Setiawan Hadi

Istilah data science atau data sains atau ilmu data, whateverlah, saat ini sudah menjadi hal yang biasa terutama di kalangan peneliti, pengajar, maupun di industri yang sehari-harinya berkecimpung dengan data. Saat ini muncul istilah image data. Mungkin istilah ini untuk mempersempit cakupan data science dengan memfokuskan kepada data yang berupa gambar/citra digital dan atau video digital. Sebuah link internet yaitu https://imagescience.org/ memberikan informasi yang terkait ilmu citra atau ilmu gambar ini dalam hal : News, Books, Conferences, Images, Journals, Software, Organizations, Jobs, Tutorials, and Search.

Tahun 2022 telah dilewati dimana even paling berkesan adalah kunjungan ke University of Leiden. Banyak hal diperoleh, ide, inovasi, kerjasama, dll., namun belum sepenuhnya secara langsung terhadap jenjang karir saya yang sudah terlalu lama terhenti. Sampai dengan pertengahan 2023, even yang menarik dan berkesan adalah perubahan aturan kepangkatan dan untuk keluarga adalah perkawinan Karina. Tahun 2023 ditandai dengan perubahan (atau bisa diartikan dengan kemudahan) dalam kenaikan pangkat khususnya ke jenjang guru besar. Banyak faktor yang mungkin bisa memuluskan perjalanan karir seseorang ke jenjang profesor. Banyak profesor baru termasuk di bidang ilmu komputer/informatika, beberapa diantara relatif muda muncul sebagai guru besar yang tentunya akan sangat bermanfaat bagi institusi.

Namun bagi saya tidak berarti karena adanya kegagalan dalam pengajuan paper ke IEEEAccess pada bulan Februari 2023. Namun kegagalan itu bukan menjadi penghambat namun menjadi pemicu dan pemacu untuk lebih semangat untuk mengejar apa yang diharapkan dan berlomba dengan masa pengabdian saya di Unpad. Stretegi-strategi mulai dibangun antara lain (i) komunikasi dengan para profesor dan expert untuk menilai paper sekaligus merevisi yang sudah saya buat (ii) mengingatkan mitra postdoc 2021 mengenai tanggungjawabnya (iii) membangun komunikasi dengan tim AMADI untuk membuat join publication (iv) menyediakan waktu spesial untuk bekerja melihat kembali AMADI (sebelumnya menyerahkan ke tim) (v) meninjau paper end-to-end (vi) memfokuskan data kepada kelengkapan dan kebenaran 66 data digital manuskrip (vii) move on ke DREAM dan sebagainya.

Gimana 2023 ini? Semoga ada keberhasilan.

A smart city uses information and communication technology (ICT) to improve operational efficiency, share information with the public and provide a better quality of government service and citizen welfare.

The main goal of a smart city is to optimize city functions and promote economic growth while also improving the quality of life for citizens by using smart technologies and data analysis. The value lies in how this technology is used rather than how much technology is available.

A city’s smartness is determined using a set of characteristics, including:

• An infrastructure based on technology
• Environmental initiatives
• Effective, practical, and highly functional public transportation
• Confident and progressive city plans
• People are able to live and work within the city, using its resources

The success of a smart city relies on the relationship between the public and private sectors as much of the work to create and maintain a data-driven environment falls outside the local government’s remit. For example, smart surveillance cameras may need input and technology from several companies.

Aside from the technology used by a smart city, data analysts also need to assess the information provided by the smart city systems so that any problems can be addressed and improvements found.

Smart City Definition

There are a number of definitions of what makes a city ‘smart,’ for example, IBM defines a smart city as “one that makes optimal use of all the interconnected information available today to better understand and control its operations and optimize the use of limited resources.”

However, in short, a smart city uses a framework of information and communication technologies to create, deploy and promote development practices to address urban challenges and create a joined-up technologically enabled, and sustainable infrastructure.

