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All Journal INTERNATIONAL JOURNAL OF MECHANICAL COMPUTATIONAL AND MANUFACTURING RESEARCH West Science Journal Economic and Entrepreneurship Vertex Acta Psychologia
Herjuna, Silvester Adi Surya
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Religion Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Mechanical Engineering Public Health

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Electricity demand forecasting in Ambon using machine learning techniques Herjuna, Silvester Adi Surya; Budisusila, Eka Nuryanto; Haddin, Muhammad
International Journal of Mechanical Computational and Manufacturing Research Vol. 13 No. 2 (2024): August: Mechanical Computational And Manufacturing Research
Publisher : Trigin Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/computational.v13i2.179

Abstract

This study aims to analyze the impact of electrical load forecasting using Artificial Neural Networks (ANN) to improve power supply reliability and efficiency in Ambon’s electric system. The objective is to develop a reliable forecasting model that supports effective energy management, helping to achieve operational excellence in terms of quality, safety, and cost-efficiency. A quantitative approach was utilized, gathering historical electricity load data from 2019 to 2024, alongside relevant environmental and temporal factors. The data were analyzed using ANN within a Python-based framework to predict future electricity demands accurately. The study employs a structured equation modeling to validate the forecasting model and its components. The findings reveal that the ANN model effectively predicts electrical loads with high accuracy, demonstrating substantial improvements in operational efficiency and energy cost reductions. The model’s ability to incorporate multiple input variables allows for nuanced understanding and prediction of load variations, thereby facilitating better resource allocation and strategic planning. This research contributes uniquely by applying ANN for electrical load forecasting in the context of Ambon’s electrical system, underscoring the integration of AI techniques in improving the operational efficiency of power utilities. The study extends the knowledge on the application of machine learning in the power sector by demonstrating how sophisticated forecasting models can significantly enhance energy management strategies
Performance Analysis of Wet Gas Flow in Up and Down Transmission Pipelines Herjuna, Silvester Adi Surya; Labriet, Andrieu; Akbar, Bima; Dimara, Johannes; Kaisiepo, Frans
Vertex Vol. 13 No. 2 (2024): June: Nuclear
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/apjc7f13

Abstract

This research examines the performance of wet gas flow in up and down transmission pipelines, addressing the critical challenges and dynamics associated with fluid behavior in varying orientations. Through extensive empirical analysis and modeling, the study identifies key factors influencing slugging, liquid accumulation, and flow efficiency. Findings reveal that upward transmission systems are particularly prone to slugging, leading to operational instability and increased energy demands, while downward flow systems benefit from gravitational assistance, resulting in enhanced reliability and reduced maintenance needs. Additionally, effective liquid management strategies and advanced monitoring technologies are essential for mitigating adverse effects and optimizing system performance. The research also addresses gaps in existing literature by providing new insights into flow management and environmental considerations, offering practical recommendations for pipeline design and operation. Ultimately, this study underscores the importance of ongoing research to refine understanding and improve practices in the transport of wet gas, contributing to the development of sustainable energy solutions.
The Influence of Startups in Increasing Entrepreneurship Awareness and Digital Literacy Sabil, Sabil; Herjuna, Silvester Adi Surya; Hakim, Lukman; Suharmono, Soni; Utami , Eva Yuniarti
West Science Journal Economic and Entrepreneurship Vol. 1 No. 09 (2023): West Science Journal Economic and Entrepreneurship
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsjee.v1i09.207

Abstract

This research explores the influence of startups on raising entrepreneurship awareness and enhancing digital literacy in West Java. A mixed-methods approach was employed, combining quantitative surveys with qualitative in-depth interviews and focus group discussions. The study involved 300 respondents with diverse demographic characteristics, allowing for a comprehensive analysis of the startup ecosystem's impact. Quantitative findings revealed that a significant portion of the population in West Java has high levels of entrepreneurship awareness (40%) and digital literacy (35%). Moreover, 60% of respondents reported engagement with startups. Qualitative insights from interviews and focus group discussions emphasized the inspirational role of startups, their contribution to skill development, and the potential for collaboration between startups and educational institutions. The study also found that 75% of respondents perceived a positive influence of startups on entrepreneurship awareness and 70% on digital literacy. These findings underscore the critical role of startups in fostering entrepreneurship awareness and digital literacy in West Java. However, challenges such as resource limitations and the digital divide must be addressed to fully harness the benefits of startups.
Impact of Training and Development Programs on Employee Performance in the Manufacturing Sector Herjuna, Silvester Adi Surya; Marhaeni, Vindy Fitriawaty; Alvira, Mila; Putri, Faiqah; Anastasya, Fiona
Acta Psychologia Vol. 3 No. 3 (2024): Oktober: Psychology and Health
Publisher : PELNUS | Pen en Light for Natural Union of Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/psychologia.v3i3.68

Abstract

This research examines the impact of training and development programs on employee performance within the manufacturing sector. By exploring the relationship between targeted training initiatives and various performance outcomes, such as productivity, job satisfaction, and safety, the study highlights the critical role of well-structured training in enhancing employee capabilities. The research also emphasizes the importance of motivation, organizational support, and customized training to address the specific needs of employees in different roles. Using a combination of qualitative and quantitative methods, including surveys and performance data analysis, the findings suggest that effective training programs lead to improved performance and higher employee retention. The study provides practical implications for manufacturing companies, urging them to prioritize training as a strategic investment and to cultivate a continuous learning culture that aligns with both organizational goals and employee development. This research contributes to the broader understanding of how training programs can be optimized to boost employee performance and organizational success in the manufacturing industry.
Co-Authors Akbar, Bima Alvira, Mila Anastasya, Fiona Dimara, Johannes Eka Nuryanto Budi Susila Kaisiepo, Frans Labriet, Andrieu Lukman Hakim Marhaeni, Vindy Fitriawaty Muhamad Haddin Putri, Faiqah sabil sabil, sabil Suharmono, Soni Utami , Eva Yuniarti