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All Journal Mechatronics, Electrical Power, and Vehicular Technology Wave: Jurnal Ilmiah Teknologi Maritim Jurnal Riset dan Rekayasa Kelautan Automotive Experiences
Waskito, Dwitya Harits
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Environmental Science Materials Science & Nanotechnology Mechanical Engineering Physics

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DEVELOPMENT OF ANALYTICAL STRUCTURAL MODEL FOR COMPOSITE GRILLAGE Waskito, Dwitya Harits
Wave: Jurnal Ilmiah Teknologi Maritim Vol 14, No 1 (2020)
Publisher : Badan Pengkajian dan Penerapan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (292.555 KB) | DOI: 10.29122/jurnalwave.v14i1.4136

Abstract

With the high demand and market growth for composite boats, there is a competition to design a high-quality ship which leads to thinner boat?s hull structure. To meet this objective, a highly accurate and computationally fast calculation for composite structure is needed. In ship's structure the most used configuration of the stiffeners is by using grillage. However, most of the theory of the grillage is designated for steel structure. There is a gap between steel and composites for grillage theory which will be modified especially for the deflection. There are two general methods that used in this analysis, Navier Grillage theory as the analytical method and Finite Element Analysis as the benchmark tools. To develop analytical method of composite grillages, the current method were investigated by comparing the analytical results and FEA. Two composites with high difference in Young Modulus were analyzed and the results shows that there are significant difference of results with two previous method. The deflection results of two method were analyzed with every composite elastic properties of E-Glass. Empirical equation were developed from the relation between deflection graph of two methods and composite to increase the accuracy of Navier Grillage theory for E-Glass composites grillage.
ESTIMASI PERFORMA SISTEM PROPULSI PADA KAPAL DENGAN TIPE CONTROLLABLE PITCH PROPELLER Waskito, Dwitya Harits
Oseanika Vol. 1 No. 1 (2020): Oseanika: Jurnal Riset dan Rekayasa Kelautan - Juni 2020
Publisher : Laboratory for Marine Survey Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29122/oseanika.v1i1.4050

Abstract

Perhitungan performa sistem propulsi sebuah kapal secara real-time sangat penting untuk dapat mengukur kualitas dan efisiensi dari komponen yang ada pada sistem propulsi itu sendiri. Metode yang telah dikembangkan berpusat kepada perhitungan pada kapal dengan tipe propeller Fixed Pitch Propeller, sedangkan untuk kapal dengan tipe Controllable Pitch Propeller metode tersebut kurang tepat jika digunakan karena perbedaan pitch pada setiap kecepatan. Oleh karena itu dibutuhkan suatu metode perhitungan analitik yang dapat digunakan secara efisien, mudah, akurat, dan dapat dilakukan pada saat kapal berlayar. Metode yang digunakan adalah mendapatkan data dari pitch pada propeller pada masing – masing kecepatan dimana dari data pitch tersebut dapat digunakan untuk mendapatkan nilai estimasi performa sistem propulsi dengan menggunakan metode engine- propeller matching. Metode perhitungan yang dilakukan pada tulisan ini dapat juga digunakan sebagai pembanding terhadap kondisi sistem propulsi kapal terkini dengan kondisi propulsi kapal pada saat commissioning. Kata Kunci: propulsi; Controllable Pitch Propeller;  pitch;  propeller; kapal riset
Enhancing Brake System Evaluation in Periodic Testing of Goods Transport Vehicles through FTA-FMEA Risk Analysis Ansori, Irfan; Waskito, Dwitya Harits; Mutharuddin, Mutharuddin; Irawati, Novi; Nugroho, Sinung; Mardiana, Tetty Sulastri; Subaryata, Subaryata; Siregar, Nurul Aldha Mauliddina
Automotive Experiences Vol 6 No 2 (2023)
Publisher : Automotive Laboratory of Universitas Muhammadiyah Magelang in collaboration with Association of Indonesian Vocational Educators (AIVE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.8394

Abstract

Failure of the braking system is one of the factors causing traffic accidents, therefore periodic testing of goods transport vehicles is very important. In fact, the incidence rate is still very high despite routine testing. Standard Operating Procedures (SOP) for periodic testing must be updated to reduce the risk of possible accidents. Therefore, procedures for updating the SOP for periodic brake system testing are presented in this article. The Fault Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA) methods were applied based on accident investigation data from the National Transportation Safety Committee (NTSC) from 2017 to 2022. FTA is used for risk identification, while FMEA is used for risk analysis to find the highest-risk failure cases. The results of our analysis showed that 13 failure cases were classified as intolerable so additional SOPs were required for each case. Finally, the results of this study provide new insights for stakeholders to revise the rules regarding periodic vehicle testing.
The Road Safety: Utilising Machine Learning Approach for Predicting Fatality in Toll Road Accidents Mutharuddin, Mutharuddin; Rosyidi, M.; Karmiadji, Djoko Wahyu; Fitri, Hastiya Annisa; Irawati, Novi; Waskito, Dwitya Harits; Mardiana, Tetty Sulastry; Subaryata, Subaryata; Nugroho, Sinung
Automotive Experiences Vol 7 No 2 (2024)
Publisher : Automotive Laboratory of Universitas Muhammadiyah Magelang in collaboration with Association of Indonesian Vocational Educators (AIVE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.11082