Smart City Technologies

Smart cities use a variety of software, user interfaces, and communication networks alongside the Internet of Things (IoT) to deliver connected solutions for the public. Of these, the IoT is the most important. The IoT is a network of connected devices that communicate and exchange data. This can include anything from vehicles to home appliances and on-street sensors. Data collected from these devices is stored in the cloud or on servers to allow for improvements to be made to both public and private sector efficiencies and deliver economic benefits and improvements to the lives of citizens.

Many IoT devices use edge computing, which ensures that only the most relevant and important data is delivered over the communication network. In addition, a security system is implemented to protect, monitor, and control the transmission of data from the smart city network and prevent unauthorized access to the IoT network of the city’s data platform.

Alongside the IoT solutions, smart cities also use technologies including:

• Application Programming Interfaces (APIs)
• Artificial Intelligence (AI)
• Cloud Computing Services
• Dashboards
• Machine Learning
• Machine-to-Machine Communications
• Mesh Networks

Smart City Features

Combining automation, machine learning and the IoT is allowing for the adoption of smart city technologies for a variety of applications. For example, smart parking can help drivers find a parking space and also allow for digital payment.

Another example would be smart traffic management to monitor traffic flows and optimize traffic lights to reduce congestion, while ride-sharing services can also be managed by smart city infrastructure.

Smart city features can also include energy conservation and environmental efficiencies, such as streetlights that dim when the roads are empty. Such smart grid technologies can improve everything from operations to maintenance and plan to power supplies.

Smart city initiatives can also be used to combat climate change and air pollution as well as waste management and sanitation via internet-enabled rubbish collection, bins, and fleet management systems.

Aside from services, smart cities allow for the provision of safety measures such as monitoring areas of high crime or using sensors to enable an early warning for incidents like floods, landslides, hurricanes, or droughts.

Smart buildings can also offer real-time space management or structural health monitoring and feedback to determine when repairs are necessary. Citizens can also access this system to notify officials of any problems, such as potholes, while sensors can also monitor infrastructure problems such as leaks in water pipes.

In addition, smart city technology can improve the efficiency of manufacturing, urban farming, energy use, and more.

Smart cities can connect all manner of services to provide joined-up solutions for citizens.

History of Smart Cities

The concept of smart cities began as far back as the 1960s and 1970s when the US Community Analysis Bureau began using databases, aerial photography, and cluster analysis to collect data, direct resources, and issue reports in order to direct services, mitigate against disasters and reduce poverty. This led to the creation of the first generation of smart cities.

The first generation of smart cities was delivered by technology providers to understand the implications of technology on daily life. This led to the second generation of smart cities, which looked at how smart technologies and other innovations could create joined-up municipal solutions. The third generation of smart cities took the control away from technology providers and city leaders, instead creating a model that involved the public and enabled social inclusion and community engagement.

This third-generation model was adopted by Vienna, which created a partnership with the local Wien Energy company, allowing citizens to invest in local solar plants as well as work with the public to resolve gender equality and affordable housing issues. Such adoption has continued around the world, including in Vancouver, where 30,000 citizens co-created the Vancouver Greenest City 2020 Action Plan.

How Smart Cities Work

Smart cities follow four steps to improve the quality of life and enable economic growth through a network of connected IoT devices and other technologies. These steps are as follows:

1. Collection – Smart sensors gather real-time data

2. Analysis – The data is analyzed to gain insights into the operation of city services and operations

3. Communication – The results of the data analysis are communicated to decision-makers

4. Action – Action is taken to improve operations, manage assets and improve the quality of city life for the residents

The ICT framework brings together real-time data from connected assets, objects, and machines to improve decision-making. However, in addition, citizens are able to engage and interact with smart city ecosystems through mobile devices and connected vehicles and buildings. By pairing devices with data and the infrastructure of the city, it is possible to cut costs, improve sustainability and streamline factors such as energy distribution and refuse collection, as well as offer, reduced traffic congestion, and improved air quality.

Why Smart Cities Are Important

54% of the world’s population lives in cities and this is expected to rise to 66% by 2050, adding a further 2.5 billion people to the urban population over the next three decades. With this expected population growth there comes a need to manage the environmental, social, and economic sustainability of resources. 

