1. A process
by which organizational goals are achieved by using resources called by
....
a. Business Intelligence
b. Data Mining
c. Business Performance Management
d. Knowledge Management System
e. Management
2. The
following is included in Mintzberg's 10 Managerial Roles, except
....
a. Figurehead
b. Entrepreneur
c. Resource allocator
d. Monitor
e. Slacker
3. BI is ….
a. A large repository of
well-organized historical data
b. The tools
that allow transformation of data into information and knowledge
c. Allows
monitoring, measuring, and comparing key performance indicators
d. Allows
access and easy manipulation of other data components
e.
An umbrella term that combines architectures, tools, databases, analytical
tools, applications, and methodologies
4. A BI
system has four major components, except
....
a. data warehouse
b. business analytics
c. business performance management
d. user interface
e. historical data management
5. A process of choosing among two or more alternative courses of action
for the purpose of attaining a goal(s) called by ….
a. Intelligence
b. Design
c. Choice
d. Implementation
e.
Decision making
6. A model is ....
a. A simplified representation (or
abstraction) of reality
b. Actually irrelevant in solving a specific problem
c. A successful computerized system should fit the
decision style and the decision situation
d. A significant part of many DSS and BI systems
e. Represent systems/problems
7. Models can be classified based
on their degree of abstraction ....
a. Iconic models
b. True models
c. Analog models
d. Mental models
e. Mathematical (quantitative) models
8. The following is not included in Simon’s
Decision-Making Process ....
a. Intelligence
b. Design
c. Choice
d. Consciously
e. Implementation
9. Potential issues in data/information collection and
estimation, except ….
a. Lack of data
b. Cost of data collection
c. Inaccurate and/or imprecise data
d. Data estimation is often subjective
e. Choosing
and validating against
10. KMS is an acronym for ....
a. Kartu Menuju Sehat
b. Knowledge Management Survey
c. Knowledge Managerial System
d. Knowledge Management System
e. Knowledge Maintenance Survey
11. A system
intended to support managerial decision makers in semistructublack and
unstructublack decision situations called by ....
a. KMS
b. ES
c. MIS
d. DSS
e. ANN
12. The following is not included in 10 Key Ingblackients of
Data (Information) Quality Management ….
a. Data quality is a business problem, not only a systems
problem
b. Focus on information about customers and suppliers,
not just data
c. Focus on all components of data: definition, content,
and presentation
d. Actually irrelevant in solving a
specific problem
e. Measure real costs (not just the percentage) of poor
quality data/information
13. The following is not included in knowledge components ....
a. Expert systems
b. Neural networks
c. Intelligent agents
d. Fuzzy logic
e. Information system
14. How is the search process through a heuristic approach
....
a. All possible solutions are checked
b. Comparison: Stop when all alternatives are checked
c. Stop when no improvement is possible
d. All possible sollution are checked
e. Stop searching when solution is good enough
15. The following is included in when to use Heuristics ….
a. Inexact
or limited input data
b. Complex
reality
c.
Cannot guarantee an optimal solution
d. Reliable,
exact algorithm not available
e. For
making quick decisions
16. The right answer to the following Intersections of many disciplines ….
a. Data
warehouse
b. Data mart
c.
Data mining
d. Database
e. Mathematic
17. The nontrivial process of identifying valid, novel,
potentially useful, and ultimately understandable patterns in data stoblack in
structublack databases is a definition of ....
a. Data
warehouse
b. Data mart
c.
Data mining
d. Database
e. Mathematic
18. Most popular
Decision Tree algorithms include below, except ….
a. ID3
b. C4.5
c.
k-Means
d. C5
e. CART
19. Which technique also known as market basket analysis ….
a. Classification
b. Clustering
c.
Association
d. Rapid
Miner
e. Weka
20. A brain
metaphor for information processing also called by ....
a. Neurons
b. Pattern
c. Deep learning
d. Machine learning
e. Neural networks
21. The following is not included in the backpropagation learning
algorithm procedure ….
a. Initialize
weights with random values and set other network parameters
b. Read in
the inputs and the desiblack outputs
c.
