Implementasi Konsep Dynamic Capacity Dalam Peningkatan Kapasitas Terminal 1 Bandara Internasional Soekarno Hatta

Bandara Internasional Soekarno Hatta (CGK) saat ini bukan hanya menjadi bandara tersibuk di Indonesia, namun telah menjadi bandara tersibuk di wilayah Asia Pasifik. Lonjakan penumpang tiap tahunnya tidak sebanding dengan kapasitas terminal sehinggal mengalami overcapacity. Penelitian ini bertujuan untuk penilaian kapasitas dan alur kerja Terminal 1 dengan mengidentifikasi pola perilaku penumpang (passenger behavior) dalam penggunaan waktu pemrosesan fasilitas (processing time), antrian penumpang (passenger queueing), ruang (space) dan jumlah faslitas yang dibutuhkan (number of facilities). Selanjutnya akan dilakukan implementasi konsep dynamic capacity dalam peningkatan kapasitas Terminal 1 Bandara Internasional Soekarno Hatta sehingga dapat memberikan usulan perubahan/rekomendasi dalam mengatasi overcapacity yang terjadi. Analisis yang dilakukan menggunakan implementasi konsep dynamic capacity pada jam puncak (peak hour) dengan pembatasan pada area sisi udara yang dianggap tidak mempengaruhi kapasitas terminal. Pendekatan keseimbangan kapasitas (balancing capacity) digunakan dalam analisis ini dalam penentuan kapasitas setiap fasilitas pemrosesan (processor) Terminal 1 dengan mempertimbangkan bottleneck yang terjadi sehingga didapatkan peningkatan kapasitas menjadi 3 kali dari kapasitas Terminal 1 Bandara Internasional Soekarno Hatta. Setiap sub terminal 1 mempunyai kapasitas 3.000.000 penumpang/tahun yang dapat ditingkatkan menjadi 10.090.000 penumpang/tahun dengan beberapa perubahan yang harus dilakukan meliputi pengurangan waktu pemrosesan (processing time), penambahan area (space), penyesuaian tingkat layanan dan penambahan fasliltas yang dapat mempercepat waktu pemrosesan (processing time

Determination of the International Hub Airport to Support the Flight Network Efficiency of ASEAN Region Countries (Case Study of the Indonesian Airport System)

Indonesia has 266 airports spread throughout the Indonesia archipelago. With the growth rate of air passengers increasing year by year, Indonesia needs to increase its role in managing the international network in Southeast Asia. Especially with the implementation of the Open Sky policy, Indonesia must take advantage of the potential opportunities. This chapter attempts to examine parameters at hub airports for international flights with the Association of Southeast Asian Nations (ASEAN), which has network efficiency performance. There are eight airports actively showing behaviour as “hubs”, and considering the potential geographic and movement potential in ASEAN, the most efficient is the three hubs and 32 spokes configuration. Thus, the three hub airports that can be optimised to support the efficiency of international flight routes in ASEAN are Kualanamu Airport-Medan, Soekarno Hatta Airport-Jakarta and Juanda Airport-Surabaya

Pengukuran Kinerja Operasional Airside Bandara Berdasarkan Delay Pesawat Menggunakan Arcport Altocef (Studi Kasus : Bandara Internasional Soekarnohatta)

