Flight Ticket Research Articles

Deep-Learning-Powered GRU Model for Flight Ticket Fare Forecasting

Forecasting flight fares is a critical task in the rapidly expanding civil aviation industry and involves numerous factors. However, traditional airfare prediction systems are ineffective due to the complex and nonlinear relationships of multiple factors, which are not able to accurately account for the impact of different attributes such as time period. To tackle these issues, in this study, we proposed a novel approach that utilizes a deep-learning model, specifically, the Gated Recurrent Unit (GRU), by incorporating 44 decision features. The proposed model is able to capture the intricate relationships between various factors effectively and predict air ticket prices with high accuracy. In the experiments, it was found that the GRU model significantly outperforms not only classic machine learning models but also the MLP and LSTM in terms of assessment indicators of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The GRU model is thus promising concerning the fare prediction of flight tickets.

Analisis Pengawasan Otoritas Bandar Udara Wilayah V terhadap Kesesuaian Tarif Tiket Maskapai Lion Air di Bandar Udara Internasional Sultan Hasanuddin Makassar


 Flight Fare is the cost that must be borne by the passenger based on the components of the Distance Fare, Tax, Compulsory Insurance Contribution and Surcharge for using flight services. The mechanism for calculating the fare limit for domestic scheduled commercial transport economy class passengers has been regulated in Ministerial Regulation Number 20 of 2019 and flight fare restrictions have been regulated in Ministerial Decree No 106 of 2019 according to flight class. This research uses Qualitative Research. Data collection was obtained and collected at the Makassar Region V Airport Authority Office. Data obtained from observations, interviews and documentation. Primary data was obtained from interviews with 3 informants consisting of the Head of the Air Transportation Section, Aircraft Eligibility and Operation and Air Transport Inspector, as well as Observations at the Regional Airport Authority Office. Supervision and sanctions for tariffs, as well as a list of flight ticket prices for Lion Air Airlines, Sultan Hasanuddin International Airport, Makassar. The results of the study show that tarif control by the Region V Airport Authority is carried out in 2 ways, namely Offline and Online. The Offline method is carried out by viewing proof of ticket sales at the Airline Office and the Online Method is carried out by viewing prices at the Travel Agent or on the Lion Air Airline Website. And the results of the study show that in general the prices for Lion Air airline tickets at Sultan Hasanuddin Makassar International Airport are still categorized or included in the safe range and do not exceed the Upper Limit Rates that have been regulated in the Regulations.
 Keywords: Supervision, Airfare, Air Transport Inspector, Airport Authority

Flight Reservation System

Flight Reservation System

AIR RESERVE -An Airline Reservation System

“Airline Reservation System” is all about flight ticket booking and managing flight database as admin. It is a project toward enhancing the relationship between customer and airline agencies through the use of ARSs.

FAKTOR PENGARUH PADA KEPUTUSAN PEMBELIAN TIKET PESAWAT DI APLIKASI TRAVELOKA

The purpose of this study was to determine whether or not the influence of customer experience, consumer confidence, prices and flight ticket purchasing decisions on the Traveloka application for UNIMAR AMNI Semarang Students. The sample in this study amounted to 52 respondents who used airline ticket services in the Traveloka application. By using probability sampling technique, namely by random sampling. Analysis of the data used in this study is descriptive analysis and quantitative analysis, the data were analyzed using multiple linear regression analysis with the help of (software) Statistical Package For Social Science (SPSS) version 26. The results of the study by partially testing the t-test hypothesis showed that Customer experience, consumer trust, and price have a positive and significant effect on purchasing decisions. Based on the research results, it can be seen that the research model of the multiple linear regression equation is Y= 0.095 + 0.484.X1 + 0.195.X2 +0.295.X3 + . From the regression equation, it can be seen that the most dominant variable affecting the increase in the number of passengers is the price variable with a regression coefficient of 0.095. And the R2 test (Adjusted R Square) obtained results of 0.780 or 78.0% which means that purchasing decisions are influenced by customer experience, consumer confidence and price variables which are 63.8% and other factors that influence passenger decisions are 36.2 % or 0.362.

