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All Journal International Journal of Marine Engineering Innovation and Research
Budianto, Budianto
Politeknik Perkapalan Negeri Surabaya
Automotive Engineering Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Transportation

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A Linear Regression Analysis Was Conducted To Determine The Principal Dimensions of A Prospective Tourist Ship Budianto, Budianto
International Journal of Marine Engineering Innovation and Research Vol 9, No 4 (2024)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25481479.v9i4.21931

Abstract

The objective of this research is to design a catamaran tourist ship that incorporates green technology. To achieve this, the main dimensions of the ship will be determined using the linear regression method, based on existing data from similar vessels. The empirical data from existing vessels serves as the foundation for the analysis and prediction, thereby enabling the identification of relationships and patterns between the key variables, including length, width, draft, displacement, and passenger capacity. The linear regression method offers several advantages, including simplicity, ease of interpretation, and efficiency in the use of resources. As a result, it is an effective tool for initial analysis before the application of more complex methods. The R2 value has an average result above 90 percent, so the data can be considered good and valid. The innovative aspect of the design of this tourist ship is the utilisation of data pertaining to existing vessels that have demonstrated optimal performance in their role as tourist ships. The use of existing data not only helps reduce the risk of errors in the design of new vessels but also ensures that the design is efficient and compliant with operational needs and safety regulations. This research confirms the importance of a data-driven approach in the design of environmentally friendly and efficient ships. In the context of ship design and operation, displacement represents a pivotal parameter in linear regression analysis. An understanding of the relationship between displacement and ship performance allows for more accurate predictions regarding speed, fuel consumption, stability, and draft. This allows for the design of ships that are more efficient, stable, and safe, and optimized under a variety of operational conditions.
Corrosion Detection on Ship Hull Using ROV Based on Convolutional Neural Network Widiarti, Yuning; Setiawan, Edy; Prasetiyo, Hendra Aldi; Budianto, Budianto; Sutrisno, Imam; Adianto, Adianto; Rahmat, Mohammad Basuki
International Journal of Marine Engineering Innovation and Research Vol 9, No 1 (2024)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25481479.v9i1.17235

Abstract

The Remotely Operated Underwater Vehicle (ROV) has several inspection functions. One of them is the inspection function for hull damage. The damage that often occurs in the hull is corrosion. The corrosion can cause a decrease in the strength of the hull plate, reduce the speed of the ship, and decrease the quality of the safety level of ships and passengers. This study aims to classify the level of corrosion intensity on ship hulls by implementing a Convolutional Neural Network (CNN). Identification is carried out on images taken by underwater cameras via a Remotely Operated Vehicle (ROV). The intensity of the area affected by corrosion is identified so that the level of corrosion intensity can be classified and it can be considered that the ship needs maintenance to prevent even greater losses due to corrosion. The dataset used is 240 image data divided into 3 classification categories: low, medium, and high corrosion intensity. The accuracy of the real-time testing of the CNN method on the dataset plate when conditions outside the water reached 91.1% and on the dataset plate when conditions underwater reached 86.6%. 
Co-Authors Adianto, Adianto Prasetiyo, Hendra Aldi Rahmat, Mohammad Basuki Setiawan, Edy Sutrisno, Imam Widiarti, Yuning