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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Nusantara Hasana Journal Kontigensi: Jurnal Ilmiah Manajemen Indonesian Community Journal Buletin Sistem Informasi dan Teknologi Islam
Nurdiansyah Nurdiansyah
Bisnis Digital, Universitas Dipa Makassar
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Nursing Public Health Social Sciences Other

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Analysis of Consumer Behavior Towards Product Groups at Indomaret Sudiang Raya Paulus Djohan Lolo; Nurdiansyah Nurdiansyah
Kontigensi : Jurnal Ilmiah Manajemen Vol 11 No 2 (2023): Kontigensi: Jurnal Ilmiah Manajemen
Publisher : Program Doktor Ilmu Manajemen, Universitas Pasundan, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56457/jimk.v11i2.489

Abstract

Effective marketing necessitates a profound understanding of consumer behavior, and the SOR Model (stimuli-organisms-response) stands out as a crucial theory in this domain. This model identifies three stages in the purchasing process: stimuli, organism (consumer's mind), and response. In this study, we explore consumer responses within various product categories, seeking to unveil patterns in decision-making processes. The experiment at Indomaret Sudiang Raya involved observing 146 randomly selected individuals, ensuring gender balance among respondents. The research encompassed three key sections: initial purchase goals and desires, observations of consumer behavior, and a survey on reasons for purchasing products. Our findings shed light on diverse consumer behaviors, encompassing immediate purchases, indecisiveness, analytical reviews, and non-stop behavior across different product categories. Certain product groups were less frequently purchased, suggesting dynamic shifts in consumer preferences over time. We delved into the cognitive and personal responses of Indomaret shoppers, revealing distinct patterns such as experiential behavior (purchasing known products) and analytical behavior (meticulously analyzing options before purchasing). The study identified nuances across product categories, providing valuable insights into the intricate landscape of consumer decision-making processes.
Scope Management in Industry 4.0 Projects Nurdiansyah Nurdiansyah; Masdar Masud; Serlin Serang
Kontigensi : Jurnal Ilmiah Manajemen Vol 12 No 1 (2024): Kontigensi: Jurnal Ilmiah Manajemen
Publisher : Program Doktor Ilmu Manajemen, Universitas Pasundan, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56457/jimk.v12i1.525

Abstract

This study investigates the implementation of marketing innovations within the framework of Industry 4.0, focusing on Asian corporate culture. Companies must innovate in the rapid globalization and digital transformation era to stay competitive and meet evolving client demands. The research identifies 15 critical attributes essential for marketing innovation in Industry 4.0, including augmented reality, virtual cryptocurrencies, and the Internet of Things (IoT). These innovations represent a blend of technological and non-technological advancements companies use to enhance their marketing strategies. The study examines the impacts of these innovations through a survey of 50 companies utilizing Industry 4.0 technologies. Key findings indicate significant impacts such as increased competitiveness, improved customer communication, and enhanced work efficiency. Larger companies and those in the automotive sector rate these impacts more highly, reflecting their greater reliance on technological advancements. Additionally, the study reveals that cultural factors influence the perception of these impacts, with companies in Asian cultures exhibiting varied responses compared to those with a global business culture. The pilot study utilized qualitative and quantitative methods, including structured surveys and descriptive statistical analysis. The results underscore the importance of effective scope management in successfully implementing Industry 4.0 projects, emphasizing the need for clear goal-setting, resource allocation, and change control. The research highlights the necessity for organizations to adapt their corporate culture and develop innovative marketing strategies tailored to their specific contexts. These insights offer practical recommendations for optimizing project outcomes and maintaining competitiveness in the digital age.
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.
A Hybrid K-Means and Neural Network for Enhancing Students’ Academic Performance Suriani Suriani; Muhammad Faisal; Darniati Darniati; Emil Agusalim H. T; Muhammad Syafaat S. Kuba; Swa Lee Lee; Nurdiansyah Nurdiansyah
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 7, No 2 (2026)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v7i2.3467

Abstract

Ketersediaan data pada Learning Management System (LMS) mendorong penerapan pembelajaran adaptif di pendidikan tinggi. Penelitian ini mengusulkan kerangka kerja hybrid berbasis kecerdasan buatan yang mengintegrasikan K-Means clustering dan Neural Network untuk profil mahasiswa berbasis perilaku dan prediksi kinerja akademik. Model divalidasi menggunakan Open University Learning Analytics Dataset yang mencakup data demografi, interaksi, dan performa akademik. Hasil menunjukkan akurasi sebesar 0,68 dan F1-score sebesar 0,66, melampaui metode dasar dengan stabilitas yang lebih baik. Clustering menghasilkan silhouette score 0,62 yang menunjukkan pemisahan kelompok yang jelas. Selain itu, sistem meningkatkan relevansi konten sebesar 27% dan menurunkan risiko putus studi sebesar 18%, dengan waktu inferensi rata-rata 0,85 detik. Temuan ini menunjukkan efektivitas kerangka dalam mendukung pembelajaran adaptif yang dipersonalisasi dan skalabel. Model hybrid yang diusulkan dapat mendukung pembelajaran adaptif melalui jalur belajar yang dipersonalisasi serta membantu perguruan tinggi melakukan intervensi dini terhadap mahasiswa berisiko berdasarkan pemantauan berbasis data.
Co-Authors Alimuddin Alimuddin Andi Muhammad Nur Hidayat Darniati Emil Agusalim H. T Hi Baharuddin, Suardi Ida Mulyadi, Ida Lisa Fitriani Ishak Made Widia, I Dewa Masdar Masud Medy Wisnu Prihatmono Muhammad Faisal Musdalifa Thamrin Nasir Usman Paulus Djohan Lolo Reskiani Reskiani Rosnani Rosnani S. Kuba, Muhammad Syafa'at Saharuddin Saharuddin Serlin Serang Sri Wahyuni Suriani Suriani Suriyanti Suriyanti Swa Lee Lee SYAFAR, A. MUHAMMAD