Algoritma K-Nearest Neighbors dan Synthetic Minority Oversampling Technique dalam Prediksi Pemesanan Tiket Pesawat

This study applies the Synthetic Minority Oversampling Technique to improve the performance of the K-Nearest Neighbors method in predicting the unbalanced data class. Most classification algorithms implicitly assume that the processed data has a balanced distribution, so that the standard classifier is more inclined towards data with a dominant class number (majority class). The use of Synthetic Minority Oversampling Technique can improve the performance of the K-Nearest Neighbors method for flight ticket booking data. Although in terms of accuracy, Synthetic Minority Oversampling Technique with K-Nearest Neighbors is lower at 79.65% compared to K-Nearest Neighbors without using Synthetic Minority Oversampling Technique, which is 97.81%, the suggested technique did not improve but from other performance, The proposed method can outperform K-Nearest Neighbors by using Synthetic Minority Oversampling Technique in terms of precision, recall, and F1-Score when applied to the Airline Ticket Booking dataset. Precision increased 18.00% from 62.00% to 80.00%, recall increased 28.00% from 52.00% to 80.00%, and F1-Score increased 27.00% from 53.00% to 80 ,00% on the flight ticket booking dataset.

Pengaruh Perilaku Konsumen terhadap Keputusan Pembelian Tiket Pesawat Secara Online pada Situs Tiket.Com Periode 2021

Transactions in shopping are things that cannot be separated from the community in order to carry out life. Judging from the transaction process carried out in the era before digitization, it required consumers to come to see the seller in order to get the goods or services they needed and wanted. With digitalization happening on a large scale in the community, it can facilitate the transaction process for consumers. There are many factors that can have an impact as a result of this extraordinary change, these factors include cognitive, cultural, social and personality factors of a person. This study is intended to find out changes in attitudes in individuals due to online flight ticket transactions on the tiket.com website. The quantitative approach acts as the method used in this study with the support of a simple regression analysis step. The sample used in this study was 100 samples. The conclusion of this study shows that the number of individual attitudes has a relevant impact on the choice to make transactions to buy airline tickets online on the tiket.com website. Thus it can be proven from the value in a simple linear regression analysis worth, Y = 2.762 + 0.306X. This value shows that consumer attitudes have a good impact on the choice to buy, which is worth 59%.
 Keywords: Consumer Behavior, Purchasing Decisions, Tiket.com

Implementation of Flight Fare Prediction System Using Machine Learning

Abstract: The Flight ticket prices increase or decrease every now and then depending on various factors like timing of the flights, destination, duration of flights. In the proposed system a predictive model will be created by applying machine learning algorithms to the collected historical data of flights. Optimal timing for airline ticket purchasing from the consumer’s perspective is challenging principally because buyers have insufficient information for reasoning about future price movements. In this project we majorly targeted to uncover underlying trends of flight prices in India using historical data and also to suggest the best time to buy a flight ticket. The project implements the validations or contradictions towards myths regarding the airline industry, a comparison study among various models in predicting the optimal time to buy the flight ticket and the amount that can be saved if done so. Remarkably, the trends of the prices are highly sensitive to the route, month of departure, day of departure, time of departure, whether the day of departure is a holiday and airline carrier. Highly competitive routes like most business routes (tier 1 to tier 1 cities like Mumbai-Delhi) had a non-decreasing trend where prices increased as days to departure decreased, however other routes (tier 1 to tier 2 cities like Delhi - Guwahati) had a specific time frame where the prices are minimum. Moreover, the data also uncovered two basic categories of airline carriers operating in India – the economical group and the luxurious group, and in most cases, the minimum priced flight was a member of the economical group. The data also validated the fact that, there are certain time-periods of the day where the prices are expected to be maximum. The scope of the project can be extensively extended across the various routes to make significant savings on the purchase of flight prices across the Indian Domestic Airline market. Keywords: Flight ticket, Optimal timing, historical data, competitive routes, Indian Domestic Airline market.

