Calculation of K-Nearest Neighbor With Euclidean Distance

In this post, I will discuss the calculation of K-Nearest Neighbor with Euclidean Distance.

The classification that will be discussed is the Classification of Scholarship Recipients.

The data we will use is:

• Consists of 10 data
• Consists of 2 criteria
• K = 5

Information :

• Which will be categorized consists of Non-Academic values ​​and Academic values.
• Status consists of TMS (Not Qualified) and MS (Qualified).
• What we will classify is Name = See, Non-Academic Value = 81 and Academic Value = 86, Status = ?

Calculation (formula to be used) :

• 79 is Alan's Non-Academic score
• 81 is See's Non-Academic score
• 97 is Alan's Academic score
• 86 is See's 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 Cee and Moon's result is 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 Eks are eligible, while Cee, Moon and Tet are ineligible (2 met the requirements and 3 did not fulfill 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).