Materi - · PDF fileMacam-macam Sumber Data • Internal (dari dalam organisasi) ......
Transcript of Materi - · PDF fileMacam-macam Sumber Data • Internal (dari dalam organisasi) ......
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Materi
1. Era Informasi2. Strategi dan Peluang Yang Kompetitif3. Database dan Database Warehouse4. Desain Database5. Sistem Pendukung Keputusan dan Sistem Cerdas6. E-Commerce
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DATABASE DAN DATA WAREHOUSE
Pertemuan 06
2 SKS
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Data dalam SPK• Data merupakan elemen penting
dalam menentukan kualitas suatu SPK.
• Data yang buruk atau tidak lengkap menyebabkan SPK tidak mencapai hasil yang optimal/bagus.
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Preprocessing Data• Data Warehouse :
–Tempat kumpulan data yang digunakan untuk pengambilan keputusan, dikumpulkan dari berbagai sumber dan biasanya terpisah dari database organisasi/perusahaan.
• Data Mining : –Memilih data berdasarkan pola tertentu
sehingga diperoleh relasi antar variabel dan memiliki tingkat informasi yang lebih tinggi.
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Tingkatan Data• Data:
– Kumpulan sesuatu, kejadian, aktivitas, transaksi yang direkam, diklasifikasikan dan disimpan namun tidak diorganisasikan untuk memberikan arti tertentu.
• Informasi: – Data yang telah diorganisasikan sedemikian sehingga
memberikan arti bagi penerimanya.• Knowledge:
– Data/informasi yang memberikan pemahaman, pengalaman, pelajaran, keahlian yang berguna untuk pemecahan masalah.
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Macam-macam Sumber Data
• Internal (dari dalam organisasi)• Eksternal (dari luar organisasi)• Personal (dari tenaga ahli yang
berupa pendapat subjektif)
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Data Warehouse• Definisi :
– “A data warehouse is a copy of transaction data specifically structured for querying and reporting” (Ralph Kimball)
• Suatu database untuk pendukung keputusan yang disimpan terpisah dari database operasional suatu organisasi
• Mendukung pemrosesan informasi dengan menyediakan platform data yang historical dan consolidated untuk analisis.
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Data Warehousing• Data warehousing:
Proses konstruksi dan penggunaan data warehouses
• Data Warehousing berupaya mengumpulkan data-data dari berbagai sumber data sehingga mempunyai kualitas data yang bagus.
• Kualitas data yang bagus sangat mempengaruhi hasil keputusan.
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Kualitas Data (Data Quality)
• Kualitas data (DQ) dapat dilihat dari 4 katergori:– Contextual DQ: Relevansi, nilai tambah, timeliness,
kelengkapan dan jumlah data.– Intrinsic DQ: akurasi, objektivitas, keterpercayan,
reputasi.– Accessibility DQ: aksesibilitas, keamanan akses.– Representation DQ: interpretabilitas, kemudahan
untuk dimengerti, representasi yang ringkas dan konsisten.
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Arsitektur Data Warehouse
Arsitektur Data Warehouse 3-tier
ApplicationServer
Client
DatabaseServer
Application &Database
Server
Client
Arsitektur Data Warehouse 2-tier
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Organisasi dan Struktur Database dalam Data Warehouse
• Relational Databases. Berbentuk tabel.• Hierarchical Databases. Berbentuk
pohon atau bagan organisasi.• Network Databases. Berbentuk jaringan
kompleks.• Struktur Lain: objec-oriented,
multimedia-based, documen-based, intelligent databases.
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Karakteristik Data Warehousing (1)
• Subject-oriented. Data diorganisasi berdasarkan subyeknya. Mis: pelanggan
• Integrated. Data dari berbagai sumber disimpan dalam format yang sama. Mis: jenis kelamin : ‘L’ dan ‘P’. Maka data yang masuk mengalami konversi.
• Time-variant.Menyediakan data dari masa lampau hingga masa kini.
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Karakteristik Data Warehousing (2)
• Nonvolatile. Tidak berubah/hilang. Data dalam data warehouse tidak boleh diupdate.
• Summarized. Data operasional dapat digabungkan ke dalam ringkasan.
• Not normalized. Tidak ternormalisasi.• Metadata. Metadata (data tentang data)
disertakan antara lain deskripsi struktur, istilah dan definisi, kepemilikan data, dsb.
