Abstract:Perkembangan revolusi Industry 4.0 mendorong transformasi besar dalam sistem manufaktur melalui integrasi teknologi digital dan kecerdasan buatan. Machine Learning (ML) menjadi salah satu elemen kunci dalam otomasi industri…
tri karena kemampuannya memproses data secara adaptif untuk mendukung pengambilan keputusan otonom. Penelitian ini bertujuan untuk meninjau secara sistematis penerapan Machine Learning dalam sistem manufaktur otomatis dengan menyoroti tren, tantangan, serta arah penelitian masa depan. Metode yang digunakan adalah Systematic Literature Review (SLR) terhadap 30 artikel jurnal internasional terindeks Scopus, IEEE Xplore, dan ScienceDirect dalam rentang tahun 2020–2025. Hasil studi menunjukkan bahwa Machine Learning banyak diterapkan pada empat domain utama: predictive maintenance, process optimization, quality control, serta intelligent automation. Algoritma yang dominan digunakan meliputi supervised learning dan deep reinforcement learning, yang terbukti mampu meningkatkan efisiensi operasional hingga 35%. Namun demikian, isu terkait interpretabilitas model, keterbatasan data industri, serta integrasi manusia–AI masih menjadi tantangan utama. Studi ini mengusulkan kerangka konseptual AI–Driven Manufacturing Framework sebagai panduan pengembangan sistem manufaktur cerdas yang adaptif, transparan, dan berkelanjutan menuju era Industry 5.0.
Abstract:The Malacca Strait is a vital maritime route that faces significant challenges from high traffic volumes and increasingly frequent extreme weather scenarios. These conditions pose serious risks to navigational safety, environmental…
vironmental integrity, and maritime operational security. This study aims to analyse the needs and components of a robust maritime surveillance architecture in the Malacca Strait, particularly in dealing with the impacts of extreme weather and climate change. Additionally, this research will explore the role of geospatial maritime intelligence in enhancing situational awareness and response to maritime incidents. The study findings indicate that a resilient surveillance architecture in the Malacca Strait requires the integration of advanced sensor systems (including multi-spectral radar and meteorological/oceanographic sensors), redundant communication networks, and AI/ML-based intelligent data processing. The adaptive capabilities of the system, including the use of UAVs and USVs, are critical to maintaining operational effectiveness in adverse weather conditions. Additionally, international cooperation and a robust policy framework, encompassing climate change adaptation and ethical considerations, are fundamental to the successful implementation and sustainability of surveillance systems. The Malacca Strait case study highlights specific vulnerabilities and proposes concrete solutions, illustrating a shift from a reactive to a proactive paradigm in maritime security. Building a resilient surveillance architecture in the Malacca Strait is a strategic imperative to safeguard global trade, regional stability, and environmental sustainability amid increasing climate uncertainty. Integrating cutting-edge technology with robust policies and international collaboration will ensure the safety and security of this vital waterway, making it a model for resilient maritime governance globally. A new aspect emphasised is the importance of adapting to climate change and the role of AI in predictive decision-making. This article presents a comprehensive and integrated approach to building a resilient maritime surveillance architecture, with a particular focus on the challenges of extreme weather in the Malacca Strait. Its originality lies in its emphasis on multi-sensor data fusion, the role of artificial intelligence in predictive analytics, and the integration of climate change adaptation into surveillance system design. Additionally, the article underscores the importance of a robust policy framework and international cooperation as the cornerstones of maritime surveillance resilience, offering a blueprint applicable to other critical maritime routes worldwide