Bandara Internasional Soekarno Hatta menempati ranking 12 bandara tersibuk di dunia pada sepanjang tahun 2014.Bandara ini diperkirakan akan terus mengalami peningkatan hingga 150 juta penumpang per tahun pada tahun 2025 mendatang. Oleh karena jumlah penumpang akan terus bertambah maka seluruh fasilitas bandara harus mampu menunjang besarnya laju penumpang setiap tahunnya. Dengan adanya prediksi tersebut, maka perlu dilakukan evaluasi kinerja pada sisi airside yang merupakan elemen penting dari suatu bandara. Pengukuran kinerja airside menggunakan alat airport simulator yang termasuk pada teknologi simulasi bandara 5D terkini dan termaju yang dikembangkan oleh Aviation Research Corporation.Pada penelitian ini, pengukuran kinerja airside bertujuan untuk mendapatkan waktu delay rata-rata dari pesawat yang datang maupun berangkat melalui simulasi kondisi aktual. Informasi tersebut akan digunakan untuk membandingkan dengan skenario simulasi dengan airfield enhancement. Skenario dengan selisih waktu rata-rata delay terbesar dengan simulasi aktual akan menjadi landasan rekomendasi untuk PT Angkasa Pura 2 selaku pengelola Bandara Internasional Soekarno-Hatta. Skenario dengan penambahan departure queueing taxiway dapat menurunkan rata-rata delay sebanyak 61.5% dari rata-rata delay aktual. =============================================================================================== Soekarno-Hatta International Airport has placed 12 in the busiest airports in the world throughout 2014. The airport is expected to handle up to 150 million passengers per year in 2025. Since the number of passengers will always increase, all the airport's facility needs support the rate of passenger per year. With such predictions, there needs to be an airside performance evaluation which is an important part of an airport. Airside performance measurement uses an airport simulator appertain to the latest and most advanced 5D airport simulation technology developed by Aviation Research Corporation. In this research, airside performance measurement aims to acquire average delay of arriving and departing planes through actual condition simulation. This information will be used as a comparison with simulation skenarios using airfield enhancement. A skenario with the largest average delay time difference with the actual simulation will be used as a base of recommendation for PT Angkasa Pura 2 as the manager of Soekarno-Hatta International Airport. Skenarios using departure queueing taxiway could reduce the average delay as much as 61.5 percent from the actual average delay

Energy research in airports: A review

The main function of an airport is to provide access to air transport both for passengers and cargo. The number of air operations over the past 20 years has increased rapidly, and this has led to a rise in the energy needs of airports to satisfy this demand. As a consequence, the cost of energy supply for airport managers has escalated. At the same time, global energy consumption has soared due to the needs of emerging countries like China and India, with the consequent environmental impact. This complex scenario of environmental and economic factors has made airport managers become aware of the need to reduce energy consumption as well as a more efficient use of it. The aim of this article is to analyze the main behaviors and energy trends at airports in more recent research, starting with the description of the main energy sources and consumers, the application of energy conservation and energy efficiency measures, the establishment of energy indicators and benchmarking between airports, as well as energy modeling and simulation

Framework for airport outbound passenger flow modelling

This paper focusses upon passenger flow issues within airport terminals and includes all activities occurring between curb-side and boarding. To improve passenger flow and associated planning activities, a simulation framework is developed using Discrete-Event Simulation (DES). The DES is built using ExtendSim V9.2 simulator software from Imagine That. The model can be used to evaluate the efficiency of the outbound operational processes including check-in, security screening, immigration & custom and boarding. It can also assist management to identify potential bottlenecks in the system. The main input of the model is the flight schedule. A case study of the Brisbane international airport was analysed

Studi Kinerja Runway 3 dan Pengaruh Adanya Crossing Taxiway Terhadap Kapasitas Runway 2 di Bandara Internasional Soekarno-Hatta