Compare outputs with desiblack targets
d. Compute
the actual output (by working forward through the layers)
e. Compute the error (difference between
the actual and desiblack output)
22. Other
Popular ANN Paradigms Self Organizing Maps (SOM) applications, except ….
a. Speech recognition
b. Interpretation
of seismic activity
c.
Computer vision
d. Medical
diagnosis
e. Bibliographic classification
23. The following is the right answer for disadvantages of
ANN ….
a. Not prone to restricting normality and/or independence
assumptions
b. Handles both numerical and categorical
variables (transformation needed!)
c. Can handle variety of problem types
d. Able to deal with (identify/model) highly nonlinear
relationships
e. It is hard to handle large number of
variables (especially the rich nominal attributes)
24. Part of
a data mining software, except ....
a. NeuroShell
b. PASW (formerly SPSS Clementine)
c. SAS Enterprise Miner
d. Statistica Data Miner
e. Weka
25. Text mining application area, except ….
a. Information extraction
b. Topic tracking
c. Summarization
d. Categorization
e. Weka
1. The data
warehouse is ....
a. Business Intelligence
b. A real-time system that alert managers to potential
opportunities, impending problems, and threats, and then empowers them to react
through models and collaboration
c. Fact table and dimension table
d. A physical repository where relational data are
specially organized
e. A collection of integrated, subject-oriented
databases design to support DSS functions, where each unit of data is
non-volatile and relevant to some moment in time
2. The
following is included in characteristics of DW, except
....
a. Subject oriented
b. Integrated
c. Nonvolatile
d. Time-variant (time series)
e. Normalized
3.
Independent data mart is ….
a. A large
repository of well-organized historical data
b. The tools
that allow transformation of data into information and knowledge
c. A
departmental data warehouse that stores only relevant data
d. A subset
that is created directly from a data warehouse
e.
A small data warehouse designed for a strategic business unit or a department
4. Data
integration in DW that comprises three major processes, namely
....
a. time series, data federation, and change capture
b. time series, data evaluation, and change capture
c. data access, data evaluation, and extraction
d. Extraction, Transformation, and Load
e. data access, data federation, and change
capture
5. A
technology that provides a vehicle for pushing data from source systems into a
data warehouse called by ….
a. Data mart
b.
Management information system (MIS)
c.
Service-oriented architecture (SOA)
d.
Enterprise information integration (EII)
e.
Enterprise application integration (EAI)
6. An
evolving tool space that promises real-time data integration from a variety of
sources called by ....
a. Enterprise information
integration (EII)
b. Service-oriented architecture (SOA)
c. Management information system (MIS)
d. Data mart
e. Enterprise application integration (EAI)
7. A
new way of integrating information systems called by ....
a. Enterprise information integration (EII)
b.
Service-oriented architecture (SOA)
c. Management information system (MIS)
d. Data mart
e.
Enterprise application integration (EAI)
8. The following is not included in direct benefits of a
data warehouse ....
a. Allows end users to perform extensive
analysis
b. Allows a consolidated view of corporate
data
c. Enhanced system performance
d. Enhance business knowledge
e.
Simplification of data access
9. The following is risks in Implementing DW, except ….
a. Lack of supporting software
b. Users not computer literate
c. Unrealistic user expectations
d. Key people leaving the project
e.
Single platforms
10.
Business Performance Management (BPM) is ....
a. A large
repository of well-organized historical data
b. The tools
that allow transformation of data into information and knowledge
c. A collection of integrated, subject-oriented databases
design to support DSS functions, where each unit of data is non-volatile and
relevant to some moment in time
d. A real-time system that alert
managers to potential opportunities, impending problems, and threats, and then
empowers them to react through models and collaboration
e. A small data warehouse designed for a strategic
business unit or a department
11. BPM also
called …. by Oracle
a. Corporate performance management (CPM)
b. Strategic enterprise management (SEM)
c. System application and product in data processing
(SAP)
d. Enterprise performance management
(EPM)
e. Enterprise resources planning (ERP)
12. BPM is an outgrowth of BI and incorporates many of its ….
a. data quality
b. customers and suppliers
c. definition, content, and presentation
d. technologies, applications, and
techniques
e. data, information, and knowledge
13. The following is not included in a closed-loop process
to optimize business performance ....
a. Strategize
b. Plan
c. Monitor/ analyze
d. Act/ adjust
e. Metrics
14. Key performance indicator (KPI) represents a strategic
objective and metrics that measures performance against a goal. Distinguishing
features of KPIs below, except ....
a. Strategy
b. Targets
c. Time frames
d. Benchmark
e. Forecast
15. A
performance measurement and management methodology that helps translate an
organization’s financial, customer, internal process, and learning and growth
objectives and targets into a set of actionable initiatives called by ….
a. Business
performance management (BPM)
b. Key
performance indicator (KPI)
c.
