In this post, I will discuss the calculation of K-Nearest Neighbor with Manhattan 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.
- Keep in mind, for Manhattan the final result must be absolute value.
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 MS because Dani the result is MS.
- If using K=3, the result is TMS because Dani's result is MS, while Cee and Moon's result is TMS (1 MS and 2 TMS).
- If using K=5 then the result is TMS because Dani's result is MS, while Cee, Moon, Jo and Alan the result is TMS (1 MS and 4 TMS).
So that the final result See TMS
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).
Tidak ada komentar:
Posting Komentar