Setiawan Hadi

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.

Sumber : https://hackr.io

Both machine learning and deep learning are forms of artificial intelligence, however, with some notable differences. While machine learning is a specific application of AI, deep learning is a distinctive form of ML.

In order to make the most out of them, it is important to know how the two subsets of AI differ. Before discussing the various prominent differences between machine learning and deep learning, let’s first get a brief idea about AI, followed by brief descriptions of the two contenders.

AI – This is Where Everything Starts From!

In a less abstract sense, AI or artificial intelligence refers to that branch of study and research that deals with imparting a machine with human-like cognitive ability. Hence, it is important to understand and learn artificial intelligence before delving into ML or DL.

For now, we are still in the premature stages of AI. What this means is that machines that can have reasoning, speech, and understanding levels comparable to human beings are a distant reality. Any AI-powered machine can be listed among one of the three categories:

• Narrow AI – An AI-powered machine comes under the narrow AI classification if it can perform some specific task better than humans. Currently, we have some artificially intelligent machines that are superior to humans in doing certain tasks.
• General AI – The next step in AI classification is general AI. It is the stage when an AI-powered machine or computer is able to carry out an intellectual task with the same level of accuracy as performed by a human.
• Active AI – An artificially intelligent system is one that is superior to human beings in terms of performing several tasks.

Deep learning is a subset of machine learning, while machine learning itself is the subset of artificial intelligence.

Machine Learning – A Specialized Form of AI

Machine learning or ML is a subset of AI. The most fascinating aspect of machine learning is its ability to modify itself when new data is available. This means that ML is dynamic in nature and doesn’t necessitate for human intervention for making changes or modifications.

ML has proven to be a great tool for analyzing and identifying patterns in datasets of varying sizes. The main idea of machine learning is to train a computer or machine to automate tasks that are either exhaustive, impossible, or redundant for an individual to perform.

According to Arthur Samuel, a pioneer in machine learning, ML is a,

“Field of study that gives computers the ability to learn without being explicitly programmed.”

This suggests that machine learning programs aren’t explicitly entered into a computer using if-then and other statements.

In a way, ML programs adjust themselves as a response to the data to which they are exposed to. Although learning ML doesn’t necessitate for having prior AI knowledge, it certainly helps to have a good understanding of AI.

Machine learning makes use of data to feed an algorithm capable to understand the relationship between certain input and output. As soon as the machine completes learning, it can predict either the value or the class of the new data point.

Deep Learning – A Specialized Form of ML

A deep learning model is a software that is capable of mimicking the network of neurons found in the human brain. Typically deep learning refers to mostly deep artificial neural networks and rarely to deep reinforcement learning.

The term ‘deep’ in deep learning signifies the number of layers in a neural network. The more layers a neural network has, the deeper it is said to be.

A deep learning machine makes use of various layers to learn from the data provided. While a shallow network has only one hidden layer, a deep network has multiple layers. A typical deep neural network has three types of layers:

• The input layer
• The hidden layer
• The output layer

As a set of algorithms, deep artificial neural networks has set unprecedented records in terms of accuracy for several important problems. These include image recognition, natural language processing, recommender systems, and sound recognition.

In fact, deep learning is responsible for the creation of DeepMind’s critically-acclaimed AplhaGo algorithm. In 2016, the deep learning algorithm was able to defeat former Go world champion Lee Sedol.

Multiple hidden layers let deep neural networks learn features of available data in a feature hierarchy. Simple features, such as pixels, recombine multiple times in the next layer to yield more complex features, such as lines and shapes.

One of the distinctive features of deep learning is computational intensity. This is why powered-up GPUs are required for training deep learning models.

Machine Learning vs Deep Learning: The Face-Off!

Execution Time
An important distinction between machine learning and deep learning can be drawn in terms of execution time. A typical machine learning algorithm can take anything between less than a minute to a few hours for finishing execution.

Unlike machine learning algorithms, deep learning algorithms require up to several weeks to finish execution. This is due to the fact that a deep artificial neural network requires computing a significantly large number of weights and additional parameters.

Feature Engineering
In feature engineering, domain knowledge is used for creating feature extractors. These reduce the complexity of the data as well as enhance the visibility of patterns. The trade-off for the benefits of feature engineering is that it is time-consuming and requires a high level of expertise.

