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The importance of providing safety training for ship crews based on STCW Dipta, I. W. Gede; Giyas, M.; Azmi, M. Aidil; Wanadi, Adil
JURNAL APLIKASI PELAYARAN DAN KEPELABUHANAN Vol 15 No 2 (2025): bulan Maret
Publisher : Universitas Hang Tuah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30649/japk.v15i2.157

Abstract

Maritime safety is a fundamental aspect of the maritime industry that heavily relies on the competence and preparedness of the crew in dealing with emergency situations. The International Convention on Standards of Training, Certification, and Watchkeeping for Seafarers (STCW) establishes a safety training framework that must be met by every crew member worldwide. This research aims to highlight the urgency of STCW-based safety training, evaluate the effectiveness of its implementation, and identify the challenges faced in its execution on the ground. The methods used include literature review and descriptive analysis of training standards as well as case studies from several maritime incidents. The study results show that STCW-based safety training significantly improves the readiness of crew members to handle emergencies, reduces the risk of accidents, and strengthens the safety culture onboard. However, there are obstacles in the implementation of the training, such as disparities in facilities, limitations in trainer resources, and differences in compliance levels between countries. Therefore, stricter supervision, improvement in training quality, and harmonization of implementation standards at the global level are required.
Penataan Penataan Lajur Antrian Kegiatan Pengawasan Penimbangan Angkutan Barang di UPPKB Singosari M. Adil Wanadi; Dandun Prakosa; Wisnu Wardana K; Farid Hanifan; Candy, Ade Irfan Efendi
JURAL RISET RUMPUN ILMU TEKNIK Vol. 4 No. 1 (2025): April : Jurnal Riset Rumpun Ilmu Teknik
Publisher : Pusat riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurritek.v4i1.5169

Abstract

This study discusses the arrangement of queue lanes in the supervision of weighing freight transportation at UPPKB Singosari to improve service efficiency. Based on the analysis of the Minimum Service Standards (SPM), three main indicators, namely compliance, conformity, and implementation, have reached 100%, while the accuracy indicator has only reached 50% due to the duration of weighing exceeding the set time limit. The main factor causing long queues is the lack of clear lane markings, making it difficult for vehicles to be directed without an officer. In addition, the parking area of 3,330.6 m² has not been utilized optimally. Improvement efforts include the implementation of queue lane markings to reduce dependence on officers, optimization of facilities such as Pos 2 which is parallel to Pos 1, and improvement of human resource management to suit operational needs. The use of Weight in Motion (WIM) technology is also recommended to reduce workload and accelerate the supervision of freight transport vehicles. By implementing this strategy, it is hoped that services at UPPKB Singosari can be more efficient, transparent, and in accordance with applicable standards.
Predicting willingness to pay for urban rail transit using machine learning : Evidence from jakarta MRT Kusuma, Wisnu Wardana; EFENDI, ADE IRFAN EFENDI; Prakosa, Dandun; Montanasyah, M. Popik Montanasyah; wanadi, adil; Rizal, Yus
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.18

Abstract

The development of urban transportation requires an efficient, reliable and sustainable system, so fare determination is an important factor in the success of the Jakarta MRT service. In this context, understanding the user's Willingness to Pay (WTP) is crucial because it is not only influenced by economic ability, but also perception and preference for services. This study aims to analyze and predict the WTP of MRT users by integrating transportation economics approaches and machine learning methods. The research data is in the form of primary data from a survey of 296 MRT users which includes socio-economic characteristics, transportation costs, frequency of use and Ability to Pay (ATP). The methodology used includes descriptive analysis and regression modeling using various algorithms, namely Linear Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Regression (SVR) and XGBoost. Model evaluation was carried out using MAE, RMSE and determination coefficient (R²). The results showed that the value of WTP was relatively homogeneous compared to variations in income and transportation costs, which indicated that willingness to pay was not entirely determined by economic ability. The performance of the model shows that no algorithm is consistently superior, with R² values that tend to be low. The feature importance analysis identified income, transportation costs and ATP as the main factors. This research contributes through the application of a multi-model machine learning framework and policy implications that MRT fare determination needs to consider economic aspects and user preferences in a balanced manner.