ANALYSIS OF TAKEOFF BEHAVIOR OF A320 AND B738 AIRCRAFT AT SULTAN HASANUDDIN INTERNATIONAL AIRPORT BASED ON UNSUPERVISED LEARNING DOI Creative Commons
Rossi Passarella, Huda Ubaya, Sutarno Sutarno

и другие.

Опубликована: Дек. 20, 2024

This research aims to enhance aviation safety in Indonesia by examining the impact of takeoff speed on flight incidents. Specifically, we investigate relationship between abnormal speeds and runway exit or other accident risks for A320 B738 aircraft at Sultan Hasanuddin International Airport. Employing a quantitative design, analyzed dataset 4,550 flights over 91 days. Due data quality constraints, only 14% (628 flights) was suitable analysis. The further divided into three classes using elbow method identify patterns speeds. These included low, medium, high speeds, allowing us assess correlation each category incidence exits accidents. Preliminary findings suggest that with are significantly associated increased risks, highlighting need improved monitoring intervention strategies airport. Our will contribute better understanding factors influencing safety, particularly relation By identifying potential developing targeted interventions, this can help improve standards Indonesia. In addition, collaboration airlines, regulatory bodies, airport authorities be essential implementing these effectively. Future studies may also explore weather conditions pilot training performance measures sector.

Язык: Английский

Meta -model-based optimization of rule-based energy management in second-hand plug-in hybrid electric vehicles DOI Creative Commons
Debraj Bhattacharjee, Sourabh Mandol, Tamal Ghosh

и другие.

Data Science and Management, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Anomaly Detection in Commercial Aircraft Landing at SSK II Airport using Clustering Method DOI Creative Commons
Rossi Passarella,

Taswiyah Marsyah Noor,

Osvari Arsalan

и другие.

Aerospace traffic and safety., Год журнала: 2024, Номер unknown

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

1
Classification Models for Assessing the Severity of Marine Accidents Based on Machine Learning DOI Creative Commons
Rossi Passarella,

Arinda I. Safitri,

Nyayu Latifah Husni

и другие.

International Journal of Safety and Security Engineering, Год журнала: 2024, Номер 14(4), С. 1213 - 1221

Опубликована: Авг. 30, 2024

Marine transport is still famous and claimed to be part of human civilization, but in practice, marine vessels experience accidents quite frequently, which can result large losses.Therefore, this research aims integrate multiple data sources on accidents, classify them identify patterns, create a model forecast prevent future accidents.The first step the methodology connect several variables from generate target variables.We then feed ready set into 10 machine learning algorithms determine one best suit type quality.The training results provided four with performance, namely label spreading, propagation, random forest, XGB classifier algorithms.After comparing testing results, we found that performed slightly better than other three models, where developed dataset only had performance 70%-74% predicting corresponding class.

Язык: Английский

Процитировано

0
ANALYSIS OF TAKEOFF BEHAVIOR OF A320 AND B738 AIRCRAFT AT SULTAN HASANUDDIN INTERNATIONAL AIRPORT BASED ON UNSUPERVISED LEARNING DOI Creative Commons
Rossi Passarella, Huda Ubaya, Sutarno Sutarno

и другие.

Опубликована: Дек. 20, 2024

This research aims to enhance aviation safety in Indonesia by examining the impact of takeoff speed on flight incidents. Specifically, we investigate relationship between abnormal speeds and runway exit or other accident risks for A320 B738 aircraft at Sultan Hasanuddin International Airport. Employing a quantitative design, analyzed dataset 4,550 flights over 91 days. Due data quality constraints, only 14% (628 flights) was suitable analysis. The further divided into three classes using elbow method identify patterns speeds. These included low, medium, high speeds, allowing us assess correlation each category incidence exits accidents. Preliminary findings suggest that with are significantly associated increased risks, highlighting need improved monitoring intervention strategies airport. Our will contribute better understanding factors influencing safety, particularly relation By identifying potential developing targeted interventions, this can help improve standards Indonesia. In addition, collaboration airlines, regulatory bodies, airport authorities be essential implementing these effectively. Future studies may also explore weather conditions pilot training performance measures sector.

Язык: Английский

Процитировано

0