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WorksheetsUAS AI
Total questions: 55
Worksheet time: 55mins
AI can do these things, choose that apply!
Nihil kesalahan
Null error
Tidur
Sleep
Berfikir Cepat
Think fast
Semuanya
Everything
Tidak bosan
Not Feeling Bore
The following is the INCORRECT goal of AI!
Reasoning/decision making
Peramalan
Forecasting
Menampilkan emosi
Shows emotion
Bahasa Alami
Natural Language
The features belonging to AI are:
Learning
All Wrong
Perception
Moving and manipulating objects
Apa yang dilakukan untuk membuktikan bahwa komputer telah berprilaku seperti manusia?
What is the tool to prove that computer behaves like human?
IQ test
AI test
Turing Test
Machine Learning
Apa itu Kecerdasan/Intelligence?
What is Intelligence?
Banyak Informasi
Lots of information
Kesadaran
Consciousness
Menjadi Emosional
Emotional
Tidak punya hati
Heartless
Yang bukan teknologi AI adalah ...
Choose the technology less relevant to AI ...
Facial recognition
Image recognition
Robotics
Animation
Siapakah orang ini?
Who is this person?
Representasi informasi yang digunakan komputer untuk memahami dan mencari solusi suatu masalah.
Kalimat di atas adalah definisi dari
Representation of information for computers to understand and solve problems is called ...
Kecerdasan buatan
Dataset
Big Data
Knowledge Representation
Pilihlah beberapa representasi pengetahuan dari daftar berikut ... (pilih lebih dari 1)
Choose more than one representation of knowledge!
Persepsi
Learning
BackEnd
Planning
Search Engine
Penarikan kesimpulan dari hal khusus ke umum adalah ....
Conclusion made from specific case to general case is called ...
Reduction
Deduction
Induction
Abduction
Inferensi dengan penalaran dari umum ke khusus adalah
Inference from general to specific logic is called ...
Deduction
Abduction
Induction
Asumsi
1: John died due to a Stroke
2: Excessive fat consumption potentially leads to Stroke
Conclusion: John prefers fatty foods/snacks
The above case is a type of .... inference
Abduction
Deduction
Induction
Asumption
1: All snacks are food
2. Kripik is a snack
Conclusion: Kripik is a Food
The above case is a type of .... inference
Induction
Deduction
Assumption
Abduction
The syntax of Knowledge representation can be in the form of ... and ...
Logical sentences
The meaning in a sentence
Symbols
The meaning of logical sentences
Semantik dalam representasi pengetahuan adalah ... (pilih lebih dari 1)
The followings are the semantics in knowledge representation:
Kalimat yang logis
Logical sentences
Makna dalam kalimat
The meaning in a sentence
Simbol-simbol
Symbols
Arti kalimat logika
The meaning of a logical sentence
Nilai boolean: true atau false, pada Logika Proposisi merupakan ...
The boolean values in Propositional Logic is called ...
Konstanta Logika
Logic constants
Proposisi
Proposition
Penghubung
Connector
Hasil
Result
The meaning of this predicate logic: RacquetBrand(Babolat, Rafa), is ...
Rafa Babolat is a Racquet Brand
Babolat Rafa is a Racquet brand
Rafa has a racquet from Babolat brand
Rafa is Babolat's racquet brand
Berdasarkan sintaks Logika Predikat berikut:
∀ X (mamalia(X) → produksi(X,susu)),
Maka statemen yang benar di bawah ini adalah ...
The correct statement for this predicate logic:
∀ X (mammals(X) → produce(X,milk)),
Semua sapi menghasilkan susu
All cows produce milk
Tidak semua kucing menghasilkan susu
Not all cows produce milk
Mamalia tertentu menghasilkan susu
Certain mammals produce milk
semua statemen benar
All statements are correct
Yang merupakan slot adalah (pilih lebih dari 1)
Choose more than one items belongs to slot.
Use: sitting
hotel room
20-40 cm
location: hotel
Sistem berbasis fuzzy terdiri dari 3 bagian dengan urutan yang benar adalah ...
Fuzzy inference system consist of three consequent parts which are ...
Knowledge Base, Crisp Input, Fuzzification
Inference Engine, Fuzzification, Defuzzification
Crisp Data, Fuzzification, Defuzzification
Fuzzification, Inference Engine, Defuzzification
The correct statements for such an illustration are ... (more than 1 choice)
Y-axis is the degree of membership
The membership function for Young-aged is linear up
X-axis contains membership functions
The domain scope for Old-aged is 20-60 y/o
Jika umur berada pada rentang 20-35 maka rumus derajat keanggotaan yang salah untuk Young-aged adalah ...
The incorrect formulas for the Young-aged membership function given the age between 20-35 years old is ... (check all that apply)
(20 − x) /15
(35 − x) /25
(35 − x) /15
(20 − x) /25
Jika umur = 25 maka derajat keanggotaan yang benar adalah ... (lebih dari 1 pilihan)
The followings are the correct degree of memberships for the age of 25 years old! (check all that apply)
μYoung−aged=0.4
μYoung−aged=0.5
μMiddle−aged=0.4
μOld−aged=0
Bagian fuzzy sistem yang mengolah fuzzy input menjadi fuzzy output adalah ...
