In this post, I will discuss the calculation of K-Nearest Neighbor with Manhattan Distance using 5 criteria.
The classification that will be discussed is the Classification of Scholarship Recipients.
The data we will use is :
- Consists of 10 data
- Consists of 5 criteria
- K = 5
Information :
- What will be categorized consists of Mathematics scores, Indonesian B scores, English B grades, Science scores and Non-Academic scores.
- Status consists of Not Qualified and Qualified.
- What we will classify is Name = See, Mathematics Score = 81, Indonesian Score = 86, English Score = 79, Science Score = 93 and Non Academic Score = 87, Status = ?
Calculation (formula to be used):
- 79 is Alan's Math score
- 81 is See's Math score
- 88 is Alan's Indonesian score
- 86 is See's Indonesian score
- 72 is Alan's British value
- 79 is See's British value
- 92 is Alan's Science score
- 93 is See's Science score
- 97 is Alan's Non-Academic score
- 87 is See's Non-Academic score
- Then do the same calculations on data belonging to Dani, Rey and so on.
Then the calculation will be obtained as follows :
After that, do the ranking from the smallest to the largest value, the results obtained are as follows :
After getting the ranking, we can ensure that on behalf of See is eligible to receive a Scholarship or Not Eligible to receive a Scholarship.
Explanation :
- If using K=1 then the result is eligible because Dani the result is eligible.
- If using K=3 then the result is not eligible because Dani's result is eligible, while Alan and Cee's results are not eligible (fulfilling the requirements is 1 and not fulfilling the requirements is 2).
- If using K=5, the result is not eligible because Dani and Rey are eligible, while Alan, Cee and Eks are not eligible (2 meet the requirements and 3 do not meet the requirements).
So the final result See does not meet the requirements.
Notes :
- The value of K is independent (try to have an odd number so it doesn't confuse us when there are the same number of voting results).
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