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All Journal Journal of Industrial Engineering & Management Research (JIEMAR) Journal of World Future Medicine, Health and Nursing
Budi Darmawan
Politeknik Angkatan Laut
Humanities Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering

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Analisis Pemilihan Pesawat Terbang Tanpa Awak Dalam Mendukung Operasi Keamanan Di Laut Marwanto Marwanto; Budi Darmawan; Manahan Budianto Pandjaitan
Journal of Industrial Engineering & Management Research Vol. 4 No. 4 (2023): August 2023
Publisher : AGUSPATI Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.7777/jiemar.v4i4.483

Abstract

early detection is a crucial element in maintaining maritime security and preventing various threats at sea. In the context of maritime security operations, the use of unmanned aerial vehicles (UAVs) as a means of supporting early detection has attracted attention due to its ability to carry out extensive and real-time monitoring. However, selecting the right drone requires a systematic and objective approach. Therefore, this study aims to analyze the selection of unmanned aircraft to increase early detection in order to support security operations at sea using the Analytical Hierarchy Process (AHP) method. The AHP method is used to overcome complexity in the decision-making process by taking into account various relevant criteria and sub-criteria. Factors considered include the aircraft's technical capabilities, such as operational range, endurance and speed, as well as the sensor capabilities of the aircraft, including optical, infrared and radar cameras. Security and system reliability aspects are also a major consideration, including communication systems that can ensure fast and secure data transmission. In this study, the AHP-based assessment involved the participation of experts and stakeholders related to security operations at sea. They provide preferences and relative weights of each criterion to determine the priority and importance of each factor in the selection of unmanned aircraft. The results of the analysis show that the selection of suitable unmanned aircraft can increase the efficiency and effectiveness of security operations at sea with an emphasis on early detection. Unmanned aircraft that have superior technical capabilities, sophisticated sensors, and reliable communication systems are a priority in supporting security operations at sea
INTEGRATING ARTIFICIAL INTELLIGENCE–ASSISTED CLINICAL DECISION SUPPORT IN NURSING PRACTICE: IMPACTS ON PATIENT SAFETY AND CARE QUALITY IN SMART HEALTHCARE SYSTEMS Binamin Binamin; Budi Darmawan; Dewadharu Achsyan; Ahmad Faisol; Ahmed Al Kadhim
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i2.3622

Abstract

Artificial intelligence–assisted clinical decision support systems are increasingly integrated into smart healthcare environments, offering new opportunities to enhance patient safety and care quality while transforming nursing practice. Persistent challenges such as medical errors, delayed clinical responses, and variability in care highlight the need for effective decision support tools. This study aims to examine the impact of artificial intelligence-assisted systems on patient safety, care quality, and nursing decision-making processes. A mixed-methods design was employed, combining quantitative analysis of clinical indicators with qualitative insights from nurses across multiple hospital units. Data were collected from 120 nurses and corresponding patient records before and after system implementation, supported by surveys and interviews. Findings reveal significant reductions in medication errors and adverse events, alongside improvements in response time, care quality, and nurse decision confidence. Inferential analysis confirms that system usability and training significantly influence outcomes, while experience level moderates system effectiveness. The study concludes that artificial intelligence–assisted decision support enhances clinical performance by complementing nursing expertise and enabling data-driven decision-making. Effective integration depends on user readiness, organizational support, and alignment with clinical workflows, highlighting the need for human-centered implementation strategies in smart healthcare systems.
Co-Authors Ahmad Faisol Ahmed Al Kadhim Binamin Binamin Dewadharu Achsyan Manahan Budianto Pandjaitan Marwanto Marwanto