Flight Fare Prediction System Using Machine Learning

Abstract: The Flight ticket prices increase or decrease every now and then depending on various factors like timing of the flights, destination, duration of flights. In the proposed system a predictive model will be created by applying machine learning algorithms to the collected historical data of flights. Optimal timing for airline ticket purchasing from the consumer’s perspective is challenging principally because buyers have insufficient information for reasoning about future price movements. In this project we majorly targeted to uncover underlying trends of flight prices in India using historical data and also to suggest the best time to buy a flight ticket. The project implements the validations or contradictions towards myths regarding the airline industry, a comparison study among various models in predicting the optimal time to buy the flight ticket and the amount that can be saved if done so. Remarkably, the trends of the prices are highly sensitive to the route, month of departure, day of departure, time of departure, whether the day of departure is a holiday and airline carrier. Highly competitive routes like most business routes (tier 1 to tier 1 cities like Mumbai-Delhi) had a non-decreasing trend where prices increased as days to departure decreased, however other routes (tier 1 to tier 2 cities like Delhi - Guwahati) had a specific time frame where the prices are minimum. Moreover, the data also uncovered two basic categories of airline carriers operating in India – the economical group and the luxurious group, and in most cases, the minimum priced flight was a member of the economical group. The data also validated the fact that, there are certain time-periods of the day where the prices are expected to be maximum. The scope of the project can be extensively extended across the various routes to make significant savings on the purchase of flight prices across the Indian Domestic Airline market. Keywords: Flight ticket, Optimal timing, historical data, competitive routes, Indian Domestic Airline market

Pengaruh Perilaku Konsumen terhadap Keputusan Pembelian Tiket Pesawat Secara Online pada Situs Tiket.Com Periode 2021

Transactions in shopping are things that cannot be separated from the community in order to carry out life. Judging from the transaction process carried out in the era before digitization, it required consumers to come to see the seller in order to get the goods or services they needed and wanted. With digitalization happening on a large scale in the community, it can facilitate the transaction process for consumers. There are many factors that can have an impact as a result of this extraordinary change, these factors include cognitive, cultural, social and personality factors of a person. This study is intended to find out changes in attitudes in individuals due to online flight ticket transactions on the tiket.com website. The quantitative approach acts as the method used in this study with the support of a simple regression analysis step. The sample used in this study was 100 samples. The conclusion of this study shows that the number of individual attitudes has a relevant impact on the choice to make transactions to buy airline tickets online on the tiket.com website. Thus it can be proven from the value in a simple linear regression analysis worth, Y = 2.762 + 0.306X. This value shows that consumer attitudes have a good impact on the choice to buy, which is worth 59%.

Flight Ticket Price Prediction Using Regression Models

Abstract: Many people nowadays choose to travel by flights. The cost of an airline ticket has a significant impact on a traveller’s decision on which mode of transportation to use. A wide number of factors influence the price of an airline ticket, including social, competitive, marketing, and financial factors, among others. Every airline has a different technique for determining ticket prices. We can uncover the rules that airlines may use to model their fare variation using Machine Learning. In this project paper, we propose developing a web-based application for projecting the price of a flight ticket using Kaggle data, where the dataset contains various data related to 10,000 flights. The framework proposed will be used to simulate several regression algorithms for estimating projected flight fares. The model that will produce extremely accurate forecasts will be finalized, and it will solely be utilised to forecast the price. Keywords: regression, machine learning, model, prediction, algorithms

BI-OBJECTIVE GOAL PROGRAMMING FOR AIRLINE CREW PAIRING

The crew cost constitutes 20% of the direct operating cost in airline operations after the aircraft fuel cost. Effective crew scheduling can save tens of millions of dollars to the airlines and result in low-cost flight tickets for the passengers and improved quality of life for the crew. The crew pairing requires addressing union expectations, company rules, regulations of countries' civil aviation authorities. In this study, bi-objective goal programming is proposed to minimize the number of crew to perform flights and minimize the flight pairings cost while addressing the challenging issues mentioned above. The integer set-covering goal programming model is formulated and solved using GAMS mathematical programming software. The results showed that the bi-objective model could provide significant cost advantages to airlines. The computational experiments have been performed over a set of real data.