Balanced scorecard (BSC)
d. Corporate
performance management (CPM)
e. Strategic
enterprise management (SEM)
16. The right answer to the following groupware in supporting GSS, except
….
a. Lotus
Notes / Domino Server
b. Microsoft
NetMeeting
c.
Pentaho
d. Novell
Groupwise
e.
GroupSystems
17. A characteristic of a person that leads to
production of acts, items and/or instances of novelty is a definition of ....
a. Idea
b. Genius
c.
Creativity
d.
Brainstorming
e. Actor
18. In a knowledge management system,
knowledge is ….
a. facts
b. data
c.
information in action
d.
understanding
e. anything
that has been learned
19. Explicit (leaky) knowledge is ….
a. Highly
personal and hard to formalize
b. Knowledge
that is usually in the domain of subjective, cognitive, and experiential
learning
c.
Knowledge that deals with objective, rational, and technical material (data,
policies, procedures, software, documents, etc.)
d. Hard to
document, transfer, teach and learn
e. Involves a lot of human interpretation
20. Tacit (embedded) knowledge is ....
a. Knowledge that deals with objective, rational, and
technical material (data, policies, procedures, software, documents, etc.)
b. Easily documented, transferblack, taught and learned
c. A system that facilitates knowledge management by
ensuring knowledge flow from the person(s) who know to the person(s) who need
to know throughout the organization; knowledge evolves and grows during the
process
d. A characteristic of a person that leads to production
of acts, items and/or instances of novelty
e. Knowledge that is usually in the domain of
subjective, cognitive, and experiential learning
21.
Knowledge management systems (KMS) is ….
a. Knowledge that deals with objective, rational, and
technical material (data, policies, procedures, software, documents, etc.)
b. Easily documented, transferblack, taught and learned
c.
A system that facilitates knowledge management by ensuring knowledge flow from
the person(s) who know to the person(s) who need to know throughout the
organization; knowledge evolves and grows during the process
d. A
characteristic of a person that leads to production of acts, items and/or
instances of novelty
e. Knowledge
that is usually in the domain of subjective, cognitive, and experiential
learning
22. A computer program that attempts to
imitate expert’s reasoning processes and knowledge in solving specific problems
called by ….
a. Speech
recognition
b. Machine
learning (ML)
c.
Expert system (ES)
d. Computer
vision (CV)
e. Artificial intelligence (AI)
23.
Three major components in Expert system (ES) are ….
a. Explanation
subsystem (justifier), Blackboard (working memory), and User
interface
b. time series, data evaluation, and change capture
c. data access, data evaluation, and extraction
d.
Extraction, Transformation, and Load
e. Knowledge base, Inference engine, and User
interface
24. Human
learning is a combination of many complicated cognitive processes, including
below, except ....
a. Introduction
b. Induction
c. Deduction
d. Analogy
e. Other special procedures related to observing and/or analyzing
examples
25. A family of
artificial intelligence technologies that is primarily concerned with the
design and development of algorithms that allow computers to “learn” from
historical data called by ….
a. Speech
recognition
b.
Machine learning (ML)
c.
Expert system (ES)
d. Computer
vision (CV)
e.
Artificial intelligence (AI)
1. Lingkungan bisnis saat ini sangat kompleks terutama
dalam menciptakan peluang serta masalah. Sebutkan faktor lingkungan bisnis yang
anda ketahui disertai dengan penjelasan secara singkat! (10)
2. Sebutkan dan jelaskan komponen utama yang dimiliki oleh
Business Intelligence! (10)
3. Menurut Simon, Intelligence merupakan salah satu
tahapan dari beberapa tahapan dalam proses pengambilan keputusan. Jelaskan apa
saja yang terjadi pada fase/ tahapan ini! (15)
4. Apa yang dimaksud dengan Data Mining dan sebutkan jenis
pola yang anda ketahui! (15)
5. Sebutkan dan gambarkan 3 langkah proses dalam
Supervised Learning of ANN! (20)
6. Buatlah percobaan Supervised Learning ANN dengan
membuktikan Tabel Kebenaran dengan fungsi logika “inclusive OR” dimana nilai
Learning Rate (α) = 0.1 dan Threshold (θ) = 0.2! (30)
1. Manajer dalam membuat
keputusan biasanya mengikuti beberapa langkah proses (pendekatan ilmiah).
