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Doan Perdana
"[ABSTRAK
Intelligent Transportation System (ITS) adalah salah satu teknologi yang mengintegrasikan antar sistem informasi dan teknologi komunikasi dengan infrastruktur transportasi, kendaraan, dan pengguna jalan. Salah satu implementasi teknologi Intelligent Transportation System (ITS) adalah Vehicular Ad Hoc Network (VANET). VANET merupakan sistem komunikasi kendaraan yang mendukung untuk komunikasi Vehicle to Infrastructure (V2I) dan Vehicle to Vehicle (V2V). Sebagai bagian dari Intelligent Transportation System (ITS), komunikasi kendaraan dalam jaringan VANET dapat lebih efektif dalam menghindari kecelakaan dan kemacetan lalu lintas dari pada jika setiap kendaraan mencoba untuk memecahkan masalah ini secara individual.
Standar IEEE 1609.4 didefinisikan sebagai mode operasi Multikanal jaringan VANET pada lapisan Medium Access Control (MAC) yang terdiri dari tujuh kanal frekuensi yang berbeda, yaitu satu kanal CCH178 akan dialokasikan untuk Control Channel (CCH), yang digunakan sebagai kanal publik untuk aplikasi keamanan yang relevan di jalan. Enam kanal ya ng lainnya dialokasikan untuk Service Channel (SCH), yang digunakan sebagai kanal untuk menangani layanan multimedia dan yang tidak berhubungan dengan keamanan di jalan. Salah satu permasalahan dalam penjaminan kinerja pada IEEE 1609.4 adalah tingginya mobilitas node kendaraan dan perubahan lintasan yang berbeda. Hal ini menyebabkan delay yang tinggi dan throughput yang rendah. Peningkatan kinerja pada standar IEEE 1609.4 dapat dilakukan dengan optimasi pada proses sinkronisasi interval kanal CCH dan SCH.
Pada disertasi ini dikembangkan model baru Markov chain yang bertujuan untuk meningkatkan kinerja sistem koordinasi kanal dinamis pada standar multikanal IEEE 1609.4 terhadap pengaruh anomali kinerja, slot anomali, efek Doppler, fading Nakagami dan AWGN. Perbaikan kinerja yang dilakukan terhadap pengaruh diatas adalah dengan menggunakan nilai awal optimal Contention Window (CW). Penentuan nilai awal CW akan mempengaruhi kinerja yang dihasilkan pada model Markov chain yang digunakan. Nilai awal optimal CW didapatkan dari hasil distribusi node di setiap zone dengan menggunakan distribusi Poisson.
Dari hasil simulasi dan evaluasi kinerja yang dihasilkan, dapat dianalisa bahwa model DCF yang diajukan pada disertasi ini dapat menurunkan nilai delay transmisi CCH terhadap adanya kanal propagasi Nakagami dengan rata-rata (mean) sebesar 16.84 %. Selanjutnya, dapat disimpulkan bahwa kinerja yang dihasilkan pada model Markov chain dengan menggunakan nilai awal optimal CW didapatkan meningkatkan nilai Aggregate Throughput sebesar 42.53% dibandingkan dengan model yang diajukan oleh Wang. Sedangkan model DCF yang diajukan meningkatkan nilai probabilitas transmisi paket WAVE Service Advertisement (WSA) terhadap fenomena anomalous slot dengan persentase kenaikan rata-rata (mean) sebesar 11.35 %. Selanjutnya, dapat dianalisa bahwa model DCF yang diajukan meningkatkan nilai interval waktu dari akses contention kanal CCH terhadap efek Doppler dengan persentase kenaikan rata-rata (mean) sebesar 11.31 %;

ABSTRACT
Intelligent Transportation System (ITS) is one of the technologies that integrate information systems and communication technologies with transportation infrastructures, vehicles and road users. One implementation of the Intelligent Transportation System (ITS) is Vehicular Ad Hoc Network (VANET). VANET is a vehicle communication system which supports Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) communication. As a part of the Intelligent Transportation System (ITS), vehicles communication in VANET networks can be more effective in avoiding accidents and traffic congestion than if each vehicle try to solve this problem individually.