PERANCANGAN SISTEM INFORMASI PEMESANAN TIKET PESAWAT DOMESTIK BERBASIS WEB PADA CV JENIKA GROUP DEPOK

Based on the Tiket.com survey in the third quarter of 2020, there was a three-fold increase in transactions that occurred in hotel booking services and flight bookings. This increase is considered very fast in the midst of the Covid-19 pandemic due to the encouragement of people to take vacations in the midst of the pandemic. To be able to compensate for the increase that occurred, tour and travel companies are required to be able to optimize services by computerizing the flight ticket booking system. Therefore, the author intends to create a website for booking domestic airline tickets for CV JENIKA GROUP Depok. The author uses a method that has proven its effectiveness in designing an information system, namely the Waterfall method. Based on the results of the study, it can be concluded that the design of this web-based domestic flight ticket booking program can provide convenience and comfort for admins and customers of CV JENIKA GROUP and can further expand marketing reach.

EFEKTIVITAS IKLAN TERHADAP KEPUTUSAN WISATAWAN MEMBELI TIKET PESAWAT DI TRAVELOKA.COM

This research was conducted to analyze the Effectiveness of Advertising on Tourist Decisions in Purchasing Flight Ticket on the Online Travel Agent Traveloka.com using the EPIC Model and Direct Rating Method (DRM) and its Effect on the Flight Ticket Purchase Decision partially and simultaneously. This research method uses data collection techniques in the form of observation, questionnaires, and literature study. The technique of determining the sample using a purposive sampling method. The data analysis technique used quantitative descriptive analysis and multiple linear regression analysis. The results of this study indicate that Traveloka.com advertising using the EPIC Model method in measuring the advertising communication approach has been effective in introducing products in the form of flight tickets with a value of 4,008 and the Direct Rating Method in measuring good value advertising messages in conveying messages with a value of 79.44. EPIC Model's multiple linear regression analysis on Purchasing Decisions has a positive effect with a significant value of 0.015, the Direct Rating Method has a positive effect with a significant value of 0.003, while simultaneously or simultaneously, the Effectiveness of Advertising on Purchasing Decisions has a positive and significant effect. Based on the results obtained, the effectiveness of advertising can have an influence on purchasing decisions made by buyers through advertisements that contain information about existing products.

Flight Ticket Taxes in Europe: Environmental and Economic Impact

Flight Ticket Taxes in Europe: Environmental and Economic Impact

Novel Approach for Fare Prediction Using Machine Learning Techniques

A survey suggests that the Flight and Cab fares vary according to various factors like location, time of the day, etc. The airline companies put into effect dynamic pricing for the flight tickets. Also, it changes with the festival, holiday season and weekends. So, what’s an excellent time to buy a flight ticket? The same can be seen with cabs as well, where the fare depends upon the number of passengers, traffic, etc. The seller has information about all of the factors, but the buyers are able to access the information that is limited through which we cannot predict the tariffs. Considering the characteristics like time of departure, the number of days left for departure and time of the day, it’ll give the prime time to purchase the ticket. Likewise, the cab companies like Uber and Ola use factors like traffic in a particular location, demand and supply factors, for example when the demand for cabs is high there is a hike in prices but when the demand for cabs is not high the prices are calculated normally according to their algorithm. Availability of drivers and type of car to travel in are also crucial factors in determining the cab fares. The motive of the paper is to analyse the factors that influence the deviation within the tariffs and the way they’re associated with the change within the prices. The impetus for the research paper is to inspect the elements which have an impact on the deviations in the tariffs and how they could be related to the variation within the prices. Using this data, build an algorithm that can assist buyers to buy a ticket at an optimal time when they get the maximum benefits and minimum fares.

The Effects of Covid19 Pandemic on Tourism Sector

In this research, we are going to analyze the effects of Covid 19 pandemic on the human resources management in the tourism sector. MakeMyTrip is Indian online tourism company which provides online travel services, including flight tickets, domestic and international vacation packages, hotel reservations, railways and bus tickets. MakeMyTrip has branch office in New York, Singapore, Kuala Lumpur, Phuket, Bangkok, and Dubai. This sector is one of the most affected by the pandemic. This study aims to analyze how employees adapt to the new needs of the sector and how companies motivate them to meet the organizational goals. The method used in this study was descriptive method in which the data taken from secondary source such as research articles and scientific journal. The results found that as many as 350 employees of MakeMyTrip had been laid off in order to maintain the company financial remain stable. They also reduce their variable costs such as advertising, sales promotions, payment gateway costs, optimized IT infrastructure and expenses related to the operation of their offices and other facilities.

