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All Journal JUITA : Jurnal Informatika Conference on Business, Social Sciences and Technology (CoNeScINTech)
Zulkarnain, Nur Zareen
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science

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Strategic Analysis of Business Decisions PT Flextronics Techology Indonesia by Utilizing Generative Artificial Intelligence (GenAI) in the Electronics Manufacturing Aklani, Syaeful Anas; Basiron, Halizah; Zulkarnain, Nur Zareen
Conference on Business, Social Sciences and Technology (CoNeScINTech) Vol. 5 No. 1 (2025): Conference on Business, Social Sciences and Technology (CoNeScINTech)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/conescintech.v5i1.10375

Abstract

New technologies in manufacturing companies continue to develop and innovate, companies face increasing challenges related to operational efficiency, supply chain complexity, and the need for continuous innovation. PT Flextronics Technology Indonesia, as one of the large-scale industries in the Batam city, is trying to overcome this challenge by integrating Generative Artificial Intelligence (GenAI) into its strategic decision making. GenAI, a transformative part of artificial intelligence, offers unmatched capabilities in generating insights, optimizing processes, and driving innovation.  This research carries out a comprehensive strategic analysis of PT Flextronics Technology Indonesia's decision by evaluating the potential impact of using GenAI in all main operational domains such as management, engineering and quality. Using a mixed-methods approach, this research combines qualitative insights from stakeholder interviews and surveys with quantitative analysis of pilot projects to assess the feasibility and benefits of Gen AI integration. The findings highlight that GenAI significantly improves decision-making accuracy, reduces operational costs, and drives innovation in product design and production processes. However, successful implementation requires addressing challenges such as workforce readiness, data security, and initial investment costs. The study also emphasizes the importance of aligning GenAI initiatives with organizational goals and encouraging stakeholder engagement to ensure continued adoption. This research provides actionable insights and a roadmap for PT Flextronics Technology Indonesia utilizes GenAI as a strategic tool, strengthening its position as a leader in the electronics manufacturing sector while setting a benchmark for future AI-based industrial transformation.
Leafy AI: Integrating MobileNetV2 and TensorFlow Lite into a Flutter-Based Application for Real-Time Ornamental Plant Recognition Setyawan, Haris; Zulkarnain, Nur Zareen; Fikri, Abian Ayatullah
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

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Abstract

Operating artificial intelligence on smartphones attracted interest in various applications, but in practice, device capacity limited AI capabilities. Limited processing power, restricted memory capacity, and unstable network connectivity could make AI models difficult to use outside lab environments. In this work, we describe Leafy AI, a mobile application that identifies ornamental plants designed to work fully on the device. The classifier is based on MobileNetV2 and trained with transfer learning using 67,200 images from 112 plant categories. Images were resized to 224 × 224 pixels and normalized before training. After training, the model was converted into TensorFlow Lite format and integrated within a Flutter application. A lightweight service layer manages preprocessing and inference so that the interface remains simple for the user. Evaluation using 13,440 test images achieved a top-one accuracy of 0.89. A smaller field experiment involving 226 photos captured under real-world conditions resulted in lower accuracy, primarily due to variations in lighting and background. Nevertheless, the system remained reliable in offline mode. The findings show that recognition of ornamental plants can be carried out on ordinary smartphones and that further improvements are possible through augmentation, domain adaptation, quantization, and hardware acceleration.
Co-Authors Basiron, Halizah Fikri, Abian Ayatullah Haris Setyawan, Haris Syaeful Anas Aklani, Syaeful