salim Habi Talib; Medy Wisnu Prihatmono; Lisa Fitriani Ishak; Musdalifa Thamrin; Darniati Darniati; Alvina Felicia Watratan; Saharuddin Saharuddin; Muh Ilham Akbar
I-Com: Indonesian Community Journal Vol 5 No 4 (2025): I-Com: Indonesian Community Journal (Desember 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/i-com.v5i4.8547

Abstract

The rapid development of digital technology demands a more innovative and data-driven research paradigm, yet the utilization of Artificial Intelligence (AI) and Decision Support Systems (DSS) in academic environments remains hindered by digital literacy gaps and the dominance of subjective manual methods. This community engagement program aims to introduce and strengthen participants’ understanding of the synergy between AI and DSS in supporting sustainable research in the era of digital transformation. The program employed a participatory approach through the Quadruple Helix model involving 359 participants consisting of lecturers, researchers, and practitioners. Methods included interactive lectures, technical mentoring on hybrid intelligence (integration of Machine Learning and Multi-Criteria Decision Making), and collaborative discussions via the Zoom platform. The results indicate a 35.6% improvement in participants' digital literacy, with the mean score increasing from 62.5 to 84.8. Furthermore, the technical readiness survey yielded a high score of 4.35 on a Likert scale, with participants successfully identifying practical AI–DSS applications in smart agriculture and MSME development. This program has successfully established an initial foundation for an adaptive and inclusive research ecosystem.
Co-Authors A. Muhammad Syafar Alvina Felicia Watratan Andi Harmin Andi Muhammad Nur Hidayat Darniati Darniati Darniati Darniati Emil Agus Salim Habi Talib Hi Baharuddin, Suardi Ida Mulyadi, Ida Lisa Fitriani Ishak Made Widia, I Dewa Medy Wisnu Prihatmono Medy Wisnu Prihatmono Muh Ilham Akbar Muhammad Faisal Musdalifa Thamrin Musdalifa Thamrin Nurdiansyah Nurdiansyah Nurdiansyah Nurdiansyah Rosnani Rosnani Saharuddin Saharuddin Sri Wahyuni