Bab 2 Berpikir Komputasional | Informatika Kelas 10 Kurikulum Merdeka

Main Ideas & Lessons (Informatics Grade 10)

• Computational thinking is presented as a way of thinking like an information scientist to solve problems efficiently, effectively, and optimally.
• The lesson emphasizes not thinking like a machine, but using strategies humans and computers can apply to real-world problems (e.g., service robots and digital monitoring systems).

Four Foundational Concepts of Computational Thinking

1. Abstraction Filter out irrelevant details and keep only important information to focus on the solution.

2. Algorithms Express solutions as structured, step-by-step procedures that a computer can understand and execute.

3. Decomposition Break a large problem into smaller, more manageable parts.

4. Patterns Recognize similarities, generalize from known cases, and reuse solutions for related problems.

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Training and Learning Progression

• Computational thinking skills should be trained gradually, starting from simple to complex problems.
• For larger/more complex problems, computers become more necessary for efficient solving.

Suggested Level Focus

• Elementary/JHS: problem-solving is not yet focused on algorithmic forms.
• High school: students begin learning to create solutions as efficient, programmable algorithms.

Learning Approach (“BK”, as described)

• Solving problems involves learning from existing cases/solutions, then adapting them to one’s context.
• Practice problems should be chosen appropriately, focusing on everyday problems relevant to informatics concepts.
• Goal: build a reusable catalog/pattern of solutions from many cases to apply to real problems later—students essentially save solution pieces to assemble later.
• Computational thinking in this unit is framed as critical thinking from an informatics perspective, using the strategies above.

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Methodologies / Content Topics Presented as Instruction-Like Concepts

1) Searching (finding items that match criteria)

• Searching is defined as finding an object/number/concept/information in a search space that meets certain criteria.

Examples:

• Find a book with a specific title on library shelves.
• Find uniform/batik clothes in a wardrobe containing all your clothes.
• Find documents/websites using a search engine like Google.

Formal structure of a search problem:

• A group of objects/items (search space)
• Search criteria
• A checking step to determine whether an item satisfies the criteria

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2) Sorting (arranging items based on rules)

• Sorting is a classic computing problem: arranging a group of objects/entities according to specific rules.
• Example: sorting numbers ascending or descending.

Typical flow of a sorting problem:

• Start with a set of randomly arranged objects
• Use a process that corrects positions by exchanging elements
• Repeat until the arrangement is fully correct/sorted

Sorting techniques mentioned:

• Bubble sort
• Insertion sort
• Quick sort
• Merge sort (listed as “mat short” in the text; likely merge sort)
• Selection sort

Insertion Sort (as described)

• Purpose: sort a list by inserting elements one by one into the correct position.
• Method: insert elements according to their size/order, repeating until the entire list becomes sorted.

Selection Sort (as described)

• Purpose: sort by repeatedly selecting the smallest or largest element from the unsorted portion.
• Method (described flow):
  • Divide the list into:
    • Sorted part (initially empty)
    • Unsorted part (initially the full list)
  • Iteration idea:
    • Find the smallest (or largest) element in the unsorted part
    • Move/place it into the sorted part, keeping it ordered

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3) Stack and Queue (data storage structures with different rules)

Stack (LIFO)

• Definition: objects stored like a stack of items (example: plates).
• Rule: Last In First Out (LIFO) The item saved most recently is removed first.

Plate example:

• The first plate placed is at the bottom.
• When taking plates one by one, the bottom plate is removed last.

Queue (FIFO)

• Definition: objects stored in a queue structure (like a line).
• Rule: First In First Out (FIFO) The item arriving first is removed first.

People queue example:

• The person who arrives first stands at the front and receives service first.

Link to problem types:

• Queues are more appropriate for some situations.
• Stacks are more appropriate for others.

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Speakers / Sources Featured

• No specific person/teacher/speaker is named in the provided subtitles.
• The only explicitly mentioned “source” is Google (as an example of a search engine).