The IEEE 1609.4 standard is defined as the multichannel operation mode of VANET on Medium Access Control (MAC) layer. One of the problems in guaranteeing the performance of the IEEE 1609.4 is the high vehicular node mobility and different trajectory changes. These cause high delay and low throughput. Services Quality assurance to the IEEE 1609.4 standard can be done using optimizing the synchronization process of CCH and SCH channel intervals so that delay can be reduced and throughput saturation of SCH channel can be increased.
In this dissertation a new model of the Markov chain will be developed which aims to evaluate the performance of dynamic channel coordination system on the IEEE 1609.4 multichannel standard against performance anomalies influences, slot anomalies, the Doppler Effect, Nakagami fading and Additive White Gaussian Noise (AWGN). The performance improvements that is done to the effect above is to use the optimal initial value of Contention Window (CW). This is consistent with previous studies that have been done, the determination of the initial value of Contention Window (CW) will affect the resulting performance of the used Markov chain model. Optimal initial value Contention Window (CW) is obtained from the distribution of nodes in each zone by using the Poisson distribution.
From the simulation and performance evaluation results, it can be concluded that the DCF model in this dissertation can reduce the CCH transmission delay against the propagation channel Nakagami with an average of 16.84%. Moreover, it can be concluded that the performance of the resulting Markov chain model using the optimal initial value obtained CW increases value Aggregate Throughput of 42.8% against the effects of the anomaly performance. Meanwhile, the probability of packet transmission WSA influenced by anomalous slot with the percentage of mean increases approximately 11.35 %. Furthermore, it can be analyzed that the DCF model proposed result is the time interval CCH access contention influenced by anomalous slot with the percentage of mean increases approximately 11.31%;Intelligent Transportation System (ITS) is one of the technologies that integrate information systems and communication technologies with transportation infrastructures, vehicles and road users. One implementation of the Intelligent Transportation System (ITS) is Vehicular Ad Hoc Network (VANET). VANET is a vehicle communication system which supports Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) communication. As a part of the Intelligent Transportation System (ITS), vehicles communication in VANET networks can be more effective in avoiding accidents and traffic congestion than if each vehicle try to solve this problem individually.
The IEEE 1609.4 standard is defined as the multichannel operation mode of VANET on Medium Access Control (MAC) layer. One of the problems in guaranteeing the performance of the IEEE 1609.4 is the high vehicular node mobility and different trajectory changes. These cause high delay and low throughput. Services Quality assurance to the IEEE 1609.4 standard can be done using optimizing the synchronization process of CCH and SCH channel intervals so that delay can be reduced and throughput saturation of SCH channel can be increased.
In this dissertation a new model of the Markov chain will be developed which aims to evaluate the performance of dynamic channel coordination system on the IEEE 1609.4 multichannel standard against performance anomalies influences, slot anomalies, the Doppler Effect, Nakagami fading and Additive White Gaussian Noise (AWGN). The performance improvements that is done to the effect above is to use the optimal initial value of Contention Window (CW). This is consistent with previous studies that have been done, the determination of the initial value of Contention Window (CW) will affect the resulting performance of the used Markov chain model. Optimal initial value Contention Window (CW) is obtained from the distribution of nodes in each zone by using the Poisson distribution.
From the simulation and performance evaluation results, it can be concluded that the DCF model in this dissertation can reduce the CCH transmission delay against the propagation channel Nakagami with an average of 16.84%. Moreover, it can be concluded that the performance of the resulting Markov chain model using the optimal initial value obtained CW increases value Aggregate Throughput of 42.8% against the effects of the anomaly performance. Meanwhile, the probability of packet transmission WSA influenced by anomalous slot with the percentage of mean increases approximately 11.35 %. Furthermore, it can be analyzed that the DCF model proposed result is the time interval CCH access contention influenced by anomalous slot with the percentage of mean increases approximately 11.31%, Intelligent Transportation System (ITS) is one of the technologies that integrate information systems and communication technologies with transportation infrastructures, vehicles and road users. One implementation of the Intelligent Transportation System (ITS) is Vehicular Ad Hoc Network (VANET). VANET is a vehicle communication system which supports Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) communication. As a part of the Intelligent Transportation System (ITS), vehicles communication in VANET networks can be more effective in avoiding accidents and traffic congestion than if each vehicle try to solve this problem individually.