In the case of deep learning algorithms, there is no requirement for understanding the features or best feature that represents the data. To put simply, DL algorithms don’t require feature engineering. However, the inverse is true for machine learning algorithms.

Hardware Dependencies
While ML algorithms work well on low-end machines, deep learning algorithms necessitate for powerful machines with multiple GPUs. DL algorithms need to compute a significant amount of matrix multiplication, which results in them to demand high-spec systems.

Interpretability
Machine learning comprises of an array of algorithms. While some of them are easy to interpret, such as decision tree and logistic, others are almost impossible to interpret, including SVM and XGBoost. Thus one can say the interpretability of ML varies from easy to impossible.

In the context of deep learning, the interpretability is difficult to impossible. This is the primary reason why implementing deep learning in industrial applications is still a rarity.

Performance
Machine learning algorithms perform exceptionally well on a dataset that is small, medium, or somewhere in between. On the contrary, DL algorithms fail to perform well for such datasets. Instead, they perform better for bigger datasets.

The Approach
Machine learning algorithms are used for parsing data, learning from that data, and make informed decisions based on this very learning. On the contrary, deep learning is used for creating an artificial neural network, capable of learning and making intelligent decisions by itself.

Some Notable Applications of ML and DL

• Computer Vision – Computer vision makes use of ML as well as DL algorithms. Implementations of computer vision include facial recognition and number plate identification.
• Healthcare – Machine learning and deep learning models have promising applications in healthcare. There has been extensive ongoing research in several facets of the medical field like anomaly detection and cancer identification.
• Information Retrieval – Another important application of artificially intelligent learning models is information retrieval. This typically refers to search engines with the ability to seek and present appropriate results for image search, text search, and even audio search.
• Marketing – Automated email marketing, as well as target identification, can benefit from machine learning and deep learning models.

Conclusion

Both machine learning and deep learning are specialized forms of artificial intelligence undergoing extensive research and offering continuously evolving applications.

Although each of them has several independent applications, the combination of the two facets of AI has demonstrated to reach new heights of success in various research fields.

Hope this article will help you build a clear understanding of the various important differences between deep learning and machine learning models.

Source : https://www.mygreatlearning.com

What is Machine Learning?

Machine Learning is a subsection of Artificial intelligence that devices means by which systems can automatically learn and improve from experience. This particular wing of AI aims at equipping machines with independent learning techniques so that they don’t have to be programmed to do so, this is the difference between AI and Machine Learning.

Machine learning involves observing and studying data or experiences to identify patterns and set up a reasoning system based on the findings. The various components of machine learning include:

• Supervised machine learning: This model uses historical data to understand behaviour and formulate future forecasts. This kind of learning algorithms analyse any given training data set to draw inferences which can be applied to output values. Supervised learning parameters are crucial in mapping the input-output pair.
• Unsupervised machine learning: This type of ML algorithm does not use any classified or labelled parameters. It focuses on discovering hidden structures from unlabeled data to help systems infer a function properly. Algorithms with unsupervised learning can use both generative learning models and a retrieval-based approach.
• Semi-supervised machine learning: This model combines elements of supervised and unsupervised learning yet isn’t either of them. It works by using both labelled and unlabeled data to improve learning accuracy. Semi-supervised learning can be a cost-effective solution when labelling data turns out to be expensive.
• Reinforcement machine learning: This kind of learning doesn’t use any answer key to guide the execution of any function. The lack of training data results in learning from experience. The process of trial and error finally leads to long-term rewards.

Machine learning delivers accurate results derived through the analysis of massive data sets. Applying AI cognitive technologies to ML systems can result in the effective processing of data and information. But what are the key differences between Data Science vs Machine Learning and AI vs ML? Continue reading to learn more.

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Source : https://www.mygreatlearning.com

What is Artificial Intelligence?
AI, a rather hackneyed tech term that is used frequently in our popular culture – has come to be associated only with futuristic-looking robots and a machine-dominated world. However, in reality, Artificial Intelligence is far from that.

Simply put, artificial intelligence aims at enabling machines to execute reasoning by replicating human intelligence. Since the main objective of AI processes is to teach machines from experience, feeding the right information and self-correction is crucial. AI experts rely on deep learning and natural language processing to help machines identify patterns and inferences.

