Convert Decimal to Hexadecimal

As with binary and octal, we will use the same calculation technique.

Example 1:

67₁₀ = …….₁₆ ?

• First of all 67/16 = 4, remainder 3
• Then 4/16 = 0, remainder 4,
• Thus from the calculation results obtained 67₁₀ = 43₁₆

 

Example 2:

92₁₀ = ………………………₁₆ ?

• First 92/16 = 5, remainder 12 (written C)
• Then 5/16 = 0, remainder 5,
• Thus from the calculation results obtained 92₁₀ = 5C₁₆

Fuzzy Metode Sugeno

Calculation of Naive Bayes Classification (Part 2) Training Data Test Phase

After calculating each probability, the next step is to test the training data.

• Training Data Test Phase

Information :

• The data above is the data that we have obtained from the previous training data
• What is calculated first is the part of P (Remarks | Roadworthy)
• P(Remarks|Easy to Road) is the calculation for the criteria Description|Easy to Go * Driver criteria = Healthy * Lights criteria = On * Tire criteria = New * Machine criteria = New
• = 0.6 * (0.555556 * 0.666667 * 0.555556 * 0.44444) and the result is below
• This stage is carried out for all training data

So the results obtained are as follows :

This stage is also carried out for criteria P (Remarks|Not Roadworthy), so the results are as follows :

The next step is to enter the results of criteria P (Remarks|Road Worthy) and P criteria (Remarks|Not Roadworthy) into a table.

From the data above, we compare the results of Roadworthy and Roadworthy criteria and select the largest value, so we get the following results :

From the data above it can be concluded :

• The data that corresponds to the training data is 13 data
• Data that does not match the training data are 2 data

Konversi Desimal ke Heksadesimal

Seperti halnya biner dan oktal, kita pun akan menggunakan teknik perhitungan yang sama.

Contoh 1:

67₁₀ = …….₁₆ ?

1. Pertama-tama 67/16 = 4, sisa 3
2. Lalu 4/16 = 0, sisa 4,
3. Dengan demikian dari hasil perhitungan didapatkan 67₁₀ = 43₁₆

 

Contoh 2:

92₁₀ = ………………………₁₆ ?

1. Pertama-tama 92/16 = 5, sisa 12 (ditulis C)
2. Lalu 5/16 = 0, sisa 5,
3. Dengan demikian dari hasil perhitungan didapatkan 92₁₀ = 5C₁₆

Naive Bayes Classification Calculations (Part 1)

In this post, I will discuss classification calculations using the Naive Bayes method.

What we will classify is the Roadworthy BUS Classification

The data to be used consists of the following:

• Number of data = 14
• Noise = 4
• Number of classifications = 2 

From the data above, what we have to do is:

1. Determine class probabilities

• Roadworthy = 9/15 = 0.6
• Not Roadworthy = 6/15 = 0.4

Information :

• 15 is the amount of data
• 9 is the amount of data for Roadworthy
• 6 is the amount of data for Not Roadworthy

2. Determine category probabilities

• Roadworthy Category

Information :

• P(Driver=Healthy|Road Worthy) is the number of Healthy and Roadworthy Driver categories
• 5 is the number of healthy drivers who are roadworthy
• 9 is the number of roadworthy classifications
• So the result is 0.555556
• Likewise with the next process

• Not Roadworthy Category

Information :

• P(Driver=Healthy|Unfit for Road) is the number of categories of Healthy and Unfit for Road drivers
• 2 is the number of healthy drivers who are not roadworthy
• 6 is the number of classifications not roadworthy
• So the result is 0.333333
• Likewise with the next process

After the probability calculation process is carried out, the data that will be obtained is as follows :

Convert Decimal to Octal

With the same formula as binary we can also do it for base 8 (octal) numbers.

Example:

67₁₀ = ……₈ ?

• First of all 67/8 = 8, remainder 3
• Then 8/8 = 1, remainder 0,
• Last 1/8=0, remainder 1.
• Thus from the calculation results obtained 67₁₀ = 103₈

Comparison of Euclidean Distance, Manhattan Distance and Minkowsky Distance

This time I will post a comparison of Euclidean distance, Manhattan distance and Minkowsky distance.

This is just a comparison.

The data to be used are as follows :

Perform the data calculation above using Euclidean distance, Manhattan distance and Minkowsky distance.

If you don't understand, you can see my previous post.

• Euclidean Distance https://infmining.blogspot.com/2022/08/set-data-euclidean-distance.html
• Manhattan distance https://infmining.blogspot.com/2022/08/set-data-jarak-manhattan.html
• Minkowsky Distance https://infmining.blogspot.com/2022/09/set-data-jarak-minkowski.html

So the results we will get are as follows :

From the above results it is obtained that the Minkowsky Distance has a smaller value than the Manhattan Distance and also the Euclidean Distance.