Article Per Year (5 Year)
p-Index From 2020 - 2025
0.751
P-Index
This Author published in this journals
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

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
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.
Utilization of Artificial Intelligence to Support Technology Development at PT. Aplikanusa Lintasarta – Makassar Faisal, Muhammad; Usman, Nasir; Mulyadi, Ida; Rosnani, Rosnani; Darniati, Darniati; Thamrin, Musdalifa; Mardiah, Mardiah; Watratan, Alvina Felicia
I-Com: Indonesian Community Journal Vol 5 No 2 (2025): I-Com: Indonesian Community Journal (Juni 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/icom.v5i2.6945

Abstract

This community service activity aimed to enhance the understanding of Machine Learning (ML) and Deep Learning (DL) technologies among employees of PT. Aplikanusa Lintasarta, as an academic contribution to supporting the company’s digital transformation acceleration. Conducted in a hybrid format (offline and online) on April 21, 2025, the program featured expert speakers and employed an interactive outreach approach combined with applicable case studies. To assess its effectiveness, pre-test and post-test instruments were utilized, revealing an average increase of 45% in participants’ comprehension. Participants' responses were highly positive, as demonstrated by their enthusiasm during discussions and interest in implementing ML/DL within the workplace. This activity not only strengthened internal technological literacy but also supported the development of the national AI ecosystem, in alignment with the launch of GPU Merdeka by Lintasarta.
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.
Co-Authors Alvina Felicia Watratan Andi Harmin Darniati Darniati Hi Baharuddin, Suardi Ida Mulyadi Ida Mulyadi, Ida Made Widia, I Dewa Medy Wisnu Prihatmono Muhammad Faisal Mulyadi, Ida Musdalifa Thamrin Musdalifa Thamrin Nurdiansyah Nurdiansyah Rosnani Rosnani Rosnani Rosnani