Smart cities allow citizens and local government authorities to work together to launch initiatives and use smart technologies to manage assets and resources in the growing urban environment.

Why do we need them?

A smart city should provide an urban environment that delivers a high quality of life to residents while also generating economic growth. This means delivering a suite of joined-up services to citizens with reduced infrastructure costs.

This becomes increasingly important in light of the future population growth in urban areas, where more efficient use of infrastructure and assets will be required. Smart city services and applications will allow for these improvements which will lead to a higher quality of life for citizens.

Smart city improvements also provide new value from existing infrastructure while creating new revenue streams and operational efficiencies to help save money for governments and citizens alike.

Are Smart Cities Sustainable?

Sustainability is an important aspect of smart cities as they seek to improve efficiencies in urban areas and improve citizen welfare. Cities offer many environmental advantages, such as smaller geographical footprints, but they also have some negative impacts, including the use of fossil fuels to power them. However, smart technologies could help alleviate these negative effects, such as through the implementation of an electric transport system to reduce emissions. Electric vehicles could also help to regulate the frequency of the electric grid while not in use. 

Such sustainable transport options should also see a reduction in the number of cars in urban areas as autonomous vehicles are expected to reduce the need for car ownership amongst the population.

Creating such sustainable solutions could deliver environmental and societal benefits.

Smart City Challenges

For all of the benefits offered by smart cities, there are also challenges to overcome. These include government officials allowing widespread participation from citizens. There is also a need for the private and public sectors to align with residents so that everyone can positively contribute to the community.

Smart city projects need to be transparent and available to citizens via an open data portal or mobile app. This allows residents to engage with the data and complete personal tasks like paying bills, finding efficient transportation options, and assessing energy consumption in the home.

This all requires a solid and secure system of data collection and storage to prevent hacking or misuse. Smart city data also needs to be anonymized to prevent privacy issues from arising.

The largest challenge is quite probably that of connectivity, with thousands or even millions of IoT devices needing to connect and work in unison. This will allow services to be joined up and ongoing improvements to be made as demand increases. 

Technology aside, smart cities also need to account for social factors that provide a cultural fabric that is attractive to residents and offers a sense of place.  This is particularly important for those cities that are being created from the ground up and need to attract residents.

Are they Secure?

Smart cities offer plenty of benefits to improve citizen safety, such as connected surveillance systems, intelligent roadways, and public safety monitoring, but what about protecting the smart cities themselves?

There is a need to ensure smart cities are protected from cyber attacks, hacking, and data theft while also making sure the data that is reported is accurate.

In order to manage the security of smart cities there is a need to implement measures such as physical data vaults, resilient authentication management, and ID solutions. Citizens need to trust the security of smart cities which means the government, private sector enterprises, software developers, device manufacturers, energy providers, and network service managers need to work together to deliver integrated solutions with core security objectives. These core security objectives can be broken down as follows:

1. Availability – Data needs to be available in real-time with reliable access in order to make sure it performs its function in monitoring the various parts of the smart city infrastructure
2. Integrity – The data must not only be readily available, but it must also be accurate. This also means safeguarding against manipulation from outside
3. Confidentiality – Sensitive data needs to be kept confidential and safe from unauthorized access. This may mean the use of firewalls or the anonymizing of data
4. Accountability – System users need to be accountable for their actions and interaction with sensitive data systems. Users logs should record who is accessing the information to ensure accountability should there be any problems

Legislation is already being put in place in different nations, such as the IoT Cybersecurity Improvement Act in the United States to help determine and establish minimum security requirements for connected devices in smart cities.

