Question 1

Dalam Pemodelan ada 2 variabel yaitu Independent variables and Dependent variable

Accepted Answer

true

Answer

false

Question 2

Jika Variabel dependen merupakan variabel numerik, apakah metode yang bisa digunakan untuk memprediksi nilai dari varibel dependen tersebut?

Accepted Answer

Linear Regression

Answer

Logistic Regression

Answer

Naive Bayes

Answer

K-Means

Question 3

Which algorithm is based on the concept of probability theory and Bayes' theorem?

Accepted Answer

Naive Bayes

Answer

Support Vector Machine (SVM)

Answer

K-Means

Answer

Decision Tree

Question 4

Proses untuk mengatasi missing values dan data yang tidak konsisten disebut dengan

Accepted Answer

Data Pre-Processing

Answer

Building a Model

Answer

Data Gathering

Answer

Model Evaluation

Question 5

Which algorithm is suitable for finding patterns in data based on similarity measures?

Accepted Answer

K-Nearest Neighbors

Answer

Naive Bayes

Answer

Random Forest

Answer

Support Vector Machine (SVM)

Question 6

Berikut ini yang termasuk dalam Machine Learning, KECUALI

Accepted Answer

Transfer Learning

Answer

Unsupervised Learning

Answer

Reinforcement Learning

Answer

Supervised Learning

Question 7

Which algorithm is used for ensemble learning by combining multiple weak learners to create a strong learner?

Accepted Answer

AdaBoost

Answer

K-Means

Answer

Decision Tree

Answer

Linear Regression

Question 8

Berikut ini yang termasuk dalam Machine Learning Algorithms, KECUALI

Accepted Answer

Hypothesis Testing

Answer

Regression

Answer

Classification

Answer

Clustering

Question 9

Which algorithm is known for its ability to handle non-linear relationships between input and output?

Accepted Answer

Support Vector Machine (SVM)

Answer

Linear Regression

Answer

K-Nearest Neighbors

Answer

Decision Tree

Question 10

Artificial Intelligence merupakan bagian dari Machine Learning

Accepted Answer

false

Answer

true

Question 11

Which algorithm is used for finding patterns in data through unsupervised learning?

Accepted Answer

K-Means

Answer

Naive Bayes

Answer

Logistic Regression

Answer

Random Forest

Question 12

Berikut ini contoh dari hasil analisis

Accepted Answer

Clustering

Answer

Hypothesis Testing

Answer

Classification

Answer

Regression

Question 13

Which algorithm is useful for handling high-dimensional data?

Accepted Answer

Principal Component Analysis (PCA)

Answer

Naive Bayes

Answer

K-Nearest Neighbors

Answer

AdaBoost

Question 14

Prediksi Nilai mahasiswa jika dia belajar selama 10 jam.

Jika model regresinya berikut:

Nilai = 20 + 6 Jam belajar

Accepted Answer

80

Answer

26

Answer

20

Answer

60

Question 15

Which algorithm is used for both classification and regression tasks with a hyperplane separating classes in high-dimensional space?

Accepted Answer

Support Vector Machine

Answer

Random Forest

Answer

Decision Tree

Answer

K-Means

Question 16

Berikut ini adalah contoh dari metode

Accepted Answer

Random Forest

Answer

Support Vector Machine

Answer

Naive Bayes

Answer

Decision Tree

Question 17

The following are the main ensemble methods except

Accepted Answer

Learning

Answer

Boosting

Answer

Bagging

Answer

Stacking

Question 18

Pada Neural Network ada 3 Layer yaitu Input layer, Proses Layer dan Output Layer

Accepted Answer

false

Answer

true

Question 19

The following is NOT a kernel function in SVM

Accepted Answer

ReLu

Answer

RBF

Answer

Polynomial

Answer

Sigmoid

Question 20

Model Neural Network yang dapat digunakan untuk image processing adalah

Accepted Answer

Convolutional Neural Network

Answer

Feed Forward Neural Network

Answer

Radial Basis Function (RBF) Networks

Question 21

What is overfitting in machine learning?

Accepted Answer

when a model performs well on training data but poorly on unseen data

Answer

when a model can only understand data that is linearly separable

Answer

when a model is trained on a very small dataset

Answer

when a model is unable to capture the underlying patterns in the data

Question 22

Apakah tujuan dari feature engineering dalam machine learning?

Accepted Answer

untuk memilih variabel yang tepat dalam model

Answer

untuk meningkatkan kompleksitas model

Answer

untuk menghapus semua variabel kecuali variabel target

Question 23

What is the objective of linear regression analysis in machine learning?

Accepted Answer

to predict a continuous value based on input features

Answer

to reduce the dimensionality of the data

Answer

to classify data points into different categories

Answer

to group similar data points together based on similarities

Question 24

Berikut ini manakah contoh dari Unsupervised Learning

Accepted Answer

Mengelompokkan data kedalam grup yang mirip

Answer

Melakukan analisis data training dengan label data

Answer

Memprediksi transaksi yang Fraud dan tidak

Answer

Memprediksi hasil penjualan suatu perusahaan

Question 25

In machine learning, what is training data used for?

Accepted Answer

Teaching a model to recognize patterns and make predictions

Answer

Deploying the model

Answer

Testing model accuracy

Answer

Interpreting results

Question 26

Metode yang digunakan untuk mengevaluasi model klasifikasi, KECUALI

Accepted Answer

RMSE

Answer

F1 Score

Answer

Presisi

Answer

Akurasi

Question 27

Hasil evaluasi model regresi linier menggunakan R-Square. Model terbaik adalah model yang memiliki R-Square yang kecil

Accepted Answer

false

Answer

true

Question 28

What is the purpose of data preprocessing in the training phase of machine learning models?

Accepted Answer

to clean, transform, and prepare raw data for model training

Answer

Applying the trained model to the validation set directly

Answer

Increasing the dimensionality of the input data

Answer

Generating new training examples from scratch

Question 29

Apakah metode yang digunakan untuk menguji hubungan antara 2 variabel dimana tipe variabelnya keduanya adalah kategorik

Accepted Answer

Uji Independensi Chi-Square

Answer

Uji Korelasi Pearson

Answer

Linear Regression

Answer

Naive Bayes

Question 30

What is the purpose of a confusion matrix in model evaluation for machine learning?

Accepted Answer

To visualize the performance of a classification model by comparing actual and predicted values

Answer

To calculate the mean squared error of a regression model

Answer

To select optimal hyperparameters for a model

Answer

To preprocess the data before training the model

Question 31

Berikut ini adalah contoh dari hasil

Accepted Answer

Decision Tree

Answer

Linear Regression

Answer

Naive Bayes

Answer

Support Vector Machine (SVM)

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