Perhaps many are wondering why alpha is always used as a reference and is the focus of research, maybe even some statistics students don’t know it. On this occasion, I intend to explain this and provide an example of a simple analogy that is hopefully easy to understand.

Table of Contents

• Type I Error
• Type II Error
• Explanation & Simple Examples Analogies

Type I Error

Type I Error in statistics is symbolized by α (alpha), Type I Error is an error because it rejects the null hypothesis, even though the null hypothesis is true. …

Resuming the previous material about facial recognition, then after successful training, it can be continued with the initial face capture coding, the previous coding was as follows,

import cv2, os
import numpy as np
from PIL import Imagerecognizer = cv2.face.LBPHFaceRecognizer_create()
detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml");def getImagesWithLabels(path):
    imagePaths=[os.path.join(path,f) for f in os.listdir(path)]    faceSamples=[]
    Ids=[]    for imagePath in imagePaths:
        pilImage=Image.open(imagePath).convert('L')
        imageNp=np.array(pilImage,'uint8')
        Id=int(os.path.split(imagePath)[-1].split(".")[1])
        faces=detector.detectMultiScale(imageNp)
        for (x,y,w,h) in faces:
            faceSamples.append(imageNp[y:y+h,x:x+w])
            Ids.append(Id)
    return faceSamples, Idsfaces, Ids = getImagesWithLabels('DataSet')
recognizer.train(faces, np.array(Ids))
recognizer.save('training/training.xml')

then a little command will be added to mark the face image…

After we have succeeded in capturing or detecting facial images in one frame in the previous material, then it is time for us to make coding to teach the machine to recognize these faces.

In this material, we will try to recognize the images in the dataset folder continuously, in the sense of training the system to correctly recognize these facial images. Therefore we need a recognizer algorithm that has been provided by OpenCV, namely

recognizer = cv2.face.LBPHFaceRecognizer_create()

then defined a loop function to learn to recognize the image and each label

def getImagesWithLabels(path):

so what is needed, take image…

We will continue the previous material so that the computer can recognize faces. We will take back the coding from the previous material as follows

# Import module 
import cv2, time# capture command with camera
camera = 0
video = cv2.VideoCapture(camera, cv2.CAP_DSHOW)
a=0
while True :
    a = a+1
    check, frame = video.read()print(check)
    print(frame)gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)cv2.imshow("capture1",gray)
    #cv2.waitKey(0)key = cv2.waitKey(1)
    if key == ord('q') :
        breakprint(a)
# Command closes camera
video.release()cv2.destroyAllWindows()

From the coding, it only displays the image as a whole and does not point precisely to the face, so here we need an algorithm to detect faces from the entire image…

On this occasion, we will try to do Face Recognition using python from the basics of knowing how computers capture images.

First, we import the necessary modules (make sure you have previously installed opencv-python). I write the code using Notepad ++.

# Import module 
import cv2, time

then make a command to capture the object from our computer camera then close it

video = cv2.VideoCapture(0)
video.release()

thus, the whole command looks like this,

# Import module 
import cv2, timevideo = cv2.VideoCapture(0)
video.release()

then save the code with a name, for example, “capture1.py” in a folder, for example, C:\Users\asus\capture1

then…

In this article I intend to briefly explain what is a Monte Carlo simulation with some examples of cases of Monte Carlo simulation using Excel, remember that Excel is only a tool! The most important thing is how you can understand the concept well and dig deeper!

Table of Contents

• What is simulation?
• What is Monte Carlo Simulation?
• Monte Carlo Simulation Algorithm
• Case Examples

What is Simulation?

Okay, before going into the Monte Carlo simulation, we must first understand what simulation is.

There are several meanings about simulation which are essentially the same:

“Simulation is to imitate a real process, called a system, through a model…

Currently, if we observe, most of the job vacancies as a data analyst in companies who have implemented IT systems, require SQL as analysis tool expertise, therefore SQL skills are skills that must be mastered by someone who wants to work as a data analyst.

In this article, I intend to introduce an initial SQL lesson for someone who is looking for a career as a data analyst.

Table of Contents

1. About SQL
2. Types of SQL Commands
3. Relational Database Management System (RDBMS)
4. Query Example

About SQL

What is SQL?

SQL which stands for Structured Query Language is the standard computer language used for a database system — or…

Assalamu’alaikum Warahmatullahi Wabarakatuh.

Pada kesempatan kali ini saya akan membahas contoh sederhana Neural Network dan sekaligus implementasinya, terkait penjelasan apa itu Neural Network bisa dibaca di tulisannya saya sebelumnya yang berjudul Prediksi Menggunakan Artificial Neural Network dengan R Studio, Studi Kasus : Data Titanic. Oke langsung saja kita praktekan!

Table of Contents

1. Simple Example
2. Implementation

Simple Example

Oke, pertama jangan lupa siapkan librarynya terlebih dahulu, kita akan memakai library neuralnet, Kalo belum ada install terlebih dahulu packagenya ya.

library(neuralnet)

kita mempunyai data seperti berikut yang akan digunakan untuk melatih fungsineuralnetdi R dan kemudian menguji keakuratan jaringan neural yang dibangun