Examples

Cities across the world are in different stages of smart technology development and implementation. However, there are several who are ahead of the curve, leading the path to creating fully smart cities. These include:

• Barcelona, Spain
• Columbus, Ohio, USA
• Dubai, United Arab Emirates
• Hong Kong, China
• Kansas City, Missouri, USA
• London, England
• Melbourne, Australia
• New York City, New York, USA
• Reykjavik, Iceland
• San Diego, California, USA
• Singapore
• Tokyo, Japan
• Toronto, Canada
• Vienna, Austria

The city-state of Singapore is considered to be one of the front-runners in the race to create fully smart cities, with IoT cameras monitoring the cleanliness of public spaces, crowd density, and the movement of registered vehicles. Singapore also has systems to monitor energy use, waste management, and water use in real-time. In addition, there is autonomous vehicle testing and a monitoring system to ensure the health and well-being of senior citizens. 

Elsewhere, Kansas City has introduced smart streetlights, interactive kiosks, and over 50 blocks of free Wi-Fi. Parking space details, traffic flow measurements, and pedestrian hotspots are also all available to residents via the city’s data visualization app.

San Diego, meanwhile, has installed 3,200 smart sensors to optimize traffic flow and parking as well as enhance public safety and environmental awareness. Electric vehicles are supported by solar-to-electric charging stations and connected cameras monitor for traffic problems and crime.

Traffic monitoring systems are also in place in Dubai, which has telemedicine and smart healthcare solutions as well as smart buildings, utilities, education, and tourism options. Barcelona also has smart transportation systems with bus stops offering free Wi-Fi and USB charging ports, along with a bike-sharing program and a smart parking app including online payment options. Temperature, pollution, and noise are also measured using sensors that also cover humidity and rainfall.

Conclusion

Creating smart connected systems for our urban areas provides a great many benefits for citizens around the world, not only to improve quality of life but also to ensure sustainability and the best possible use of resources.

These solutions are dependent on a unified approach from the government as well as the private sector and residents themselves. With the correct support and infrastructure, however, smart cities can use advances such as the Internet of Things to enhance the lives of residents and create joined-up living solutions for the growing global urban citizenry.

Source: https://www.twi-global.com/technical-knowledge/faqs/what-is-a-smart-city

Bersepeda merupakan moda transportasi yang lumrah di kota Leiden. Hampir sebagian besar penduduk Leiden senang bersepeda. Infrastruktur bersepeda tersedia secara lengkap, mulai dari jalan raya dan rambu-rambunhya, tempat p;arkir hingga persewaan. Pada bulan September 2022 kami berada di Leiden Belanda dalam rangka program RISE SMA Uni Eropa. Tepatnya di Leiden Institute for Advanced Computer Science (LIACS) University of Leiden Belanda.  Penulis juga termasuk salah seorang yang menggunakan sepeda untuk perjalanan sehari-hari khususnya dari tempat tinggal menuju ke LIACS. Video-video tersebut bisa dilihat pada link ini.

Ada kemungkinan file PDF yang kita terima tidak berada dalam posisi yang bagus, tetapi miring sehingga penampilannya kurang representatif. Jika isi file PDF diputar dengan sudut istimewa, seperti 90, 180, atau 270 derajat, maka caranya bisa dilakukan menggunakan aplikasi PDF editor. Tetapi bagaimana kalau isi PDF ingin diputar dengan sudut tertentu? Ada cara dimana file PFD dikonversikan dulu ke gambar/image, lalu diputar sesuai kebutuhan, lalu dikembalikan ke PDF. Ini bisa saja dilakukan. Namuna ada cara lain untuk memutar isi PDF file. Dengan LaTeX. Dengan kode ini, maka kita akan bisa memutar file PDF dengan sudut putar sesuai dengan kebutuhan kita.

\documentclass{article}
\usepackage{pdfpages}
\begin{document}
\includepdf[pages={-},angle=0.8]{namafile}
\end{document}


Kode diatas berarti isi file PDF namafile diputar dengan sudut 0.8 derajat counterclockwise. Lalu, bagaimana caranya?