Sebutkan empat Decision Making Process disertai dengan penjelasan secara
singkat! (10)
2. Apa yang dimaksud dengan Business Intelligence dan
sebutkan komponen utama yang dimiliki oleh Business Intelligence! (15)
3. Apa yang dimaksud dengan ERP, SCM, CRM, dan KMS? (10)
4. Gambarkanlah Taksonomi sederhana dengan metode
pembelajaran, dan algoritma untuk tugas-tugas Data Mining! (20)
5. Apa yang dimaksud dengan ANN dan sebutkan elemen-elemen
ANN! (15)
6. Buatlah percobaan Supervised Learning ANN dengan
membuktikan Tabel Kebenaran dengan fungsi logika “AND” dimana nilai Learning
Rate (α) = 0.1 dan Threshold (θ) = 0.2 (30)
1. Apa
manfaat dari text mining yang anda ketahui! (10)
Apa
perbedaan dan persamaan antara text mining dengan data mining! (10)
2. Jelaskan perbedaan antara Dependent
data mart dan Independent
data mart! (10)
Sebutkan dan jelaskan karakteristik dari Data Warehouse! (10)
3. Apa
yang dimaksud dengan BPM dan apa nama lain dari BPM menurut Gartner Group,
Oracle dan SAP? (15)
Apa yang dimaksud dengan KPI, Balanced scorecard, dan Six Sigma? (15)
4. Jelaskan
menurut anda apa yang dimaksud dengan Explicit dan Tacit knowledge disertai
dengan contohnya! (15)
Jelaskan menurut
anda apa yang dimaksud dengan KMS dan sebutkan teknologi yang mendukung KMS! (15)
5. Jelaskan
apa yang dimaksud dengan ES dan sebutkan komponennya! (20)
Jelaskan apa
yang dimaksud dengan AI dan sebutkan tujuannya! (20)
6. Buatlah Snowflake schema min.9 tabel dimensi 1 tabel fakta, kemudian tentukan mana single value attribute, multi value
attribute, store attribute dan derived attribute dari skema yang anda buat! (30)
1. Manfaat
pertambangan teks yang jelas terutama di lingkungan data-kaya teks
misalnya, hukum
(perintah pengadilan), penelitian akademik (artikel penelitian), keuangan
(laporan triwulan), obat-obatan (discharge ringkasan), biologi (interaksi
molekul), teknologi (file paten), pemasaran (pelanggan komentar), dll
catatan
elektronik communization (misalnya, Email)
filtering Spam
Email prioritas
dan kategorisasi
generasi respon
otomatis
1. Keduanya berusaha untuk novel dan berguna pola
Keduanya proses semi-otomatis
Perbedaan adalah sifat data:
Terstruktur dibandingkan data terstruktur
Data terstruktur: dalam database
data tidak terstruktur: dokumen Word, file PDF, kutipan
teks, file XML, dan sebagainya
text mining - pertama, memaksakan struktur data, maka
tambang data terstruktur
2. Dependent data mart
Sebuah subset yang dibuat langsung dari data warehouse
Mart data independen
Sebuah gudang data kecil dirancang untuk unit bisnis
strategis atau departemen
2. Subject oriented
Integrated
Time-variant (time series)
Nonvolatile
Summarized
Not normalized
Metadata
Web based, relational/multi-dimensional
Client/server
Real-time and/or right-time (active)
3. Business Performance Management(BPM) adalah sistem
real-time yang memberi pesan kepada manager tentang potensi kesempatan, masalah
dan ancaman yang akan datang, dan mendukung manager untuk bertindak melalui
model dan kolaborasi.