The IEEE 1609.4 standard is defined as the multichannel operation mode of VANET on Medium Access Control (MAC) layer. One of the problems in guaranteeing the performance of the IEEE 1609.4 is the high vehicular node mobility and different trajectory changes. These cause high delay and low throughput. Services Quality assurance to the IEEE 1609.4 standard can be done using optimizing the synchronization process of CCH and SCH channel intervals so that delay can be reduced and throughput saturation of SCH channel can be increased.
In this dissertation a new model of the Markov chain will be developed which aims to evaluate the performance of dynamic channel coordination system on the IEEE 1609.4 multichannel standard against performance anomalies influences, slot anomalies, the Doppler Effect, Nakagami fading and Additive White Gaussian Noise (AWGN). The performance improvements that is done to the effect above is to use the optimal initial value of Contention Window (CW). This is consistent with previous studies that have been done, the determination of the initial value of Contention Window (CW) will affect the resulting performance of the used Markov chain model. Optimal initial value Contention Window (CW) is obtained from the distribution of nodes in each zone by using the Poisson distribution.
From the simulation and performance evaluation results, it can be concluded that the DCF model in this dissertation can reduce the CCH transmission delay against the propagation channel Nakagami with an average of 16.84%. Moreover, it can be concluded that the performance of the resulting Markov chain model using the optimal initial value obtained CW increases value Aggregate Throughput of 42.8% against the effects of the anomaly performance. Meanwhile, the probability of packet transmission WSA influenced by anomalous slot with the percentage of mean increases approximately 11.35 %. Furthermore, it can be analyzed that the DCF model proposed result is the time interval CCH access contention influenced by anomalous slot with the percentage of mean increases approximately 11.31%]"
2015
D2073
UI - Disertasi Membership  Universitas Indonesia Library
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Waskitho Wibisono
"The integration of transportation systems is a very important issue within our society. In order to realize more secure and efficient systems, an integrated system needs to be developed that is decentralized, stable and highly automated. In previous papers we have proposed a three layer object model (3LOM) as the base architecture for the integration of transportation systems. This a per describes the design of the physical input/output sub-system in the bottom layer of the architecture and how to implement this layer from the technical perspective. This layer is primarily responsible for data acquisition, filtering and transmission of data to the middle layer and related control centers. Local knowledge is organized to be used to perform controlling functions within a localized environment. This paper is an important contribution to design the logical view of the bottom layer as an important step to develop the proposed integrated transportation system as our ultimate aim."
2004
JIKT-4-1-Mei2004-1
Artikel Jurnal  Universitas Indonesia Library
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Alexander Anindito Setyono
"Transportation has been a significant industry for big cities for hundreds of years. It is a part of our everyday lives and contributes considerably to a country’s economy. As the population of a certain country keep on increasing as time flies by, the demand for the innovation in the transportation world kept on increasing to keep up with the exponential growth of the industry. One of the technology that is used to handle the increasing demand for transportation analytics is by using big data analytics as it can handle humongous amount of data that are too large or complex to be dealt with traditional data processing application software. Big data analytics has been used through many different kind of applications in the modern era and it has achieve a great number of success in different field of work. A traffic data imputation is proposed in order to solve this problem and there are several imputation methods that are available which has their own plus and minuses. There are traditional data imputation methods that are already used from many years ago such as linear interpolation and regression but it has been proved that this traditional methos still have a low accuracy rating. Hence, a more modernized and more accurate method is introduced which is called the Generative Adversarial Network (GAN).