Scope of Artificial Intelligence

• Automation is easy with AI: AI allows you to automate repetitive, high volume tasks by setting up reliable systems that run frequent applications.
• Intelligent Products: AI can turn conventional products into smart commodities. AI applications when paired with conversational platforms, bots and other smart machines can result in improved technologies.
• Progressive Learning: AI algorithms can train machines to perform any desired functions. The algorithms work as predictors and classifiers.
• Analysing Data: Since machines learn from the data we feed them, analysing and identifying the right set of data becomes very important. Neural networking makes it easier to train machines.

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Source : https://www.mygreatlearning.com

What is Data Science?

You must have wondered, ‘What is Data Science?’, Data science is a broad field of study pertaining to data systems and processes, aimed at maintaining data sets and deriving meaning out of them. Data scientists use a combination of tools, applications, principles and algorithms to make sense of random data clusters. Since almost all kinds of organizations today are generating exponential amounts of data around the world, it becomes difficult to monitor and store this data. Data science focuses on data modelling and data warehousing to track the ever-growing data set. The information extracted through data science applications are used to guide business processes and reach organisational goals.

Scope of Data Science

One of the domains that data science influences directly is business intelligence. Having said that, there are functions that are specific to each of these roles. Data scientists primarily deal with huge chunks of data to analyse the patterns, trends and more. These analysis applications formulate reports which are finally helpful in drawing inferences. A Business Intelligence expert picks up where a data scientist leaves – using data science reports to understand the data trends in any particular business field and presenting business forecasts and course of action based on these inferences. Interestingly, there’s also a related field which uses both data science, data analytics and business intelligence applications- Business Analyst. A business analyst profile combines a little bit of both to help companies take data driven decisions.

Data scientists analyse historical data according to various requirements, by applying different formats, namely:

• Predictive causal analytics: Data scientists use this model to derive business forecasts. The predictive model showcases the outcomes of various business actions in measurable terms. This can be an effective model for businesses trying to understand the future of any new business move.
• Prescriptive Analysis: This kind of analysis helps businesses set their goals by prescribing the actions which are most likely to succeed. Prescriptive analysis uses the inferences from the predictive model and helps businesses by suggesting the best ways to achieve those goals.

Data science uses a wide array of data-oriented technologies including SQL, Python, R, and Hadoop, etc. However, it also makes extensive use of statistical analysis, data visualization, distributed architecture, and more to extract meaning out of sets of data.

Data scientists are skilled professionals whose expertise allows them to quickly switch roles at any point in the life cycle of data science projects. They can work with Artificial Intelligence and machine learning with equal ease. In fact, data scientists need machine learning skills for specific requirements like:

• Machine Learning for Predictive Reporting: Data scientists use machine learning algorithms to study transactional data to make valuable predictions. Also known as supervised learning, this model can be implemented to suggest the most effective courses of action for any company.
• Machine Learning for Pattern Discovery: Pattern discovery is important for businesses to set parameters in various data reports and the way to do that is through machine learning. This is basically unsupervised learning where there are no pre-decided parameters. The most popular algorithm used for pattern discovery is Clustering.

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Source : https://www.mygreatlearning.com

While the terms Data Science, Artificial Intelligence (AI) and Machine learning fall in the same domain and are connected to each other, they have their specific applications and meaning. There may be overlaps in these domains every now and then, but essentially, each of these three terms has unique uses of its own. Here are the brief explanation about Data Science vs Machine Learning vs AI.

1. What is Data Science?
2. What is Artificial Intelligence?
3. What is Machine Learning?
   

You can watch these videos related to this topic.

Metaverse saat ini sedang menjadi topik yang hangat didunia Teknologi Informasi. Beberapa perusahaan terutama perusahaan besar seperti Facebook dan Microsoft sudah melakukan riset dan implementasi metaverse sebagai bagian dari kegiatan mereka. Sampai saat ini metaverse masih belum sempurna namun secara pasti akan menjelma menjadi environment dan istilah yang umum bagi publik. Perkembangan yang luar biasa dalam bidang Augmented Reality (AR) dan Virtual Reality (VR) mengakselerasi pengembangan metaverse yang diyakini akan matang dalam beberapa tahun mendatang.