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Data Warehouse vs. Operational DBMS
• OLTP (on-line transaction processing)– Major task of traditional relational DBMS– Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll,
registration, accounting, etc.
• OLAP (on-line analytical processing)– Major task of data warehouse system– Data analysis and decision making
• Distinct features (OLTP vs. OLAP):– User and system orientation: customer vs. market– Data contents: current, detailed vs. historical, consolidated– Database design: ER + application vs. star + subject– View: current, local vs. evolutionary, integrated– Access patterns: update vs. read-only but complex queries
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OLTP vs. OLAP OLTP OLAP users clerk, IT professional knowledge worker function day to day operations decision support DB design application-oriented subject-oriented data current, up-to-date
detailed, flat relational isolated
historical, summarized, multidimensional integrated, consolidated
usage repetitive ad-hoc access read/write
index/hash on prim. key lots of scans
unit of work short, simple transaction complex query # records accessed tens millions #users thousands hundreds DB size 100MB-GB 100GB-TB metric transaction throughput query throughput, response
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Mengapa Memisahkan Datawarehouse
• High performance for both systems– DBMS— tuned for OLTP: access methods, indexing,
concurrency control, recovery– Warehouse—tuned for OLAP: complex OLAP
queries, multidimensional view, consolidation.• Different functions and different data:
– missing data: Decision support requires historical data which operational DBs do not typically maintain
– data consolidation: DS requires consolidation (aggregation, summarization) of data from heterogeneous sources
– data quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciled
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Model Konseptual dari Datawarehouse
• Modeling data warehouses: dimensions & measures– Star schema: A fact table in the middle connected to a set of
dimension tables
– Snowflake schema: A refinement of star schema where some dimensional hierarchy is normalized into a set of smaller dimension tables, forming a shape similar to snowflake
– Fact constellations: Multiple fact tables share dimension tables, viewed as a collection of stars, therefore called galaxy schema or fact constellation
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Contoh dari Star Schematime_keydayday_of_the_weekmonthquarteryear
time
location_keystreetcityprovince_or_streetcountry
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_salesMeasures
item_keyitem_namebrandtypesupplier_type
item
branch_keybranch_namebranch_type
branch
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Contoh Snow Flake Schematime_keydayday_of_the_weekmonthquarteryear
time
location_keystreetcity_key
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
item_keyitem_namebrandtypesupplier_key
item
branch_keybranch_namebranch_type
branch
supplier_keysupplier_type
supplier
city_keycityprovince_or_streetcountry
city
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Contoh Fact Constellationtime_keydayday_of_the_weekmonthquarteryear
time
location_keystreetcityprovince_or_streetcountry
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_salesMeasures
item_keyitem_namebrandtypesupplier_type
item
branch_keybranch_namebranch_type
branch
Shipping Fact Table
time_key
item_key
shipper_key
from_location
to_location
dollars_cost
units_shipped
shipper_keyshipper_namelocation_keyshipper_type
shipper
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Tiga Model Data Warehouse• Enterprise warehouse
– Mengumpulkan semua informasi tentang subjek-subjek yang menjangkau seluruh organisasi
• Data Mart– Sebuah subset dari corporate-wide data yang berguna untuk
kelompok pengguna tertentu. Ruang lingkupnya lebih spesifik seperti marketing data mart
• Independent vs. dependent (directly from warehouse) data mart
• Virtual warehouse– Sekumpulan view atas database-databases operational– Hanya beberapa dari view yang mungkin yang dapat
diwujudkan
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Penggunaan Data Warehouse• Pemrosesan informasi
– supports querying, basic statistical analysis, and reporting using crosstabs, tables, charts and graphs
• Analytical processing– multidimensional analysis of data warehouse data– supports basic OLAP operations, slice-dice, drilling, pivoting
• Data mining– knowledge discovery dari pola-pola tersembunyi– supports associations, constructing analytical models,
performing classification and prediction, and presenting the mining results using visualization tools.
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Dari OLAP ke OLAM (OnLine Analytical Mining)
• Why online analytical mining?– High quality of data in data warehouses
• DW contains integrated, consistent, cleaned data– Available information processing structure
surrounding data warehouses• ODBC, OLEDB, Web accessing, service facilities,
reporting and OLAP tools– OLAP-based exploratory data analysis
• mining with drilling, dicing, pivoting, etc.– On-line selection of data mining functions
• integration and swapping of multiple mining functions, algorithms, and tasks.
