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Saturday, March 28, 2020

Kisi-kisi Soal UTS dan UAS Business Intelligence Beserta Jawaban


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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