Ima Kurniastuti, Teguh Herlambang, Firman Yudianto, Awwalinnisa Fauji, Muhammad Halili

IMPLEMENTASI MACHINE LEARNING DALAM MANAJEMEN BISNIS DAN EKONOMI DI INSTITUTE OF TECHNOLOGY MANAGEMENT DAN ENTREPRENEURSHIP, UNIVERSITI TEKNIKAL MALAYSIA MELAKA

Introduction

Implementasi machine learning dalam manajemen bisnis dan ekonomi di institute of technology management dan entrepreneurship, universiti teknikal malaysia melaka. Tingkatkan pemahaman Machine Learning untuk manajemen bisnis & ekonomi melalui pelatihan di UTeM Melaka. Dapatkan keterampilan pengambilan keputusan berbasis data untuk era ekonomi digital.

Abstract

Perkembangan teknologi kecerdasan buatan, khususnya machine learning, telah memberikan dampak yang signifikan dalam bidang manajemen bisnis dan ekonomi. Di Institute of Technology Management and Entrepreneurship (IPTK), Universiti Teknikal Malaysia Melaka, teknologi ini mulai diperkenalkan kepada komunitas akademik melalui program pengabdian kepada masyarakat. Kegiatan ini bertujuan untuk meningkatkan pemahaman dan keterampilan peserta dalam menerapkan machine learning untuk pengambilan keputusan bisnis berbasis data. Melalui kegiatan sosialisasi dan pelatihan interaktif, peserta diperkenalkan pada konsep dasar machine learning, penggunaan algoritma sederhana, serta implementasinya dalam konteks bisnis. Selain itu, dikembangkan pula modul pelatihan sebagai panduan praktis agar peserta dapat menerapkan materi secara mandiri. Evaluasi kegiatan menunjukkan bahwa sebagian besar peserta merasa terbantu dan menunjukkan antusiasme tinggi, meskipun beberapa masih memerlukan pendampingan lanjutan. Hasil ini menunjukkan bahwa penerapan machine learning dalam pendidikan bisnis sangat efektif dalam mendorong literasi teknologi dan kesiapan menghadapi era ekonomi digital. Seluruh luaran yang ditargetkan telah tercapai sesuai dengan rencana

Review

This abstract presents a timely and highly relevant initiative focused on the integration of machine learning within business and economic management education. The program, conducted at the Institute of Technology Management and Entrepreneurship (IPTK), Universiti Teknikal Malaysia Melaka, addresses a critical need to bridge the gap between technological advancement and practical application in non-technical fields. By introducing machine learning concepts to the academic community through a community service program, the initiative effectively aims to enhance data-driven decision-making skills, positioning it as a significant step towards fostering digital literacy and preparing future professionals for the evolving digital economy. The methodology employed, encompassing socialization, interactive training, and the development of a practical module, appears robust for an introductory program. Participants were exposed to fundamental machine learning concepts and simple algorithms applied within a business context, which is crucial for practical understanding. The reported outcomes are positive, indicating that most participants found the program beneficial and demonstrated high enthusiasm. This positive reception, coupled with the successful achievement of all targeted outputs, underscores the effectiveness of this educational approach in promoting technological literacy and readiness for the digital age among business and economic management students. While the abstract provides a strong overview of a successful outreach program, a full paper would benefit from greater granularity in several areas. Detailing the specific "simple algorithms" introduced and providing concrete examples of their "implementasi dalam konteks bisnis" would enrich the reader's understanding of the program's scope. Additionally, a more quantitative or qualitative breakdown of the "evaluasi kegiatan," beyond general sentiment (e.g., pre- and post-assessment scores, specific skill acquisition metrics, or case studies of application), would further solidify the claims of effectiveness. Elucidating the nature of the "pendampingan lanjutan" required by some participants could also offer valuable insights for program refinement or the design of subsequent advanced modules.

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