Pada tanggal 6-14 Juli 2022 diadakan International Conference of Mathematicians (ICM) di St Petersburg Russia. Kesempatan terbuka untuk mengikuti kegiatan ICM 2022 ini karena adanya pembiayaan yang ditawarkan yaitu Chebyshev grant. Tawaran mulai dibuka pada Mei 2021 melalui pendaftaran dan pemenuhan berbagai persyaratan yang diperlukan serta dikirimkan secara online. Setelah menunggu beberapa lama, pada akhir Desember 2021, diumumkan penerima grant Chebyshev. Persiapan perlu dilakukan sebaik-baiknya karena perjalanan dilakukan pada masa Covid-19 dan ditengah-tengah kesibukan pekerjaan yang meningkat. Semoga berhasil. Keikutsertaan ini diarsipkan dalam situs ini.

Saat ini perkembangan Teknologi Informasi berjalan begitu cepat, tidak terkecuali dalam aplikasi-aplikasi yang digunakan untuk pengembangan (application development tools). Berbagai kemudahan diperoleh sehingga dapat menghasilkan yang aplikasi yang komplit dan memiliki kemampuan yang luar biasa. Tidak sedikit aplikasi-aplikasi besar dihasilkan oleh orang yang ‘kurang’ memiliki latar belakang komputer.

Kadang-kadang karena terbuai dengan kemudahan, para mahasiswa melupakan hal-hal konseptual dan dasar dalam hal pemrograman. Untuk itu, sebagai dosen yang mengajar para mahasiswa dengan keilmuan informatika, diperlukan sesuatu yang lebih untuk diajarkan kepada mahasiswa informatika sehingga selain mampu membangun aplikasi modern menggunakan library atau API yang tersedia, mahasiswa juga memiliki kemampuan pemrograman fundamental secara scratch. Ini akan menjadi modal dasar yang kuat bagi mahasiswa dalam hal memahami konsep informasi secara komprehensif dan tidak sekedar pemakai saja.

Berikut ini adalah langkah-langkah singkat untuk membuat program dari awal pada sistem operasi Windows menggunakan bahasa pemrogram C# (C Sharp). Dalam hal ini hanya digunakan editor biasa dan compiler C# secara minimal. File minimal yang diperlukan adalah compiler C# dengan nama csc.exe dan cscui.dll. Kedua file dapat diunduh pada link ini. Sebuah file hello.cs juga bisa dijadikan contoh untuk membuat program C# dari nol.

Langkah-langkahnya sbb:

1. unduhlah compiler C# pada link di atas
2. unzip dan tempatkan file csc.exe dan cscui.dll pada folder yang anda pilih, juga file hello.cs.
3. masuklah ke folder yang anda pilih melalui command prompt
4. compile dengan cara menuliskan csc hello.cs, maka akan dihasilkan hello.exe
5. run program hello.exe dengan menuliskan hello pada command prompt
6. jika berhasil maka anda sudah membuat program C# dari scratch.

Selamat mencoba.

Pada tahun 2022 Universitas Padjadjaran, baik dari fihak Rektor maupun Dekan, memberikan penghargaan kepada Kepala Departemen dalam bentuk Kalender 2022, Mug Kampus Merdeka, Buku Harian 2022, dan T-Shirt. Terima kasih kami sampaikan kepada Ibu Rektor dan Bapak Dekan atas penghargaan ini.

Sumber : https://hackr.io

The words data science and machine learning are often used in conjunction, however, if you are planning to build a career in one of these, it is important to know the differences between machine learning and data science.

Before doing so, we need to understand a few important terms that are related but different.

AI (Artificial intelligence) – AI or machine intelligence refers to the intelligent decisions made by machines at par with their human counterparts. It is a study where we enable machines to learn through experience and make it intelligent enough to perform human-like tasks. In my article about AI vs ML, I have listed the differences between AI and Machine learning. For this article, let me give you a simple definition of machine learning.

Machine learning – Think of ML as a subset of AI. Same way as humans learn with experience, machines can learn with data (experience) rather than just following simple instructions. This is called as machine learning. Machine learning uses 3 types of algorithms – supervised, unsupervised and reinforced.

