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Ika Sartika
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INDONESIA
Jurnal Penelitian Samudra
Published by Samudra Publisher
ISSN : -     EISSN : 30317258     DOI : -
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Education Library & Information Science Other
Samudra adalah Jurnal kajian Multidisiplin (Journal of Multidisciplinary Studies). Penerbitan jurnal ini bertujuan untuk menyediakan sarana komunikasi dan publikasi ilmiah bagi peneliti, mahasiswa dan dosen, yang mempunyai komitmen terhadap pengembangan ilmu pengetahuan dan teknologi.
Arjuna Subject : Umum - Umum
Articles 44 Documents
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Digital Claim Readiness as a Mediating Factor Between Marine Cargo Insurance Coverage and Compensation Recovery in Indonesian Maritime Logistics Veronika Saragih
Jurnal Penelitian Samudra Vol. 4 No. 02 (2026): Article Research Juli Vol 4, No 2, 2026
Publisher : Jurnal Penelitian Samudra

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Abstract

The increasing complexity of global maritime logistics has intensified the importance of marine cargo insurance in mitigating financial losses arising from cargo damage, loss, and transportation delays. Despite the widespread adoption of marine cargo insurance, many cargo owners continue to experience difficulties in obtaining full compensation due to incomplete documentation, delayed claim submission, and limited digital integration among shipping stakeholders. Existing studies have primarily focused on legal liability and insurance coverage while paying limited attention to the operational readiness required to ensure successful compensation recovery. This study investigates the relationship between marine cargo insurance coverage and compensation recovery by introducing Digital Claim Readiness (DCR) as a mediating construct. Digital Claim Readiness refers to an organization's capability to prepare, verify, and submit insurance claims through integrated digital documentation, electronic cargo tracking, and real-time logistics information systems. A quantitative research design was employed using Structural Equation Modeling (SEM-PLS). Data were collected from freight forwarders, shipping companies, cargo owners, marine surveyors, and insurance companies operating in major Indonesian ports. The proposed conceptual model evaluates the influence of insurance coverage quality, digital documentation systems, claim readiness, and insurer responsiveness on compensation recovery performance. The study proposes that Digital Claim Readiness significantly mediates the relationship between insurance protection and compensation effectiveness. Organizations implementing integrated digital claim management systems are expected to achieve faster claim settlement, lower dispute rates, and higher compensation recovery ratios than organizations relying on conventional documentation procedures. This research contributes to maritime insurance literature by introducing Digital Claim Readiness as a novel operational capability within marine cargo insurance management. The findings are expected to support the development of digital insurance ecosystems that improve transparency, operational efficiency, and risk management across maritime logistics.
Development of an Artificial Intelligence-Based Decision Support System for Engine Room Watchkeeping Using Real-Time Machinery Parameters Hendri Indra; Jasman W.P Saragih; M. Nur
Jurnal Penelitian Samudra Vol. 4 No. 02 (2026): Article Research Juli Vol 4, No 2, 2026
Publisher : Jurnal Penelitian Samudra

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Abstract

Engine room watchkeeping requires continuous monitoring and rapid decision-making to ensure machinery reliability and ship safety. Conventional watchkeeping practices depend heavily on officers' experience and manual interpretation of machinery parameters, leading to delayed responses and increased risk of machinery failure. This study proposes an Artificial Intelligence-Based Decision Support System (AI-DSS) for engine room watchkeeping using real-time machinery parameters. The system integrates machine learning algorithms with operational data, including lubricating oil temperature, cooling water temperature, exhaust gas temperature, fuel consumption, and vibration signals. Data were collected from a marine engine simulator and ship operational records. Random Forest and Artificial Neural Network models were employed to predict machinery abnormalities and generate early warnings. The results indicate that the proposed system achieved a prediction accuracy of 94.6%, significantly improving anomaly detection compared to conventional monitoring methods. Furthermore, the developed Engine Room Watchkeeping Index (ERWI) provides a quantitative assessment of machinery conditions during watchkeeping operations. The proposed model can support marine engineers in making timely and accurate decisions, thereby improving operational safety and machinery reliability.
Development of a Physics-Informed Artificial Intelligence Framework for Real-Time Thermodynamic Performance Prediction and Energy Optimization of Marine Diesel Engines ika sartika; Yudi
Jurnal Penelitian Samudra Vol. 4 No. 02 (2026): Article Research Juli Vol 4, No 2, 2026
Publisher : Jurnal Penelitian Samudra