Beberapa perusahaan memberi sebutan; manajemen kinerja
perusahaan (CPM) oleh Gartner Group, manajemen kinerja perusahaan (EPM) oleh
Oracle, manajemen perusahaan strategis (SEM) oleh SAP.
3. Indikator kinerja utama (KPI)
KPI mewakili sasaran strategis dan metrik yang mengukur
kinerja terhadap sasaran
Membedakan ciri-ciri KPI
Strategi
Target
Rentang
Pengkodean
Kerangka waktu
Tolok ukur
Balanced scorecard (BSC)
Metodologi pengukuran kinerja dan manajemen yang membantu
menerjemahkan tujuan keuangan, pelanggan, proses internal, dan pembelajaran
organisasi dan sasaran pertumbuhan ke dalam serangkaian prakarsa yang dapat
ditindaklanjuti
Metodologi manajemen kinerja yang bertujuan untuk mengurangi
jumlah cacat dalam proses bisnis mendekati nol cacat per juta peluang (DPMO)
mungkin
4. Pengetahuan eksplisit (bocor)
Pengetahuan yang berhubungan dengan materi objektif,
rasional, dan teknis (data, kebijakan, prosedur, perangkat lunak, dokumen, dll.)
Mudah didokumentasikan, ditransfer, diajarkan dan dipelajari
Contoh ...
Pengetahuan eksplisit dan tacit
Tacit (tertanam) pengetahuan
Pengetahuan yang biasanya berada dalam domain subjektif,
kognitif, dan pengalaman belajar
Hal ini sangat personal dan sulit untuk diformalkan
Sulit untuk mendokumentasikan, mentransfer, mengajar dan
belajar
Melibatkan banyak interpretasi manusia
Contoh ...
4. Sistem manajemen pengetahuan (KMS)
Sistem yang memfasilitasi pengelolaan pengetahuan dengan
memastikan aliran pengetahuan dari orang yang mengetahui orang yang perlu
diketahui di seluruh organisasi; Pengetahuan berkembang dan berkembang selama
proses berlangsung
Technologies that support KM
Artificial intelligence
Intelligent agents
Knowledge discovery in databases
Extensible Markup Language (XML)
5. Merupakan program komputer yang mencoba meniru proses
penalaran dan pengetahuan ahli dalam memecahkan masalah tertentu
Teknologi AI Terapan Terpopuler
Meningkatkan Produktivitas
Augment Work Forces
Bekerja paling baik dengan area / tugas yang sempit
Sistem pakar tidak menggantikan para ahli, tapi
Buatlah pengetahuan dan pengalaman mereka lebih banyak
tersedia, dan dengan demikian
Izin non-ahli untuk bekerja lebih baik
Three major components in ES are:
Knowledge base
Inference engine
User interface
ES may also contain:
Knowledge acquisition subsystem
Blackboard (workplace)
Explanation subsystem (justifier)
Knowledge refining system
5. Kecerdasan Buatan (AI)
Subfield ilmu komputer, terkait dengan penalaran simbolis
dan pemecahan masalah
AI memiliki banyak definisi ...
Perilaku dengan mesin yang, jika dilakukan oleh manusia,
akan dianggap cerdas
"... belajar bagaimana membuat komputer melakukan
berbagai hal di mana, pada saat ini, orang lebih baik
Teori bagaimana pikiran manusia bekerja
Membuat mesin
lebih pintar (tujuan utama)
Pahami apa itu
kecerdasan
Membuat mesin
lebih cerdas dan berguna
6.
Single-valued attribute adalah atribut yang menampung nilai tunggal untuk setiap
entitas.
Misalnya: dalam dimensi
mahasiswa (dim_mahasiswa): NIM, NAMA, JKEL
Multi-valued attribute adalah atribut yang menampung banyak nilai untuk entitas.
Misalnya: dalam dimensi mata
kuliah (dim_matakuliah): SMT (semester)
Store Attribute adalah attribute yang harus disimpan
dalam database.
Misalnya: dalam dimensi
pertanyaan (dim_pertanyaan): KDKS
Derived attribute adalah atribut yang mepresentasikan nilai yang dapat di
turunkan dari nilai sebuah atau sekumpulan atribut.
Misalnya: dalam dimensi
mahasiswa (dim_mahasiswa): GRADE, TOTAL_SMK
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