Definisi metaverse secara sederhana adalah aktivitas berbagi ruang dan fasilitas virtual dalam konteks yang luas dan hampir tak terbatas, dimana ruang tersebut memiliki karakteristik hiper-realistik, imersif, dan interaktif yang dibangun awalnya melalui teknologi AR dan VR. Hiper-realistik berarti ruang dan fasilitas virtual tersebut bersifat nyata dan memiliki fungsi, suasana, tampilan, dan rasa yang sama seperti ruang dan fungsi yang umum/biasa. Imersif berarti dunia digital atau dunia simulasi akan menjadi dunia nyata sehingga penggunanya bisa merasakan suasana yang sama dengan dunia nyata. Interaktif berarti pengguna bisa melakukan aktivitas dan interaksi di ruang virtual sama seperti yang biasa dilakukan pada dunia nyata. Salah satu kelebihan metaverse adalah adanya koneksi antar dunia virtual yang satu dengan dunia virtual yang lain menyerupai kegiatan aktivitas sehari-hari misalnya belanja, rapat dan pertemuan secara resmi di kantor ataupun santai dengan rekan-rekan, nonton film atau acara konser, bahkan sampai kepada kegiatan kolaborasi yang semuanya dikerjakan dan dijalankan dalam dunia virtual yang terkoneksi dalam sebuah metaverse.

Beberapa ciri unik dari metaverse adalah (a) tak dibatasi (boundless), tidak ada batas fisik (b) selalu aktif atau persisten, bisa masuk dan keluar kapan saja dan dimana saja (c) imersif melalui rasa dan suasana yang lebih nyata karena penggunaan peralatan eXtended Reality (XR) (d) desentralisasi, tidak dimiliki/dikuasai oleh satu perusahaan saja, setiap pengguna dapat membuat dunia virtualnya kemudian mengkoneksinya dengan dunia virtual lainnya (e) sistem ekonomi baru dengan penggunaan mata uang digital (f) relasi sosial yang menjadi semakin nyata dikarenakan adanya link dan kolaborasi yang menghubungkan satu dengan dengan lain.

Bagi yang mau mencoba metaverse, walaupun masih dalam tahap awal, silakan akses situs Second Life, Sensorium, Axie Infinity. Fornite, Sandbox, dan masih banyak lagi.

Memang saat ini fokus metaverse masih pada implementasi permainan (games) serta terasa masih kaku dan tidak 100% realistik, namun lambat laun pasti akan disempurnakan dan berbagai aplikasi nyata akan dibuat sehingga metaverse menjadi teknologi yang nyata dan memiliki kontribusi yang lebih positif bagi kehidupan manusia.

Wabah Covid 19 telah mengubah bentuk pembelajaran di perguruan tinggi yang sebelumnya disampaikan melalui tatap muka di kelas menjadi tatap layar alias secara daring. Perubahan ini membawa angin perubahan baru sekaligus membuka cakrawala dan oportunitas baru di dunia pendidikan tinggi. Gabungan format pembelajaran antara daring dengan luring menjadi solusi yang paling optimal pada situasi sekarang ini.

Dengan teknologi yang ada saat ini, tidak semua mata kuliah dapat disajikan secara daring, termasuk didalamnya kegiatan di laboratorium dan aktivitas yang membutuhkan keterlibatan fisik. Namun sekarang sudah muncul teknologi yang disebut metaverse. Dengan teknologi ini maka ruang dan suasana virtual yang kaku dan kurang realistik, dengan dukungan kecerdasan artifisial (AI) terbaru, akan mentransfomasikan dunia nyata ke dalam dunia virtual yang memiliki karakteristik yang sama.

Pembelajaran Daring
Pembelajaran secara daring dalam hal ini bisa diartikan secara lebih luas lagi dalam artian bekerja secara daring. Bukan hanya tatap muka secara daring saja tetapi juga mahasiswa mengerjakan pekerjaannya dan mensubmitnya secara daring melalui LMS yang tersedia. Selain itu mahasiswa juga mengeksplorasi materi-mataeri yang disediakan maupun materi-materi pelengkap yang banyak sekali tersedia di internet. Selain itu mahasiswa bisa mendengarkan ulang pengajaran yang sudah direkam sebelumnya sesuai dengan waktu dan kesempatan yang tersedia. Dengand emikian karakteristik pendidikan ini bersifat learner-centric sehingga memberikan kepuasaan optimal bagi mahasiswa itu sendiri.