Salah satu bandara dengan penerbangan terpadat yang ada di Indonesia adalah Bandara Soekarno-Hatta yang berada di Tangerang, Banten. Adanya peningkatan jumlah penumpang mengakibatkan pergerakan pesawat juga semakin meningkat sehingga diperlukan kapasitas runway yang lebih besar untuk menghindari tundaan pergerakan yang dapat terjadi. Bandara Internasional Soekarno-Hatta membangun runway baru sebagai runway ke-3 dari dua runway paralel yang sudah ada. Menurut PT. Angkasa Pura II adanya runway ke-3 dapat meningkatkan kapasitas pergerakan pesawat sebanyak 114 hingga 120 pergerakan setiap jam. Dibangun juga East Cross Taxiway berada di sebelah sisi timur bandara untuk meningkatkan kapasitas runway menjadi lebih dari 86 pergerakan setiap jamnya. Tugas akhir ini mencoba untuk mengetahui apakah kapasitas dari runway ke-3 yang direncanakan dan baru beroperasi dapat memenuhi target yang telah ditetapkan. Runway 3 baru Bandara Internasional Soekarno-Hatta memiliki panjang runway sebesar 3000 x 60 meter dengan arah runway 06 – 24. Dari hasil pengukuran didapatkan lebar taxiway ± 30 meter dengan 3 buah exit taxiway dengan sudut 30° dan 1 exit taxiway dengan sudut 90°. Dari analisis sisi udara runway 3 pada Tugas Akhir ini didapatkan panjang runway 3200 meter dengan lebar 45 meter dengan pesawat terbesar Boeing 747-400, sedangkan lebar taxiway sebesar 25 meter. Untuk analisis arah runway sama dengan arah eksisting yaitu 06 – 24 dengan usability factor 98% - 99%. Berdasarkan hasil perhitungan simulasi didapatkan kapasitas pergerakan pada runway 2 sebesar 42 pergerakan dan 43 pergerakan pada runway 3. Namun, runway 2 dan 3 tidak dapat digunakan secara bersamaan (simultan) untuk take off dan landing dikarenakan jarak pemisah antara dua runway terlalu dekat sebesar 500 meter. Adanya crossing taxiway mengakibatkan pergerakan di runway 3 harus melewati runway aktif 2, sehingga perlu dilakukan pengaturan jadwal. Dengan menggunakan prinsip Air Traffic Separation (ATS), didapatkan kapasitas sebesar 59 pergerakan dengan 30 pergerakan pada runway 2 dan 29 pergerakan pada runway 3. Dari hasil tersebut dapat disimpulkan kapasitas dari kedua runway menurun dan penambahan runway 3 hanya meningkatkan operasi penerbangan sebanyak 17 pergerakan dibandingkan dengan sebelum adanya penambahan runway 3. ======================================================================================================================== One of the airports with the busiest flight in Indonesia is Soekarno-Hatta Airport, located in Tangerang, Banten. The increase in the number of passengers affected in the increase in aircraft movement, so that a larger runway capacity was needed to avoid movement delays that could occur Soekarno-Hatta International Airport built a new runway as the 3rd runway of two existing parallel runways. According to PT. Angkasa Pura II, the presence of the 3rd runway can increase aircraft movement capacity by 114 to 120 movements per hour. Also built East Cross Taxiway on the east side of the airport to increase runway capacity to more than 86 movements per hour. The new Runway 3 of Soekarno-Hatta International Airport has a runway length of 3000 x 60 meters in the runway direction 06-24. From the measurement results obtained ± 30 meters taxiway width with 3 exit taxiways with an angle of 30 ° and 1 exit taxiway with an angle of 90 °. From the airside analysis of runway 3 in this Final Project, the runway length is 3200 meters with a width of 45 meters with the largest aircraft of the Boeing 747-400, while the taxiway width is 25 meters. The runway direction analysis is the same as the existing direction which is 06-24 with usability factor 98% - 99%. Based on the simulation calculation results, the movement capacity on runway 2 in 42 movements and 43 movements on runway 3. However, runway 2 and 3 cannot be used simultaneously for take-off and landing because the separation distance between the two runways is too close by 500 meters. The presence of a crossing taxiway results in movement on runway 3 must pass through active runway 2, so it is necessary to set a schedule. By using the principle of Air Traffic Separation (ATS), a capacity of 59 movements is obtained with 30 movements on runway 2 and 29 movements on runway 3. From these results, it can be concluded that the capacity of both runways decreases and the addition of runway 3 only increases flight operations by 17 movements compared with before the addition of runway 3

Studi Dispersi Emisi Nox Pesawat Komersil Dari Sumber Garis (Line Source) Di Bandar Udara Internasional Juanda