• Architecture of OLAM
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Contoh Arsitektur OLAM
Data Warehouse
Meta Data
MDDB
OLAMEngine
OLAPEngine
User GUI API
Data Cube API
Database API
Data cleaning
Data integration
Layer3
OLAP/OLAM
Layer2
MDDB
Layer1
Data Repository
Layer4
User Interface
Filtering&Integration Filtering
Databases
Mining query Mining result
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Data Mining• Istilah Data mining digunakan untuk
mendeskripsikan penemuan pengetahuan (knowledge) dalam database.
• Data mining merupakan proses yang menggunakan teknik, statistik, matematik, kecerdasan buatan dan machine-learning untuk mengekstrak dan mengidentifikasi informasi yang berguna dan pengetahuan dari database yang besar.
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Kovergensi dari Tiga Teknologi
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Metode Data Mining (1)• Data mining mencoba menemukan pola
dalam data.• Ada tiga jenis metode yang digunakan
untuk indentifikasi pola tersebut:– Simple models (SQL, OLAP, keputusan
manusia).– Intermediate models (regresi, decision
trees, clustering).– Complex models (neural network, dsb)
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Metode Data Mining (2), Complex Model
• Text Mining:– Library database, e-mails, book stores, Web pages.
• Spatial Data Mining:– Geographic information systems, medical image
database.
• Multimedia Mining:– Image and video/audio databases.
• Web Mining:– Unstructured and semi-structured data– Web access pattern analysis
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Metode Data Mining (3)• Metode data mining dapat pula
dikategorikan ke dalam 2 kategori:– Hypotesis-driven. Data mining dimulai dari
pernyataan yang kemudian diuji. Mis: “Apakah penjualan DVD player berkaitan dengan penjualan televisi?”
– Discovery-driven. Data mining mencari pola, asosiasi, dan hubungan antar data yang akhirnya dapat memberikan informasi lebih.
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Tingkatan Model• Beberapa model lebih baik dari model lainnya
– Accuracy– Understandability
• Model-model tersebut bervarias dari “easy to understand” ke tidak dapat dipahami– Decision trees– Rule induction– Regression models– Neural Networks
Lebih mudah
Lebih sulit
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Langkah-langkah Data Mining
• Seleksi. Memilih data.• Preprocessing. Mengatasi masalah data
rusak atau hilang.• Transformasi. Menyeragamkan format
data.• Data mining. Menerapkan algoritma data
mining.• Interpretasi/evaluasi. Evaluasi hasil.
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Fungsionalitas Data Mining (1)• Karakterisasi (Characterization):
Summarization of general features of objects in a target class. ( Concept description) Ex: Characterize grad students in Science
• Diskriminasi (Discrimination):Comparison of general features of objects between a target class and a contrasting class. (Concept comparison)Ex: Compare students in Science and students in Arts
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Fungsionalitas Data Mining (2)
• Asosiasi (Association):Studies the frequency of items occurring together in transactional databases.
Ex: buys(x, bread) buys(x, milk).• Prediksi (Prediction):
Predicts some unknown or missing attribute values based on other information.Ex: Forecast the sale value for next week based on available data.
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Fungsionalitas Data Mining (3)• Klasifikasi:
– Organizes data in given classes based on attribute values. (supervised classification)
– Ex: Labeling celestial objects, medical diagnostic, …
• Clustering:– Organizes data in classes based on attribute values.
(unsupervised classification)– Ex: group crime locations to find distribution
patterns.– Minimize inter-class similarity and maximize intra-
class similarity Similarity or dissimilarity-function ( distance)
• Outlier analysis:– Identifies and explains exceptions (surprises)
– Ex: fraud detection, rare event analysis
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Contoh Aplikasi Data Mining
• Marketing: mensegmentasi pelanggan secara demografis.
• Polisi: melacak pola kriminal, lokasi, perilaku kriminal dan sebagainya untuk membatu memecahkan kasus kriminal.
• Pabrikasi/Produksi: memperkirakan waktu kegagalan mesin, menemukan faktor-faktor penentu yang mengontrol optimisasi kapasitas pabrikasi.