Industry 4.0 has driven significant transformation in manufacturing maintenance strategies, particularly in the implementation of Total Productive Maintenance (TPM). This research examines the paradigm shift from conventional TPM methods to Artificial Intelligence (AI)-based approaches, utilizing a qualitative methodology with phenomenological design and participatory observation in manufacturing environments. Results indicate that integrating AI with TPM has transformed maintenance approaches from reactive to predictive, enabling real-time anomaly detection, predictive maintenance, and maintenance schedule optimization that significantly improve Overall Equipment Effectiveness (OEE). Despite providing benefits such as reduced downtime and maintenance costs, AI-based TPM implementation faces challenges including data fragmentation, skilled human resource requirements, and organizational culture resistance. The development of collaboration between industry and academic institutions, as well as investment in edge computing infrastructure, are identified as key to maximizing the potential of AI-based TPM in enhancing operational sustainability in manufacturing.

Digital technological advancements have driven a paradigm shift towards Industry 4.0, characterized by the integration of cyber-physical systems and artificial intelligence in manufacturing processes. This study analyzes the role of Internet of Things (IoT) in accelerating Manufacturing 4.0 transformation, focusing on production efficiency optimization. Through a systematic literature review of 45 Scopus-indexed journals published between 2019-2024, complemented by experimental analysis on a laboratory-scale smart factory prototype, this research examines the implementation of IoT-based technologies such as predictive maintenance and digital twins. Results demonstrate that IoT integration significantly enhances production efficiency through real-time monitoring (30% downtime reduction), predictive maintenance (20% maintenance cost decrease), and data-driven supply chain optimization (15% energy savings). Case studies from Siemens and PT. Unilever Indonesia confirm that IoT sensor integration with ERP systems improves demand forecasting accuracy by 25% and reduces production errors to 0.001%. Despite challenges including high initial infrastructure costs and cybersecurity vulnerabilities, IoT implementation offers sustainable competitive advantages through enhanced operational efficiency, quality control, and resource management.
 

The English language training carried out at the educational tourism Subak TeBA Majalangu aims to improve the English language skills of tour guides so they can communicate with foreign tourists. Tour guides are given English language training according to their field with the material provided totaling 4 discussion topics. On each topic, there are 2 training sessions, namely theory and practice in the field. There are 3 stages in this training, namely the preparation stage, socialization, and training implementation. The training was carried out over 8 meetings. At the socialization stage, participants are given an initial test to determine the participant's competency. Furthermore, at the end of the training implementation stage, participants are again given a test to determine the results of the training participants' English language skills. The results obtained from implementing English language training for tour guides at the educational tourism Subak TeBA Majalangu in Kesiman Kertalangu Village, Denpasar were very satisfying. Training participants get an increase in grades that were previously lacking, after the training they get good grades. For the sustainability of this program, the community service team hopes to be able to provide other training in developing human resources at TeBA Majalangu