Transportasi telah menjadi industri yang signifikan bagi kota-kota besar selama ratusan tahun. Ini merupakan bagian dari kehidupan kita sheari-hari dan berkontribusi besar terhadap perekonomian suatu negara. Seiring dengan bertambahnya jumlah penduduk suatu negara, permintaan akan inovasi dalam dunia transportasi terus meningkat untuk mengikuti pertumbuhan industri yang eksponensial. Salah satu teknologi yang digunakan untuk menangani peningkatan permintaan ini adalah dengan menggunakan analitik data besar karena dapat menangani data dalam jumlah yang terlalu besar dan kompleks untuk ditangani dengan aplikasi perangkat lunak pengolah data tradisional. Dalam menjalankan Analisa menggunakan analisis data besar, ada masalahnya yang muncul yaiu hadirnya data data yang tidak lengkap. Sebuah metode imputasi data diusulkan untuk mengatasi masalah ini seperti interpolasi linier dan metode yang lebih modern dan akurat digunakan pada skripsi ini yang disebut jaringan berlawanan generatif."
Depok: Fakultas Teknik Universitas Indonesia, 2022
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UI - Skripsi Membership  Universitas Indonesia Library
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Arnetta Nandy Pradana
"Artificial intelligence mulai diimplementasikan di Jakarta sebagai solusi untuk mengurai permasalahan lalu lintas, seperti mengotomatisasi pengaturan sinyal lalu lintas dan mengurangi kemacetan. Pemasangan ini merupakan tahap awal pengintegrasian Intelligent Transportation System (ITS) sehingga tantangan operasionalisasi sangat besar. Selain itu, tantangan terkait etika dan perlindungan data pribadi masyarakat juga turut dirasakan. Penelitian ini bertujuan untuk mengetahui persepsi pengguna jalan terhadap implementasi AI pengurai kemacetan, terutama dari sisi efektivitas dan dampaknya. Persepsi publik merupakan salah satu kriteria membentuk kebijakan yang berkelanjutan. Dengan meneliti persepsi, diharapkan pemerintah akan lebih siap untuk menanggapi tuntutan masyarakat dan mengadopsi teknologi di perkotaan. Penelitian ini menggunakan pendekatan kuantitatif dengan teknik pengumpulan data mixed method melalui survei dan wawancara mendalam. Teknik analisis yang digunakan dalam penelitian ini adalah teknik analisis univariat dan metode ilustratif (case clarification). Untuk memetakan persepsi publik terhadap pengimplementasian ITCS, riset ini menggunakan lima dimensi, yaitu Sentiment to AI, Attitude Toward AI Development, Attitude Toward “Impact of AI to Human Society”, Attitude Toward AI Governance, dan Attitude Toward AI Ethics. Hasil penelitian ini menunjukkan persepsi positif pengguna jalan terhadap pengimplementasian ITCS di Jakarta. Namun, beberapa kekurangan masih ditemukan yaitu AI belum maksimal karena belum terintegrasi di seluruh jaringan jalan dan belum dilaksanakannya evaluasi terhadap pengimplementasiannya.

Artificial intelligence is being implemented in Jakarta as a solution to traffic problems, such as automating traffic signal settings and reducing congestion. This installation is the early stage of integrating the Intelligent Transportation System (ITS), and thus the operational challenges are substantial. In addition, there are also challenges related to ethics and the protection of people's personal data. This research aims to understand road users' perceptions of the implementation of AI to alleviate congestion, particularly in terms of its effectiveness and impact. Public perception is crucial for developing sustainable policies. By analyzing these perceptions, the government is expected to be more responsive to public needs and better equipped to adopt urban technologies. The researcher assumes that road users' perceptions of the ITCS are negative, considering the current road conditions and the initial stage of AI implementation. Furthermore, this study uses a quantitative approach with mixed-method data collection techniques through surveys and in-depth interviews. The analytical techniques used in this research are univariate analysis and the illustrative method (case clarification). To map public perception of ITCS implementation, this research employs five dimensions: Sentiment to AI, Attitude Toward AI Development, Attitude Toward "Impact of AI on Human Society," Attitude Toward AI Governance, and Attitude Toward AI Ethics. The results of this study indicate a positive perception of road users towards the use of ITCS in Jakarta. However, some shortcomings are still found, namely that AI has not been maximized due to lack of integration across the entire road network and the absence of an evaluation of its implementation."
Depok: Fakultas Ilmu Administrasi Universitas Indonesia, 2024
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UI - Skripsi Membership  Universitas Indonesia Library