Soal-Soal UTS Data Mining Jurusan Teknik Informatika

Soal-Soal UTS Data Mining Jurusan Teknik Informatika

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Jelaskan pengertian datamining dan KDD!

Serangkaian proses untuk menggali nilai tambah berupa informasi yang selama ini tidak diketahui secara manual dari suatu basisdata. Informasi yang dihasilkan diperoleh dengan cara mengekstraksi dan mengenali pola yang penting atau menarik dari data yang terdapat dalam basisdata.

Sebutkan hal-hal yang melatarbelakangi datamining?

Melimpahnya data (overload data) yang dialami oleh berbagai institusi, perusahaan atau organisasi.
Merlimpahnya data ini merupakan akumulasi data transaksi yang terekam bertahun-tahun..
Data–data tersebut merupakan data transaksi yang umumnya diproses menggunakan aplikasi komputer yang biasa disebut dengan OLTP (On Line Transaction Processing).

Sebutkan dan jelaskan manfaat penggunaan datamining?

Dari sudut pandang komersial,
pemanfaatan dataming dapat digunakan dalam menangani meledaknya volume data. Bagaimana mana menyimpannya,  mengestraknya serta memanfaaatkannya. Berbagai teknik komputasi dapat digunakan menghasilkan informasi yang dibutuhkan. Informasi yang dihasilkan menjadi asset untuk meningkatkan daya saing suatu institusi. Data mining tidak hanya digunakan untuk menangani persoalan menumpuknya data/informasi dan bagaimana menggudangkannya tanpa kehilangan informasi yang penting (warehousing). Data mining juga diperlukan untuk menyelesaikan permasalahan atau menjawab kebutuhan bisnis itu sendiri, misalnya:
Bagaimana mengetahui hilangnya pelanggan karena pesaing
Bagaimana mengetahui item produk atau konsumen yang memiliki kesamaan karakteristik
Bagaimana mengidentifikasi produk-produk yang terjual bersamaan dengan produk lain.
Bagaimana memprediski tingkat penjualan
Bagaimana menilai tingkat resiko dalam menentukan jumlah produksi suatu item.
Bagaimana memprediksi prilaku bisnis di masa yang akan datang
Remote sensor yang ditempatkan pada suatu satelit
Telescope yang digunakan untuk memindai langit
Simulasi saintifik yang membangkitkan data dalam ukuran terabytes
Dari sudut pandang keilmuan
Data mining dapat digunakan untuk mengcapture, menganlisis serta menyimpan data yang bersifat real-time dan sangat besar, misalnya:

Sebutkan dan jelaskan manfaat secara singkat tahap KDD?

KDD adalah keseluruhan proses non-trivial untuk mencari dan mengidentifikasi pola (pattern) dalam data, dimana pola yang ditemukan bersifat sah, baru, dapat bermanfaat dan dapat dimengerti. Serangkaian proses tersebut yang memiliki tahap sebagai berikut
Pembersihan data dan integrasi data (cleaning and integration)
Proses ini digunakan untuk membuang data yang tidak konsisten dan bersifat noise dari data yang terdapat di berbagai basisdata yang mungkin berbeda format maupun platform yang kemudian diintegrasikan dalam satu database datawarehouse.

Sebutkan dan jelaskan berbagai metode seleksi?

Sampling, adalah seleksi subset representatif dari populasi data yang besar.
Denoising, adalah proses menghilangkan noise dari data yang akan ditransformasikan
Feature extraction, adalah proses membuka spesifikasi data yang signifikan dalam konteks tertentu.
Sebutkan dan jelaskan berbagai metode transformasi?
Centering, mengurangi setiap data dengan rata-rata dari setiap atribut yang ada.
Normalisation, membagi setiap data yang dicentering dengan standar deviasi dari atribut bersangkutan.
Scaling, mengubah data sehingga berada dalam skala tertentu.
Sebutkan dan jelaskan minimal 4 fungsi yang umum diterapkan dalam datamining?
Assosiation, adalah proses untuk menemukan aturan assosiatif antarasuatu kombinasi item dalam suatu waktu
Secuence, hampir sama dengan association bedanya seccuencediterapkan lebih dari satu periode.
Clastering, adalah proses pengelompokan sejumlah data/obyek kedalam kelompok-kelompok data (klaster) sehingga setiap klaster akanberisi data yang saling mirip.
Classification, adalah proses penemuan model atau fungsi yangmenjelaskan atau membedakan konsep atau kelas data, dengan tujuanuntuk dapat memperkirakan kelas dari suatu objek yang labelnya tidakdiketahui.


Berikan 4 contoh dan penjelasan implementasi datamining?