Deep learning – Deep learning is a part of Machine learning, which is based on artificial neural networks (think of neural networks similar to our own human brain). Unlike machine learning, deep learning uses multiple layers and structures algorithms such that an artificial neural network is created that learns and makes decisions on its own!

Big Data – Humongous sets of data that can be computationally analyzed to understand and process trends, patterns and human behavior.

Data Science – How is all the big data analyzed? Fine, the machine learns on its own through machine learning algorithms – but how? Who gives the necessary inputs to a machine for creating algorithms and models? No points for guessing that it is data science. Data Science is a uses different methods, algorithms, processes, and systems to extract, analyze and get insights from data.

Check out our exciting data science tutorials here.

If we were to see the relationship between all the above in a simple diagram, this is how it would look like this picture.

Artificial Intelligence (AI)

Artificial Intelligence includes both Machine learning and Data science which are correlated. Thus, data science is also a part (the most popular and most important one) of AI.

As we see above, Data science and machine learning are closely related and provide useful insights and generate the necessary trends or ‘experience’. In both, we use supervised methods of learning i.e. learning from huge data sets.

How are both correlated?

Data Science is a broader field of study that uses algorithms and models of machine learning to analyse and process data. Apart from learning, data science also involves data integration, visualization, data engineering, deployment and business decisions.

Data Science vs Machine learning

So, what’s the difference?
On one hand, data science focuses on data visualization and a better presentation, whereas machine learning focuses more on the learning algorithms and learning from real-time data and experience.

Always remember – data is the main focus for data science and learning is the main focus for machine learning and that is where the difference lies.

To appreciate this difference more, let us take a use case and see how both data science and machine learning can be used to achieve the results we want –

Let us say you want to purchase a phone on xyz.com. This is the first time you are visiting xyz.com and you are browsing through phones of all ranges. You use various filters to narrow down your preferences and out of the results you get, you choose 4-5 of the phones and compare those. Once you select a phone model, you will see a recommendation below the product – for a similar product in a lesser price or with more features, or related accessories for the phone you have chosen and so on. How does the website recommend you these things? It has no history about you!

That’s through the data from millions of other people who may have tried to purchase the same phone, and searched/bought other accessories along. This makes the system automatically recommend the same to you.

The entire process of collection of data from the users, cleaning and filtering out the required data for evaluation, evaluation of the filtered data for building patterns, finding similar trends and building a model for a recommendation of the same thing to other users and finally the optimization – is data science.

Where is machine learning in all this? Well, how do you build a model? Through machine learning algorithms. Based on the data collected and trends generated, the machine understands that these are the accessories that are usually bought by other users with a particular phone. Hence, it suggests you the same thing based on what it has ‘experienced’ before.

Data Science (DS)

The modeling (second last) step is the most critical step because that is what improves the overall business and makes the machine understand human behavior. If the right machine learning model is applied, it could mean more progressive learning for the machine as well as success for the business model.

This step is called as the data modeling step – which is essentially the machine learning phase of the data science lifecycle.

Data modeling – how does machine learning work?
There are different types of machine learning algorithms, the most common being clustering, matrix factorization, content-based, recommendations, collaborative filtering and so on. Machine learning involves the 5 basic steps –

The huge set of data that we receive in the first step is split into the training set and testing set and the model is built and test using the training set. A significant portion of data is used for training purposes so that different conditions of input and output can be achieved and the model built is closest to the required result (recommendation, human behavior, trends, etc…).

Once built, the model is tested for efficiency and accuracy using the test data so that it can be cross-validated.

As we can see, Machine Learning comes into picture only during the data modeling phase of the Data Science lifecycle. Data Science thus contains machine learning.

With machine learning, the machine can generate complex mathematical algorithms that need not be programmed by a human, and further can improvise and improve the programs all by itself. When compared to the traditional statistical analysis techniques, machine learning evolves as a better way of extraction and processing the most complex sets of big data, thereby making data science easier and less chaotic.

Furthermore, machines tend to be more accurate and have a better memory than humans, they can learn and produce accurate results based on experiences. We get fast algorithms and data-driven models without the errors that are possible by humans.