Bandara Internasional Juanda merupakan salah satu bandara yang paling sibuk yang berada di Jawa Timur. kegiatan penerbangan yang tinggi di bandara juanda menimbulkan potensi pencemaran udara yang dihasilkan oleh pesawat melalui pembakaran bahan bakar avtur pada mesin jet pesawat. Hal ini menimbulkan potensi pencemaran udara apalagi jika dilihat total penerbangan yang ada di juanda rata rata 137.051 pertahun. Kegiatan penerbangan per harinya mencapai 380 kegiatan. Emisi yang dikeluarkan dari pesawat salah satunya adalah NOx. NOx yang tertimbun dalam jumlah yang besar dalam waktu yang lama di dalam tubuh. Pada bandara juanda belum terdapat stasiun pemantau kualitas udara. Penelitian ini bertujuan untuk mengetahui sebaran dari polutan NOx pada sekitar bandar udara Juanda. Sebaran polutan NOxakan disesuaikan dengan arah angin dominannya. Peta sebaran polutan nantinya akan menjadi sebuah rekomendasi kepada pihak otoritas bandara untuk dapat menanggulaingi dampaknya. Penelitian ini juga bertujuan untuk melihat seberapa besar pengaruh dari kondisi meteorologi. Penelitian kali ini menggunakan pemodelan gaussian finite line source untuk mendapatkan persebaran dispersi dari polutan NO¬x. Model ini dipengaruhi oleh beberapa hal, yaitu kondisi meteorologi, arah angin, kecepatan angin, laju emisi, panjang sumber emisi. Model dapat digunakan dengan baik, maka faktor faktor tersebut harus dimiliki datanya secara lengkap. Pengumpulan data dilakukan dengan menggunakan data sekunder melalui pihak terkait. Data laju emisi diambil data dari angkasa pura dari jumlah pesawat yang melakukan aktifitas di bandar udara Juanda. Data data arah angin, kecepatan angin, dan cuaca didapatkan dari BMKG ataupun BLH kota Surabaya. Data yang sudah terkumpul akan di olah menjadi windrose yang berfungsi untuk mengetahui arah angin dominan dan kecepatannya. Windrose digunakan sebagai acuan untuk menentukan lokasi titik reseptor dari model yang akan dibuat. Sampling lapangan dilakukan untuk membandingkan hasil model yang telah dibuat. Hasil yang didapatkan dari model dengan beberapa skenario menunjukkan bahwa di beberapa tempat terdapat titik yang konsentrasinya melebihi baku mutu. Model dispersi jika dibandingkan dengan hasil sampling dengan menggunakan impinger terlihat angka yang jauh berbeda. Hasil sampling yang dihitung dengan menggunakan RSMPE error yang didapat mencapai 73,67% dan yang terendah adalah 47,62%. Hal ini dapat disebabkan oleh beberapa hal, diantaranya adalah sumber emisi lain yang masuk semisalkan dari kendaraan, keadaan meteorologi, dan juga tidak diperhitungkannya disposisi. ======================================================================================================================== International Juanda Airport is one of the most busy airport in East Java . Juanda airport potential to make air pollution because its high act ivity in aviation. Air pollution is caused by avtur combustion which is emmited by jet engine in every airplane. As we can see there are 137.051 per year. If we count there is 380 airplane that using Juanda International Airport each day. pollutant that e mmited by airplane is NO x . If NO x is concentrate with high level in body can in a long period time can cause illness. Therefore there are no air monitoring stastion in Juanda Airport. This research is to see how much airplane activity and meterological con dition is affect to dispersion of pollutant in some area near airport focusing on NO x emmision. In this research to calculate dispersion of NO x emmision that emmited by airplane is using gaussian finite line source. This model is affec with several conditi on such as, wind direction, wind speed, emmision rate, length of emmision source. If we want to calculate we have to make sure we can get all of those data. All of data we need is collect given by related agency . Emmision rate data is collect from angkasa pura, calculate from airplane number that landing or take off in bandara juanda. Wind speed and wind direction is collect from BMKG or BLH surabaya. After all data is collected so we can continue to make windrose. Windrose is calculate to know dominant win d speed and direction. After that we can put reseptor point to calculate dispersion NO x concentration. After dispersion model is calculate we need to do air sampling with impinger to know that model we calculate is accurate or not.Model calculate result from different scenario show that some reseptor point is above threeshold. But if we compare with air sampling with impinger the result is difference by + - 100 μg/m 3 . If we count with RSMPE error percentage is 73,67% and 47,62%. This condition is caused by emmision near sampling site, meterological condition and not considerable disposition of emmision