Analisa Pasar dan Manajemen
Untuk analisa pasar, banyak sekali sumber data yang dapat digunakan seperti transaksi kartu kredit, kartu anggota club tertentu, kupon diskon, keluhan pembeli, ditambah dengan studi tentang gaya hidup publik.

Telekomunikasi
Sebuah perusahaan telekomunikasi menerapkan data mining untuk melihatdari jutaan transaksi yang masuk, transaksi mana sajakah yang masih harusditangani secara manual (dilayani oleh orang). Tujuannya tidak lain adalahuntuk menambah layanan otomatis khusus untuk transaksi-transaksi yangmasih dilayani secara manual. Dengan demikian jumlah operator penerimatransaksi manual tetap bisa ditekan minimal.

Keuangan
Financial Crimes Enforcement Network di Amerika Serikat baru-baru inimenggunakan data mining untuk me-nambang trilyunan dari berbagai subyek seperti property, rekening bank dan transaksi keuangan lainnya untuk mendeteksi transaksi-transaksi keuangan yang mencurigakan (seperti money laundry). Mereka menyatakan bahwa hal tersebut akan susah dilakukan jika menggunakan analisis standar.  (www.senate.gov/~appropriations/treasury/testimony/sloan.htm. Mungkinsudah saatnya juga Badan Pemeriksa Keuangan Republik Indonesia menggunakan teknologi ini untuk mendeteksi aliran dana BLBI.

Asuransi
Australian Health Insurance Commision menggunakan data mining untukmengidentifikasi layanan kesehatan yang sebenarnya tidak perlu tetapi tetap dilakukan oleh peserta asuransi. Hasilnya? Mereka berhasil menghemat satu juta dollar per tahunnya. ATentu saja ini tidak hanya bias diterapkan untuk asuransi kesehatan, tetapi juga untuk berbagai jenis asuransi lainnya.

Jelaskan pengetian dari metodologi datamining berikut ini?

Regresi, Adalah fungsi pembelajaran yang memetakan sebuah unsure data ke sebuah variable prediksi bernilai nyata.
Jaringan syaraf tiruan, Adalah jaringan dari sekelompok unit pemroses kecil yang dimodelkan berdasarkan jaringan saraf manusia. JST merupakan sistem adaptif yang dapat merubah strukturnya untuk memecahkan masalah berdasarkan informasi eksternal maupun internal yang mengalir melalui jaringan tersebut.
Pohon keputusan,  adalah digunakan untuk memodelkan persoalan yang terdiri dari serangkaian keputusan yang mengarah ke solusi. Tiap simpul dalam menyatakan keputusan, sedangkan daun menyatakan solusi.
Algoritma genetika, adalah teknik pencarian yang di dalam ilmu komputer untuk menemukan penyelesaian perkiraan untuk optimisasi dan masalah pencarian. Atau juga dapat disebut kelas khusus dari algoritma evolusioner dengan menggunakan teknik yang terinspirasi oleh biologi evolusioner seperti warisan, mutasi, seleksi alam dan rekombinasi (atau crossover)

Jelaskan pengertian datawarehousing dari inmon dan Poe!

Menurut W.H. Inmon dan Richard D.H., data warehousing adalah koleksi data yang mempunyai sifat berorientasi subjek,terintegrasi,time-variant, dan bersifat tetap dari koleksi data dalam mendukung proses pengambilan keputusan management.
Menurut Vidette Poe, data warehousing merupakan basisdata yang bersifat analisis dan read only yang digunakan sebagai fondasi dari sistem penunjang keputusan.
Jelaskan pengertian dari istilah-istilah warehousing berikut ini?
Datamart, adalah Data warehouse yang mendukung kebutuhan pada tingkat departemen atau fungsi bisinis tertentu dalam perusahaan.
OLAP, adalah sebuah pendekatan secara cepat menyediakan jawaban-jawaban terhadap kueri analitik yang multidimensi di dalam alam. OLAP adalah bagian dari kategori yang lebih global dari pemikiran bisnis, yang juga merangkum hubungan antara pelaporan dan penggalian data.
Dimention Table, adalah satu kumpulan tabel teman untuk meja fakta.
Fact Table, adalah tabel yang berisi pengukuran tentang meter atau fakta cara bisnis . itu sering ditempatkan pada memusat skema bintang dan dikelilingi oleh tabel ukuran

Jelaskan perbedaan-perbedaan antara data operasional dengan data warehouse?

Data Operasional
Dirancang berorientasi hanya pada aplikasi dan fungsi tertentu
Fokusnya pada desain basisdata dan proses
Berisi rincian atau detail data
Relasi antar table berdasar aturan terkini(selalu mengikuti aturan terbaru)
Data Warehouse
Dirancang berdasar pada subjek-subjek tertentu (utama)
Focusnya pada pemodelan data dan desain data
Berisi data-data history yang akan dipakai dalam proses analisis
Banyak aturan bisnis dapat tersaji antara tabel-tabel

Sebutkan dan jelaskan secara singkat tugas-tugas yang dilakukan data warehouse?