Dalam konteks pengalaman belajar mahasiswa, maka setiap mahasiswa akan meiliki kebebasan atau istilahnya kemerdekaan dalam menentukan learning-path. Namun kembali kepada permasalahan sebelumnya tentang pembelajaran daring, walaupun handout dan bahan tersedia, namun tetap timbul pertanyaan khususnya bagi mahasiswa dalam bidang kesehatan, teknik, arsitektur, dan masih banyak lagi yang membutuhkan kondisi nyata khususnya dalam laboratorium dan workshop. Nah, disinilah metaverse akan mengambil alih.

Mengapa Metaverse
Teknologi augmented dan virtual reality, teknologi eye-tracking serta teknologi pengenalan objek dan gambar, sudah disadari telah menjadi elemen penting dalam membangun peralatan daring yang menyerupai keadaan nyata; namun dengan metaverse, yang saat ini masih menggunakan avatar, seseorang bisa berada dalam dunia virtual yang berbeda-beda misalnya suatu waktu ada di dalam toko, kemudian berpindah ke kampus atau kelas. Hal ini disebut juga dengan pengalaman belajar imersif atau immersive learning experience.

Perguruan tinggi tidak boleh berhenti pada pembuatan materi kuliah yang disampaikan secara daring saja, namun materi kuliah, dalam bentuk multimedia, rekaman, materi tertulis, podcast, dan lain sebagainya harus bisa daksis oleh mahasiswa kapans aja tidak dibatasi waktu, dengan kata lain mahasiswa bisa melakukan teleportasi ke mana saja yang dia inginkan. Misalnya mahasiswa dapat mengikuti kuliah dan kegiatan bedah jantung virtual dan bisa mengikuti atau mengulang kembali samapai mahasiswa tersebut memahamai dan mampu melakukannya. Juga hal sama bisa dilakukan bagi mahasiswa pada bidang studi lainnya. Lebih dari pada itu, karena proses perpindahan dilakukan secara daring maka aktivitas fisik dikurangi, selain itu juga untuk mahasiswa di berbagai belahan dunia dapat mengikuti perkuliahan dan aktivitas lainnya di kampus secara virtual.

Metaverse memiliki kemampuan utnuk membantu pembuatan materi perkuliahan yang membawa pada pengalaman belajar yang bersifat imersif dan bersifat edutainment. Edutainment adalah belajar melalui entertainment dan playing. Melalui pembuatan games yang didalamnya terkandung pembelajaran, maka format ini akan bisa dibuat menggunakan metaverse. Dengan demikian selain akan membangun lingkungan baru untuk pembelajaran mahasiswa (a new leaning environment), metaverse akan membangun lingkungan pengajaran baru (a new teaching environment).

Monetisasi Pendidikan
Pengunaan metaverse akan berdampak pada sisi finansial juga. Bahan ajar yang sudah tersedia tidak hanya bisa diakses oleh mahasiswa yang terdaftar di kampus saja, tetapi juga bisa ditawarkan kepada mahasiswa di kampus lain yang tentu saja harus membayar untuk menggunakannya. Dan ini bisa dilakukan berulang-ulang. Penggunaan mata uang digital universal, bitcoin, juga menjadi hal yang paling memungkinkan untuk proses monetisasi ini.

Secara bersamaan para akademisi dan pengembang bisa berkolaborasi untuk terus mengembangkan materi pembelajaran dalam konteks innovative edutaining learning experiences untuk para mahasiswa. Dan karena adanya leaning path yang berbeda dari setiap mahasiswa, maka ini membuka kesempatan bagi para akademisi untuk memiliki aktivitas mandiri (self-employment).

Masa Depan Pendidikan
Dengan metaverse, maka isu internasionalisasi, dimana mahaswa dari negara lain datang ke kampus induk, akan dikurangi, dengan adanya fasilitas online delivery. Mahasiswa asing akan tetap berada di negaranya. DI satu sisi ini memang akan mengurangi pendapatan kampus, namun sebetulnya tidak ada alasan untuk kuatir asal semua materi dan fasilitas ditata dengan rapi dan jelas.

Mahasiswa akan lebih tertarik kepada institusi yang berkarakteristik global yang mampu menyajikan materi sesuuai dengan kebutuhannya serta mengikuti trend teknologi yang ada. Mahasiswa juga akan memiliki fleksibilitas dan alur belajar sesuai dengan kemampuan dan kondisinya sendiri