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Abstract

Marine diesel engines operate under highly dynamic thermodynamic conditions, requiring intelligent monitoring and energy optimization systems capable of providing accurate real-time decision support. Conventional Artificial Intelligence (AI) models have demonstrated strong predictive performance; however, they often neglect fundamental thermodynamic principles, resulting in limited physical consistency and reduced reliability under varying operating conditions. This study proposes a Physics-Informed Artificial Intelligence (PI-AI) framework that integrates the first and second laws of thermodynamics into a machine learning architecture to improve real-time thermodynamic performance prediction and optimize energy efficiency in marine diesel engines. The proposed framework utilizes Internet of Things (IoT)-based sensor data, including cylinder pressure, engine speed, fuel flow rate, intake air pressure, intake air temperature, exhaust gas temperature, cooling water temperature, and lubricating oil temperature. These data are processed using a Physics-Informed Neural Network (PINN) integrated with a Digital Twin model to represent the engine's dynamic behavior, while a Deep Reinforcement Learning (DRL) algorithm continuously determines optimal operating strategies for maximizing thermal efficiency and minimizing fuel consumption within safe operational constraints. Model performance is evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The effectiveness of the optimization framework is assessed through improvements in thermal efficiency, reductions in Brake Specific Fuel Consumption (BSFC), and decreases in entropy generation. The proposed framework is expected to provide more accurate, robust, and physically consistent thermodynamic predictions than conventional data-driven AI models while enabling adaptive real-time energy optimization. The integration of Physics-Informed AI, Digital Twin technology, and Deep Reinforcement Learning constitutes the primary novelty of this study, offering a comprehensive intelligent framework for predictive monitoring, energy-efficient engine operation, and emission reduction in next-generation smart maritime transportation systems.
Developing Mobile Learning Media as an Interactive Learning Innovation in Qur'anic Kindergarten Education Muhammad Fauzi; Ahir Yugo Nugroho Harahap
Jurnal Penelitian Samudra Vol. 4 No. 02 (2026): Article Research Juli Vol 4, No 2, 2026
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Abstract

The rapid advancement of digital technology has transformed educational practices, including early childhood Islamic education. However, learning activities in many Qur'anic Kindergartens (TK Al-Qur'an) continue to rely on conventional teaching methods and printed learning materials, resulting in limited student engagement and interactive learning experiences. This study aims to develop a mobile learning media as an interactive learning innovation to improve learning engagement and learning achievement among children in Qur'anic Kindergarten education. The research employed a Research and Development (R&D) approach using the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) instructional design model. The developed application integrates interactive multimedia features, including animated Arabic letters (Hijaiyah), audio recitation (Murottal), educational games, formative quizzes, progress tracking, and visual storytelling designed according to early childhood learning characteristics. The product was validated by experts in instructional media, early childhood education, and Islamic education before being implemented with 60 kindergarten students and six teachers at TK Al-Qur'an Taqarub. Data were collected through expert validation questionnaires, the System Usability Scale (SUS), classroom observations, learning engagement questionnaires, and pretest-posttest assessments. Descriptive statistics, normalized gain (N-Gain), paired-sample t-tests, and qualitative feedback analysis were employed to evaluate the effectiveness of the developed media. The findings indicate that the mobile learning media achieved a high level of content validity and usability, with an average expert validation score exceeding 90% and an SUS score categorized as excellent. Students demonstrated significantly higher learning achievement and engagement after using the application, with a moderate-to-high N-Gain score and statistically significant improvements (p < 0.05). Teachers also reported increased classroom interaction, learner motivation, and efficiency in monitoring students' learning progress. The study concludes that mobile learning media provides an effective and innovative solution for enhancing interactive learning in Qur'anic Kindergarten education by combining Islamic learning content with child-centered digital pedagogy. The developed application contributes to the growing body of knowledge on mobile-assisted early childhood education and offers practical implications for integrating digital technology into Islamic educational institutions.

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