Pembuatan laporan, Pembuatan laporan merupakan salah satu kegunaan data warehousing yang paling umum dilakukan. Dengan menggunakan query sederhana didapatkan laporan perhari,perbulan, pertahun atau jangka waktu kapanpun yang diinginkan.
On-Line Analytical Processing (OLAP), Dengan adanya data warehouse, semua informasi baik detail maupun hasil summary yang dibutuhkan dalam proses analisa mudah didapat. OLAP mendayagunakan konsep data multi dimensi dan memungkinkan para pemakai menganalisa data sampai mendetail, tanpa mengetikkan satupun perintah SQL. Hal ini dimungkinkan karena pada konsep multi dimensi, maka data yang berupa fakta yang sama bisa dilihat dengan menggunakan fungsi yang berbeda. Fasilitas lain yang ada pada sofware OLAP adalah fasilitas rool-up dan drill-down. Drill-down adalah kemampuan untuk melihat detail dari suatu informasi dan roll-up adalah kebalikannya.
Proses informasi eksekutif, data warehousing dapat membuat ringkasan informasi yang penting dengan tujuan membuat keputusan bisnis, tanpa harus menjelajahi keseluruhan data. Dengan menggunakan data warehousing segala laporan telah diringkas dan dapat pula mengetahui segala rinciannya secara lengkap, sehingga mempermudah proses pengambilan keputusan. Informasi dan data pada laporan data warehousing menjadi target informatif bagi pengguna.


Sebutkan 4 keuntungan menggunakan data warehouse!


Data diorganisir dengan baik untuk query analisis dan sebagai bahan untuk pemrosesan transaksi.
Perbedaan diantara struktur data yang heterogen pada beberapa sumber yang terpisah dapat diatasi.
Aturan untuk transformasi data diterapkan untuk memvalidasi dan mengkonsolidasi data apabila data dipindahkan dari basisdata OLTP ke data warehouse
Masalah keamanan dan kinerja bisa dipecahkan tanpa perlu mengubah sistem produksi.
Sebutkan dan jelaskan secara singkat tiga jenis dasar sistem data warehouse?
Data Warehouse Fungsional, Data warehouse dibuat lebih dari satu dan dikelompokan berdasar fungsi yang ada di dalam perusahaan seperti fungsi keuangan, marketing dan personalia.
Data Warehouse Terpusta, Sumber data dikumpulkan dalam satu tempat tempat terpusat, kemudian data tersebar ke dalam fungsinya masing-masing sesuai kebutuhan perusahaan.
Data Warehouse Terdistribusi, dalam warehouse ini digunakan gateway yang berfungsi sebagai jemabatan penghubung antara data warehouse dengan workstation yang menggunakan sistem beranek ragam. Jadi dapat perusahaan dapat mengakses sumber data yang berada di luar lokasi perusahaan.


Sebutkan 3 jenis dasar dan kelebihan dari 3 jenis dasar sistem data warehouse!


Data Warehouse Fungsional, yaitu sistem mudah dibangun dengan biaya relatif murah.
Data Warehouse Terpusat, yaitu data benar-benar terpadu karena konsistensinya yang tinggi.
Data Warehouse Terdistribusi,nya yaitu data tetap konsisten karena sebelum data di gunakan data terlebih dahulu di sesuaikan atau mengalami proses sinkronisasi.

Sebutkan dan jelaskan komponen dari struktur data warehouse!

Current detail data, data detail yg aktif saat ini
Older detail data, “Merupakan data historis dari curen detail data
Lightly summarized data, merupakan hasil ringkasan dari curen detail data
Highly summarized data, Merupakan hasil ringkasan yang bersifat totalitas
Metadata, data tentang data

 

Sebutkan dan jelaskan 4 tahap awal metodologi dalam perancangan  basis data untuk data warehouse?

Predictive modeling,Adalah untuk membantu model untuk memprediksi suatu nilai yang mempunyai cirri-ciri tertentu.
Association analysis, adalah untuk menghasilkan sejumlah rule yang menjelaskan sejumlah data yang terhubung kuat satu dengan yang lainnya.
Clustering,Adalah untuk mengelompokan data yang sejenis sehingga data yang berada di cluster yang sama mempunyai banyak kesamaan dibandingkan dengan data yang ada di cluster yang berbeda.
Anomaly detection, adalah untuk menemukan anomaly atau outlier yaitu data yang berbeda dengan data-data yang lainnya.

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