Determining Priority Service of Yogyakarta Adisutjipto Airport Using Servqual Method and Kano Model

Nowadays, airports are expected to be operated as a self-service organisation that provides efficient and high-quality services. Since the satisfaction of passengers is essential for airport’s operators, the efforts to provide better services for passengers become a concern for airport’s operators by increasing the quality of service to passengers. It is crucial to identify which kind of services that would be the top priority service for the passengers. Thus, this study aims to improve the quality of service to passengers that are conducted by combining the Servqual method and Kano model. Servqual method is used to capture consumer perceptions and expectations of service along with the multi-dimensional research instrument, while the Kano model is a theory to observe costumer’s satisfaction preferences. The combination of the methods aims to determine the type of service that becomes the top priority for immediate improvement so that it can improve the service quality effectively. The selection of the priority services is based on the magnitude of the gap between expectations and perceptions of passengers on a particular service, and the assessment of passengers on the type of service that significantly influences passengers’ satisfaction with the service performance at the airport. The results of this research showed that there were three types of services as the top priority for improving their performance, namely the type of services related to the personal attention to passengers, the attractive waiting room conditions, and the understanding of each passenger’s needs individually. The airport management is expected to immediately improve the performance of the services so that the quality of service can immediately increase

METODE RANDOM SURVIVAL FOREST DENGAN ATURAN PEMISAHAN LOG-RANK DAN LOG-RANK SCORE UNTUK PREDIKSI DELAY PENERBANGAN AKIBAT CUACA

Metode Random Survival Forest (RSF) merupakan metode penggabungan analisis survival dengan klasifikasi. Metode ini menjadi alternatif dari model Cox Proportional Hazard ketika asumsi proportional hazard tidak terpenuhi dalam menganalisis data tersensor. Metode RSF sangat fleksibel untuk mengatasi data berdimensi tinggi dengan banyak kovariat. Dengan pendekatan seperti metode klasifikasi Random Forest, metode ini diawali dengan membuat sampel bootstrap, kemudian dilakukan random selection pada variabel untuk menumbuhkan pohon demi pohon hingga simpul terakhir. Performansi pembuatan RSF sangat bergantung pada aturan pemisahan. Aturan ini akan memecah node induk menjadi beberapa node anak hingga menghasilkan variabel penting (VIMP) yang paling berkontribusi. Aturan pemisahan yang digunakan dalam penelitian ini adalah Log-Rank dengan Log-Rank Score, kemudian membandingkan hasil keduanya. Indikator yang digunakan untuk membandingkan performansi kedua aturan pemisahan ini adalah dengan error rate atau nilai kesalahan prediksi. Studi kasus dilakukan pada data delay penerbangan akibat cuaca di rute bandara Soekarno Hatta menuju bandara I Gusti Ngurah Rai. Metode ini diterapkan untuk menentukan variabel yang berkontribusi pada lamanya delay penerbangan apakah lebih dari 30 menit atau kurang dari 30 menit. Hasil penelitian ini menunjukkan bahwa metode RSF dengan aturan pemisahan Log-Rank adalah pilihan terbaik untuk memprediksi delay penerbangan akibat cuaca dengan nilai error rate yang lebih kecil yaitu sebesar 0,3590 atau 35,90%. Ukuran hutan yang dibangun adalah terdiri dari 500 pohon. Urutan variabel penting yang berkontribusi di antaranya airlines, kecepatan angin, arah angin, suhu, curah hujan, dan kelembaban. Setelah performa RSF dengan Log-Rank dikatakan baik, dibuat web app sederhana untuk melakukan pengotomatisan prediksi delay penerbangan akibat cuaca. The Random Survival Forest (RSF) method is a method that combining survival analysis with classification. This method is an alternative to the Cox Proportional Hazard model when proportional hazard assumption is not fullfiled in analyzing the censored data. The RSF method is very flexible for handling high-dimensional data with many covariates. Using the approach of classification method such as the Random Forest, this method begins with drawing a bootstrap sample, then selecting variables randomly to grow tree by tree until the terminal node. Generated RSF performance is highly depends on splitting rules. This rule will split the parent node into several daughter nodes to show the most contributing variable importance (VIMP). The splitting rule that used for this research is Log-Rank and Log-Rank Score. We compare these two splitting rules. The indicator that used to compare the performance of the two splitting rules is the error rate or prediction error value. A case study was applied on flight delay data due to weather on the route of Soekarno Hatta airport to I Gusti Ngurah Rai airport. This method is applied to determine the variables that contributing in flight delays time, whether more than 30 minutes or less than 30 minutes. The results of this study indicate that the RSF method with Log-Rank splitting rules is the best choice for predicting flight delays due to weather with a smaller error rate value of 0.3590 or 35.90%. The size of the forest consists 500 trees. The sequence of important contributing variables are airlines, wind speed, wind direction, temperature, precipitation and humidity. After RSF with Log-Rank's performance was qualified as good, a simple web app is created to automate flight delay predictions due to weather