The part of the fuzzy inference system that processes fuzzy input into fuzzy output is called ...
Fuzzy processor
Fuzzifikasi
Defuzzifikasi
Inference Engine
Dari tabel tersebut maka bagian yang berwarna abu-abu diimplementasikan dalam rules yang benar yaitu ...
From this table, the implementation of rules indicated by the grey cells is ...
IF Vehicle_Orientation is Left OR Vehicle_Position is Left_Center THEN Set Speed to Negative_Small
IF Vehicle_Orientation is Left AND Vehicle_Position is Left_Center THEN Set Speed to Negative_Small
IF Vehicle_Orientation is Left AND Vehicle_Position is Left_Center THEN Set Speed to Negative
IF Vehicle_Orientation is Left AND Vehicle_Position is Left_Center THEN Set Speed to Positive_Small
Input untuk proses fuzzifikasi disebut ...
The input for fuzzification is ...
crisp input
analog input
discrete input
cross input
Model Inferensi yang menekankan pada penalaran monoton pada aturan-aturannya adalah...
The Inference Model which emphasizes monotonous reasoning on its rules is ...
Model Inferensi Mamdani
Model Inferensi Takagi-Sugeno-Kang
Model Inferensi Tsukamoto
Model Inferensi Tsukagi
Tahapan dalam sistem Fuzzy yang mengubah fuzzy output menjadi crisp output adalah ....
The procedures in Fuzzy System which converts a fuzzy output into a crisp output is ...
Fuzzifikasi
Inference Engine
Defuzzifikasi
Rule Base
The definition of Machine Learning is
Machine with brain
Machine that can learn or taught
Machine for learning
Learning via an algorithm
The definition of Machine Learning is
Machine with brain
Machine that can learn or taught
Machine for learning
Learning via an algorithm
The most appropriate definition of Information in Machine Learning is ...
Structured data
knowledge
information of a certain condition
variety of values
Two dice are tossed once, the probability of getting 2 and 3 is ...
0,1111
0,23
2/6
2/9
The wrong definition of probability is ...
Estimated occurrence of an event
The number of possibilities for a condition to arise
Correct Conditions
Comparison of events to trials
Dataset is ...
Collection of attributes
Collection of data
Collection of rowa
Collection of set
In Machine Learning, attributes in a dataset act as...
Pola
Output
Input
Proses
A column in a dataset is called...
sample
independent variable
label
correlation coefficient
Taking a small portion of data as a representation of the entire population is called...
miniature
partial
sample
population
The following are ordinal data types:
1, 2,5, 1923
SD, SMP, SMA
-1.0, 0.3, 2.0, 1000.0
Apple, Orange, Berry
Data with values 'Apple', 'Beetroot', 'Cherry', 'Durian' has data type ...
ordinal
discrete
boolean
categorical
The following dataset contains ... samples dan ... attributes
9 and 5
5 and 9
9 and 4
10 and 5
The technique of finding patterns of groups that have similarities in a dataset is called ...
artificial intelligence
classification
regression
clustering analysis
If the data does not have a class/label then the notation for all variables is:
X
x
y
y*
Processing data so that it is ready to be used for learning a machine learning algorithm is called...
pre-processing
processing
post-processing
training process
The most appropriate dataset for supervised machine learning is...
consists of attributes and a label
stored in CSV
consists only attributes
If the dataset specifies X of the attributes sepal_length, sepal_width, petal_length, and petal_width, then supervised machine learning aims to ...
Processing Species
Normalizing Species
Annotating the Species
Predicting Species
If the label in the dataset has only 3 values, then the prediction model built is called ...
regresi
klasifikasi
enkapsulasi
clustering
K-Means algorithm is categorised as a .......-based clustering
correlation
numerical
hierarchical
partition
The value of 'K' in KMeans is ....
The number of adjacent samples
Complexity measure
The desired number of clusters
Sample size
Check all conditions stating a poor classifier...
Generic model
Overfitting
Biased model
Underfitting
An algorithm that learns to classify based on distances between samples is ...
KMeans
KFold
KNN
ANN
the 'K' in K Nearest Neighbor algorithm refers to ...
total classes
the number of samples requires for classification
total most similar samples for a class decision
the number of similar groups that forms classes
Check all the parameters in k nearest neighbor algorithm!
number of neighbors
distance metric
number of layers
number of fold
This image illustrates Tic Tac Toe's AI which is called ...
Decision Tree
Naive Bayes Algorithm
A* Algorithm
Minimax Algorithm
The examples of Non-Player characters are...
Computer player, Bot
Player 1, Player 2
Bonuses, items, potions, lives
leaderboard, daily challenge
AI is implementable in various game components, such as... (check that apply)
All wrong
Arena
NPC
Level
