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All Journal Ilomata International Journal of Management Logistica : Journal of Logistic and Transportation
Quirina Ariantji Patrisia Mintje
Akademi Penerbang Indonesia Banyuwangi
Social Sciences Transportation

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Analysis of Passenger Satisfaction on Green Airport Blimbingsari International Service Untung Lestari Nur Wibowo Wibowo; Quirina Ariantji Patrisia Mintje; Sabam Dany Sulung; Ikhwanul Qiram
Ilomata International Journal of Management Vol 3 No 4 (2022): October 2022
Publisher : Yayasan Ilomata

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (433.545 KB) | DOI: 10.52728/ijjm.v3i4.609

Abstract

Since 2019, Blimbingsari Airport has been designated as an International Airport with a green airport concept. This airport has received the most prestigious award in The 2022 Aga Khan Award for Architecture. Several energy efficiencies and conservation program initiatives certainly have an impact on the service facilities provided. However, this program implies an increase in the number of passengers. This increase certainly requires attention to be studied further because it is related to the airport's ability to serve the needs of service users. This study was conducted to determine the service quality of Blimbingsari International Airport based on passenger perceptions. 100 respondent were randomly selected to be the research sample. This study uses 5 (five) service quality dimensions which include 7 service variables to measure airport service quality. Data collection was carried out in mid-June 2022 by distributing a Likert scale 5 questionnaire. Data analysis used the IPA and Servqual gap methods. Based on the results of data analysis, it can be seen that there are 7 (four) service variables that are important to determine the quality of airport terminal services, namely (1) the ease of passengers in obtaining information, (2) the reliability of airport facilities and equipment, (3) the coolness and comfort of the airport terminal. , and (4) a clean airport terminal room. The servqual gap shows that 5 (five) dimensions of service quality are positive. Based on these results, it can be concluded that the service quality of Blimbingsari International Airport is very satisfying for passengers.
Enhancing Driver Stress Detection through Multimodal Integration of Eye Tracking and Physiological Signals Tri Agung Widayat; Quirina Ariantji Patrisia Mintje; Sri Yanthy Yosepha
Logistica : Journal of Logistic and Transportation Vol. 3 No. 3 (2025): July 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/logistica.v3i3.1147

Abstract

Driver stress poses significant risks to traffic safety, impairing attention, decision-making, and reaction time. Traditional monitoring methods often lack sensitivity. This study proposes and validates a novel multimodal framework that integrates synchronized eye-tracking and physiological data to significantly enhance the sensitivity and real-time accuracy of driver stress detection, addressing limitations of earlier unimodal approaches. Thirty licensed drivers participated in simulated driving tasks under baseline and stress-induced conditions. Eye-tracking metrics (pupil diameter, fixation duration, blink rate) and physiological signals (heart rate, skin conductance, heart rate variability) were collected. Data were synchronized and analyzed using Linear Discriminant Analysis (LDA) and other machine learning models to classify stress conditions. Under stress, pupil dilation increased by 20%, blink rate rose by 35%, and gaze spread narrowed, indicating visual tunneling. Physiologically, heart rate increased by 17%, skin conductance by 31%, and HRV decreased by 19%. The combined multimodal model achieved 91.4% classification accuracy, outperforming unimodal approaches. These results align with previous research showing that multimodal systems provide more reliable stress detection by integrating visual and autonomic markers. The findings highlight the system’s potential for real-time applications in Driver Monitoring Systems (DMS). Multimodal integration of eye-tracking and physiological signals enhances the sensitivity and reliability of driver stress detection. This approach offers a foundation for intelligent, adaptive DMS capable of improving road safety. Future work should focus on real-world validation and ethical implementation strategies. These findings demonstrate that multimodal integration provides a more comprehensive understanding of driver stress through complementary visual and autonomic indicators. The proposed framework forms a foundation for intelligent, adaptive Driver Monitoring Systems (DMS) capable of real-time stress recognition and proactive safety intervention.
Co-Authors Ikhwanul Qiram Sabam Dany Sulung Tri Agung Widayat Untung Lestari Nur Wibowo Yosepha, Sri Yanthy