Application of The Fuel Estimation Simulator (The Bung Tomo Training Ship)
DOI:
https://doi.org/10.24036/eksakta/vol24-iss02/344Keywords:
Fuel Consumption, Simulator, Ship Operating CostAbstract
Developing new routes in the shipping business, the calculation uses the formula determined by the Minister of Transportation No. KM. 58 of 2003. However, this manual calculation takes a long time, and the possibility of human error is large. So, we need a more effective and efficient way to calculate the fuel needed to cover a certain distance. In this study, a simulator was developed that uses the spherical triangle concept to determine the distance and direction of the ship and then integrates it with the formula to get the results of the fuel calculation. From the results of trials using the Bung Tomo Training Ship, the results of calculations are faster and more accurate. The simulator positively affects cadets when applied in applied mathematics classes (85% through response questionnaire results and 87% competency test).
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Altosole, M., Campora, U., Figari, M., Laviola, M., & Martelli, M. (2019). A diesel engine modelling approach for ship propulsion real-time simulators. Journal of Marine Science and Engineering, 7(5).
Anderson, A., & Rezaie, B. (2019). Geothermal technology: Trends and potential role in a sustainable future. Applied Energy, 248(March), 18–34.
Bui-Duy, L., & Vu-Thi-Minh, N. (2021). Utilization of a deep learning-based fuel consumption model in choosing a liner shipping route for container ships in Asia. Asian Journal of Shipping and Logistics, 37(1), 1–11.
Cahyadi, T., Mirianto, A. D., Krisnawati, L., Malahayati, P. P., Surabaya, P. P., Islam, U., & Sunan, N. (2022). Implementation Of Simulator Learning Media On Container Shiploading Handling Planning For. 3(1), 14–20.
Damoyanto Purba, Novita Hindri Harini, Agus Dina Mirianto, Z. Z. (2019). Applying Spherical Triangle Concept in Simulator to Determine Distance and Direction of Ship. Proceedings of the Mathematics, Informatics, Science, and Education International Conference.
de la Peña Zarzuelo, I., Freire Soeane, M. J., & López Bermúdez, B. (2020). Industry 4.0 in the port and maritime industry: A literature review. Journal of Industrial Information Integration, 20, 100173.
Dechezleprêtre, A., Einiö, E., Martin, R., Nguyen, K.-T., & Reenen, J. Van. (2019). Do Tax Incentives For Research Increase Firm Innovation? An Rd Design For R&D. In Nber Working Paper SerieS (pp. 1–75). National Bureau Of Economic Research, Cambridge.
Fakultas, D., & Sembilanbelas, T.-U. (2021). Terhadap Perhitungan Tarif Kapal. 7(2), 1–10.
Ghaemi, M. H. (2021). Performance and Emission Modelling and Simulation of Marine Diesel Engines using Publicly Available Engine Data. Polish Maritime Research, 28(4), 63–87.
Gunarti, M. R., Harini, N. V., Suharsono, D. D., Purnomo, H., Waskito, K. L., Zuhri, Z., & Aryanti, D. E. (2021). Development of Fuel Calculation Applications Android Based for Ship Operations. Journal of Physics: Conference Series, 2117(1).
Jensen, S., Lützen, M., Mikkelsen, L. L., Rasmussen, H. B., Pedersen, P. V., & Schamby, P. (2018). Energy-efficient operational training in a ship bridge simulator. Journal of Cleaner Production, 171, 175–183.
Jia, S., Li, C. L., & Xu, Z. (2020). A simulation optimization method for deep-sea vessel berth planning and feeder arrival scheduling at a container port. Transportation Research Part B: Methodological, 142, 174–196.
Kesieme, U., Pazouki, K., Murphy, A., & Chrysanthou, A. (2019). Biofuel as an alternative shipping fuel: technological, environmental and economic assessment. Sustainable Energy and Fuels, 3(4), 899–909.
Le, L. T., Lee, G., Park, K. S., & Kim, H. (2020). Neural network-based fuel consumption estimation for container ships in Korea. Maritime Policy and Management, 47(5), 615–632.
Lee, R., Raison, N., Lau, W. Y., Aydin, A., Dasgupta, P., Ahmed, K., & Haldar, S. (2020). A systematic review of simulation-based training tools for technical and non-technical skills in ophthalmology. Eye (Basingstoke), 34(10), 1737–1759.
Ma, W., Lu, T., Ma, D., Wang, D., & Qu, F. (2021). Ship route and speed multi-objective optimization considering weather conditions and emission control area regulations. Maritime Policy and Management, 48(8), 1053–1068.
Rahman, A., Rokhmawati, R. I., & ... (2021). Evaluasi User Experience Pada Game PC Building Simulator Dengan Menggunakan Metode Game Experience Questionnaire (GEQ). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 5(1), 53–59.
Ricardianto, P., Nasution, S., Naiborhu, M. A., & Triantoro, W. (2020). Peluang dan Tantangan Sumber Daya Manusia dalam Penyelenggaraan Pelabuhan Cerdas (Smart Port) Nasional di Masa Revolusi Industri 4.0. Warta Penelitian Perhubungan, 32(1), 59–66.
Rochwulaningsih, Y., Sulistiyono, S. T., Masruroh, N. N., & Maulany, N. N. (2019). Marine policy basis of Indonesia as a maritime state: The importance of integrated economy. Marine Policy, 108(March 2016), 103602.
Santoso, Y. B., Permana, T., & Mubarok, I. (2019). Penggunaan Simulator Wiper Dan Washer Untuk Meningkatkan Pemahaman Kelistrikan Kendaraan Ringan Siswa Smk. Journal of Mechanical Engineering Education, 5(2), 267.
Song, R., Liu, Y., & Bucknall, R. (2019). Smoothed A* algorithm for practical unmanned surface vehicle path planning. Applied Ocean Research, 83(November 2018), 9–20.
Vol, X. (2022). Jurnal Al-Fikrah : Jurnal Manajemen Pendidikan. X(1), 10–19.
Widyaningsih, U., Sutoyo, Yuda, A. A. N. P., Mirianto, A., Zuhri, Z., & Harini, N. V. (2022). The Design of Ship Operation Cost Estimation Simulator Uses a Case Study of The Bung Tomo Trainer Ship. IOP Conference Series: Earth and Environmental Science, 1081(1), 012008.
Wiratno, D., Mirianto, A., Cahyadi, T., Zuhri, Z., & Harini, N. V. (2021). The design of the ship’s fuel estimation simulator uses a case study of the bung tomo trainer ship. IOP Conference Series: Materials Science and Engineering, 1010(1).
Zhou, S. K., Greenspan, H., Davatzikos, C., Duncan, J. S., Van Ginneken, B., Madabhushi, A., Prince, J. L., Rueckert, D., & Summers, R. M. (2021). A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies with Progress Highlights, and Future Promises. Proceedings of the IEEE, 109(5), 820–838.
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