Abstract

Road safety is one of the critical government transportation concerns, especially on the toll roads. With the increasing number of toll roads as part of infrastructure planning, road traffic accidents are significantly escalating. Developing a system that predicts accidents on toll roads will benefit to reduce the harm that is caused by traffic accidents. This study will propose a method for analysing toll road accidents in Indonesia using historical toll road accident data as a dataset to become a pattern to examine the frequency of accidents. This dataset consists of various parameters from three main factors that cause accidents: human, environmental, and road infrastructure factors. Machine learning technique will be mainly used to determine the most influencing factors by employing classifiers such as Logistic Regression (LR), Decision Tree (DT), Gaussian Naïve Bayes (GNB), and K-Nearest Neighbors (KNN) can construct the prediction model. Fourteen subfactors from the data were used to predict the future fatalities caused by accidents, which allowed the system to forecast the accident fatality. The results show accuracy performance on the test set with LR, DT, KNN, and GNB models, 85.3%, 79.4%, 87.1%, and 77.1%, respectively. The KNN Classifier model has the most minor error value of 0.6 compared to the other models. The study’s findings will help analyse the causal factors involved in toll road accidents and could be utilised by road authorities to employ risk control options to mitigate the ramifications.
The influence of battery-powered engine on the reduction of carbon dioxide production from fishing boats Octaviani, Nilam Sari; Waskito, Dwitya Harits; Iskendar, Iskendar; Muis, Abdul; Fuadi, Noor Muhammad Ridha; Muhajirin, Muhajirin; Palebangan, Hendra; Ismoyo, Kunto; Kartikasari, Dewi; Gutami, Nanda Itohasi; Ajidarmo, Kusno
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 14, No 2 (2023)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2023.v14.208-214

Abstract

Several technologies are currently being applied in the maritime industry to reduce greenhouse gas (GHG) emissions. An example is the implementation of an electric propulsion system with a battery charged using a renewable energy source. Meanwhile, it is important to analyze the energy demand and the quantity of emissions reduced in a vessel after installing this system. Therefore, this study focused on analyzing the energy demand and emissions produced on fishing boats, specifically the “Sandeq” fishing boats in West Sulawesi. The primary objective was to quantify the carbon dioxide emissions reduced after the conventional engine of the vessel was replaced with an electric propulsion system. Moreover, the energy demand of the boat was estimated by analyzing the daily speed, length of voyage, and engine capacity. The results showed that six batteries were required to provide the power needed for daily operation. Furthermore, the electric propulsion system was able to reduce CO2 emission by 7.94 tons annually per ship, leading to the reduction of fuel consumption and emission taxes to approximately 10 million Rupiah annually. These results were expected to encourage stakeholders to promote the transition from conventional diesel engines to electric-powered engines.
A Systematic Literature Review of Risk Assessment Methodologies for Battery Electric Vehicles Gusti, Ayudhia Pangestu; Waskito, Dwitya Harits; Kaleg, Sunarto; Bowo, Ludfi Pratiwi; Pratama, Angjuang; Maulani, Defi Rizki; Varadita, Ayumi Putri; Nugroho, Sinung; Wiguna, I Kadek Candra Parmana
Automotive Experiences Vol 8 No 1 (2025)
Publisher : Automotive Laboratory of Universitas Muhammadiyah Magelang in collaboration with Association of Indonesian Vocational Educators (AIVE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.12835

Abstract

This systematic literature review investigates risk assessment methodologies for Battery Electric Vehicles (BEVs), highlighting their diversity and effectiveness in addressing emerging safety challenges. With the rapid global adoption of BEVs, there is an increasing need for robust methodologies to assess risks such as thermal runaway (TR), degradation, and operational failures. This review highlights techniques such as fuzzy failure mode and effect analysis (FMEA), hybrid neural networks, bayesian networks (BN), and entropy weight methods. These tools effectively identify and mitigate risks; however, they face challenges in providing holistic, system-level safety assessments and adapting to long-term, real-world conditions. Unlike previous works, this study integrates interdependent BEV subsystems into unified risk models and examines underexplored areas such as maritime transport safety. The transport of BEVs by vessels presents unique risks, including high humidity and confined cargo spaces, which intensify the battery safety challenges. Tools like FMEA and real-time monitoring systems are critical to mitigate these risks. The findings highlight the growing reliance on real-time diagnostics and advanced algorithms for enhancing BEV safety and reliability. By identifying gaps and proposing recommendations, this review aims to support the development of standardized frameworks to ensure BEV safety across various environments and operational scenarios, contributing to their continued global adoption.
Co-Authors AA Sudharmawan, AA Abdul Muis Ajidarmo, Kusno Ansori, Irfan Ayudhia Pangestu Gusti Dewi Kartikasari Fitri, Hastiya Annisa Fuadi, Noor Muhammad Ridha Gutami, Nanda Itohasi Iskendar Iskendar Ismoyo, Kunto Karmiadji, Djoko Wahyu Ludfi Pratiwi Bowo Mardiana, Tetty Sulastri Mardiana, Tetty Sulastry Maulani, Defi Rizki Mutharuddin, Mutharuddin Novi Irawati Nugroho, Sinung Octaviani, Nilam Sari Palebangan, Hendra Pratama, Angjuang Rosyidi, M. Siregar, Nurul Aldha Mauliddina Subaryata, Subaryata Sunarto Kaleg Varadita, Ayumi Putri Wiguna, I Kadek Candra Parmana