Collaborative rescheduling of flights in a single mega-hub network

Traditionally, airlines have configured flight operations into a Hub and Spoke network design. Using connecting arrival departure waves at multiple hubs these networks achieve efficient passenger flows. Recently, there has been much growth in the development of global single mega-hub (SMH) flight networks that have a significantly different operating cost structure and schedule design. These are located primarily in the Middle East and are commonly referred to as the ME3. The traditionalist view is that SMH networks are money losers and subsidized by sovereign funds. This research studies and analyzes SMH networks in an attempt to better understand their flight efficiency drivers. Key characteristics of SMH airports are identified as: (i) There are no peak periods, and flight activity is balanced with coordinated waves (ii) No priority is assigned to arrival/departure times at destinations (selfish strategy) only hub connectivity is considered (iii) There is less than 5% OD traffic at SMH (iv) The airline operates only non-stop flights (v) Passengers accept longer travel times in exchange for economic benefits (vi) Airline and airport owners work together to achieve collaborative flight schedules. This research focuses on the network structure of SMH airports to identify and optimize the operational characteristics that are the source of their advantages. A key feature of SMH airports is that the airline and airport are closely aligned in a partnership. To model this relationship, the Mega-Hub Collaborative Flight Rescheduling (MCFR). Problem is introduced. The MCFR starts with an initial flight schedule developed by the airline, then formulates a cooperative objective which is optimized iteratively by a series of reschedules. Specifically, in a network of iEM cities, the decision variables are i* the flight to be rescheduled, Di* the new departure time of flight to city i* and Hi* the new hold time at the destinatioin city i*. The daily passenger traffic is given by Ni,j and normally distributed with parameters µNi,j and sNi,j. A three-term MCFR objective function is developed to represent the intersecting scheduling decision space between airlines and airports: (i) Passenger Waiting Time (ii) Passenger Volume in Terminal, and (iii) Ground Activity Wave Imbalance. The function is non-linear in nature and the associated constraints and definitions are also non-linear. An EXCEL/VBA based simulator is developed to simulate the passenger traffic flows and generate the expected cost objective for a given flight network. This simulator is able to handle up to an M=250 flight network tracking 6250 passenger arcs. A simulation optimization approach is used to solve the MCFR. A Wave Gain Loss (WGL) strategy estimates the impact Zi of flight shift Δi on the objective. The WGL iteratively reschedules flights and is formulated as a non-linear program. It includes functions to capture the traffic affinity driven solution dependency between flights, the relationship between passengers in terminal gradients and flight shifts, and the relationship between ground traffic activity gradients and flight shifts. Each iteration generates a Zi ranked list of flights. The WGL is integrated with the EXCEL/VBA simulator and shown to generate significant costs reduction in an efficient time. Extensive testing is done on a set of 5 flight network problems, each with 3 different passengers flow networks characterized by low, medium and high traffic concentrations