AbstrakKeyword : BRT, graphdatabase PENDAHULUANMasyarakat di beberapa kota di Indonesia saat ini telah memiliki alternatif transportasi massal yang dinamakan dengan Bus Rapid Transit (BRT). BRT hadir dengan berbagai nama seperti Trans Jakarta (Jakarta), Trans Semarang (Semarang), Trans Jogja (Yogyakarta), Batik Solo Trans (Solo), Trans Musi (Palembang) dan yang lainnya. Hadirnya BRT merupakan upaya untuk menyediakan transportasi yang dapat menambah penggunaan transportasi umum, menambah tingkat dan kualitas layanan transportasi publik (Mzee & Chen, 2010). Sistem BRT di Indonesia memiliki shelter / halte tertentu. Shelter tersebut memiliki beberapa tipe, misalnya transit point dan transfer point. Selain itu, setiap bus pada BRT memiliki ruterute perjalanan tertentu yang biasanya disebut dengan koridor, di mana setiap bus melewati shelter yang telah ditentukan. Dengan demikian BRT merupakan sebuah jaringan transportasi. Pemodelan data terkait dengan jaringan transportasi ini penting ketika akan membuat sebuah program/aplikasi komputer. Basis data relasional telah lama menjadi standar penyimpanan data pada program/aplikasi komputer. Dalam basis data relasional, data disimpan dalam tabel dan kolom di mana antar tabel dapat saling dikaitkan melalui kunci. Apabila diinginkan data dari tabel yang berbeda, dilakukan permintaan (query) menggunakan klausa JOIN. Untuk jaringan transportasi, jika disimpan dalam basis data relasional, akan memerlukan banyak operasi JOIN query yang menyebabkan komputasi tinggi (Dominguez-Sal et al., 2010). Dengan demikian performa aplikasi dapat menurun. Graphdatabase dapat menjadi solusi alternatif untuk menyimpan data jaringan transportasi. Hal tersebut disebabkan karena graphdatabase menyimpan data seperti halnya graph, yaitu dalam node-node yang berhubungan satu sama lain. Artinya, analisis jaringan transportasi dapat
Sub-District Government as a Public Bodies is such a state administrator in accordance to the law required to apply disclosure, either by publishing information proactively, and or providing information application services. Implementation of e-Government is one solution that can be used to improve performance in running the mandate of the law. E-Government requires collaboration from various Public Bodies, especially in data exchange, information sharing, and processes. E-Government Interoperability is the development of inter e-Government systems for sharing and integrating information using shared standards. The success of e-Government Interoperability is determined by strategies, policies, and architectures that enable data, information technology systems, business processes, and service lines to integrate precisely and efficiently. The architectural model generated from this study illustrates the structure of e-Government Interoperability, the basic organization of system components, the relationship of one component with other components and the environment. Model validation uses result approach/theory analysis for data standardization, solid platforms, easier access to information, and efficient administration and services. The architectural model can serve as a guide for design and evolution, in an effort to create a system for public information services, especially at the sub-district level. It was concluded that the model was able to realize the development of sub-district vertical database integration and single-sign-on.
Background: Semarang has broad area that cannot be covered entirely by single transportation mode. To reach a specific location, people often use more than one public transportation mode. Apart from Bus Rapid Transit, another exist namely angkot or city transportation. Multimodal traveler information is then required to help passenger searching for a route. Several studies of multimodal traveler information system has been conducted, however the data model for multimodal transportation did not conceived in detail.Objective: Proposes a database of multimodal transportation design using graph data model by taking Semarang as a case study.Method: We create our model in oriented entity-relationship diagram (O-ERD) and map this O-ERD to the graph database schema.Result: We develop our data model in graph database schema and we implement the model using Neo4J graph database for validation purpose. Our model consist of three graph node label namely Shelter, Angkot Stopper, and Closer Place. To validate our model, we execute a search query using the Cypher query to look for location with closer place to it.Conclusion: Our data model was successfully developed and implemented. Searching transportation route in the implementation of our model has been conducted using cypher query. It can successfully display all possible paths and routes. Our query can distinguish between one mode of transportation with another.Keywords: Graph database, Multimodal transportation, Neo4j, Cypher
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