Effect of marketing mix and service quality on customer satisfaction and loyalty (A study on PT. Mutiara Cemerlang Utama Bali)

The COVID – 19 pandemic been made bad impact all tourism sector. Travels agents need to be adapted to pandemic condition to survive in this current situation. The purpose of this research is to test the influence of marketing mix, quality of service on customer satisfaction and loyalty. This study took samples on the research population customers of PT. Mutiara Cemerlang Utama Bali who buys domestic and international flight tickets, makes hotel room reservations, tour package, India pilgrimage tours, assistant to make passport and visa extension. This research leads to descriptive and verificative research called explanatory research. The data was collected by interviewing and disseminating questionnaires by purposive sampling method processed using Partial Least Square (PLS) analysis tool, the number of samples used by 120 samples. The results obtained in this study are that the marketing mix has a significant positive effect on customer satisfaction, the quality of service has a significant positive effect on customer satisfaction, but marketing mix and quality of service have a positive effect on customer loyalty. This is due to several factors that affect such as the price competition of travel agents. Customer satisfaction has a positive and significant effect on customer loyalty.

The Effect of Electronic Word of Mouth (E-Wom) and Brand Image on Repurchase Intention Moderated by Brand Trust in the Garuda Indonesia Airline

Repurchase intention is a commitment of a customer formed as a consumer purchases a product or service. Many factors influencing repurchase intention, including the E-Word of Mouth (E_WOM), brand image and brand trust. E-WOM is an effective tool for building brand awareness, creating hype in the market, influencing purchasing decision and developing brand loyalty. Brand image is a description of association of consumer beliefs in a particular brand. Brand Trust is a customer's desire to rely on a brand at any cost. The present study is a survey using an associative quantitative approach aimed at determiniong the effect of E-WOM and brand image on repurchase intention moderated by brand trust. The population of the study included all customers of Garuda Indonesia airline who have used the airline in the past year, especially the Garuda Indonesia Medan, which amounted to 36,000 during 2019. The sampling method used a sample formula with certain criteria so that were 100 samples. The data collection method used a questionnaire. Data analysis used path analysis. The results of the the study showm that E-WOM has a significant effect on Brand Trust. Brand Image has a significant influence on Brand Trust. Brand Trust has a significant positive effect on the Repurchase Intention variable. E-WOM has a significant positive effect on variable Repurchase Intention. Brand Image has a significant effect on the Repurchase Intention variable through Brand Trust. E-WOM has a significant positive effect on variable Repurchase Intention through Brand Trust. Brand Image has a significant effect on the Repurchase Intention. In other words, Brand Trust is proven to moderate the influence of Brand Trust on Repurchase Intention. It is recommended that Garuda Indonesia Airlines provide consistent, fast and quality service so that it gets positive reviews from customers which will increase Repurchase Intention of Garuda Indonesia flight tickets. Keywords: E-WOM, Brand image, brand trust, repurchase intention.

WHEN ‘HOMELAND’ IS NO LONGER ‘HOME’, THE IDENTITY CRISIS, THE LOSS OF MOBILITY, AND THEIR SOCIAL MEDIA CAMPAIGN ——A STUDY OF SOCIAL MEDIA TESTIMONIOS BY CHINESE TRANSNATIONAL WORKERS UNDER COVID-19 GLOBAL PANDEMIC

In this article, I apply narrative analysis to draw a picture on the life of Chinese transnational workers under Covid-19 global pandemic in 2020. I try to explore the identity, space, and emotion of Chinese transnational workers under the pandemic crisis through their testimonios on WeChat public space, which is their first-person written narratives on their bitter, and even traumatic experiences of being a transnational worker in countries under severe pandemic conditions but unable to find their way home. With Chinese government’s policy setting broad limitations on international flights, requirements of Corona virus nucleic acid testing before boarding, unaffordable flight tickets and the forced, self-financed 14-day quarantine after landing, China, as the homeland of Chinese transnational workers, closed its door and turned them away. However, their sufferings and stories are less covered by the media and known by the public. By posting testimonios in virtual space on WeChat, Chinese transnational workers from various destination countries tried to voice themselves and appeal for supports.