Presentasi Proyek / Studi Kasus Data Warehouse


๐ŸŸข 1. Pendahuluan

๐Ÿ“Œ Pengertian Presentasi Proyek dan Studi Kasus

Presentasi proyek merupakan tahap akhir dalam pembelajaran Data Warehouse yang bertujuan untuk:

  • mempresentasikan hasil proyek,
  • menjelaskan implementasi sistem,
  • menunjukkan analisis data,
  • memvisualisasikan dashboard,
  • mempertanggungjawabkan solusi yang dibuat.

๐Ÿ” Narasi

Dalam dunia industri, kemampuan teknis saja tidak cukup. Seorang analis data atau data engineer juga harus mampu:

  • menjelaskan hasil analisis,
  • mempresentasikan dashboard,
  • menyampaikan solusi bisnis,
  • menjawab pertanyaan stakeholder.

Karena itu, presentasi proyek menjadi bagian penting dalam mata kuliah Data Warehouse.


๐Ÿ–ผ๏ธ Ilustrasi Presentasi Proyek

https://images.openai.com/static-rsc-4/EHYHnSf_PUO_gZpyjmNAeTMukZ561WZgI2uX4IQpfOoAKrpDJciJWGR33npTA-CsCwt8iCTj5L0-r6C5WKzAiznL980yM4OehGK0yDb-Xer3Mple94SShrcaiyIjkw-1XPFfDXiSI6CUyM-fJ-EEGkJdH7sL6P4AZjv9IOvH65VWf7sy4WjJcLzyYmoArDNh?purpose=fullsize
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๐ŸŸข 2. Tujuan Presentasi Proyek

๐Ÿ“Œ Tujuan Utama

  1. Menjelaskan hasil implementasi
  2. Menampilkan dashboard
  3. Menunjukkan analisis data
  4. Menjelaskan proses ETL
  5. Menyampaikan solusi bisnis

๐Ÿ” Narasi

Presentasi bukan hanya menunjukkan tampilan dashboard, tetapi juga:

  • proses pembangunan sistem,
  • kualitas data,
  • metode analisis,
  • manfaat bisnis.

๐Ÿ“Š Manfaat Presentasi

ManfaatPenjelasan
KomunikasiMenjelaskan solusi
EvaluasiMenilai proyek
DokumentasiArsip implementasi
AnalitikMenunjukkan insight

๐Ÿ–ผ๏ธ Business Presentation

https://images.openai.com/static-rsc-4/qV-xpRnT3VqS1hiopLLuZDBYLvcG3g6VS5U8gUyTCwggMY5k4CCxy6EvZ0nMkzj-KKURUoL2vL4dnW2T0JmqNHs0_1GUohouOR62sJMozsVFHbwg-kaRk-Y78Qyali6R6q_6-PRoDJOEIaJWfZtbSEH2AhjJaYS-IbpcegYAEVp3fet7IRO6-RVhj-eDRfKc?purpose=fullsize
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๐ŸŸข 3. Struktur Presentasi Proyek

๐Ÿ“Œ Struktur Umum

  1. Cover
  2. Latar Belakang
  3. Rumusan Masalah
  4. Tujuan
  5. Arsitektur Sistem
  6. Database & ETL
  7. Dashboard BI
  8. Analisis Data
  9. Kesimpulan
  10. Demo Sistem

๐Ÿ” Narasi

Struktur presentasi membantu audiens memahami alur proyek secara sistematis.

Presentasi yang baik:

  • terstruktur,
  • jelas,
  • visual,
  • mudah dipahami.

๐Ÿ–ผ๏ธ Struktur Presentasi

https://images.openai.com/static-rsc-4/ixxBg7othYEnSc-SKLddxKYcFj3aRs61vJ1YoqBVi6pDruMlLONzVCkr9rIl0dJ8dpdXPwNgO-vWMyFLRkc4LrrrybVmebgmnjtcv0QRv05DcqU2iSSGE_BZ8UExDK_DfJMn7Qr61rKFyVB1xK4j7t8UAl9T-l1u3eK3sE2TDnHJSaYGKktmBPygR5HI4lV7?purpose=fullsize
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๐ŸŸข 4. Penentuan Studi Kasus

๐Ÿ“Œ Contoh Studi Kasus

  • Dashboard Akademik
  • Sistem Analitik Penjualan
  • Dashboard Rumah Sakit
  • Analitik E-Commerce
  • Dashboard Keuangan
  • Smart Campus Analytics

๐Ÿ” Narasi

Pemilihan studi kasus harus:

  • realistis,
  • relevan,
  • memiliki data,
  • dapat dianalisis.

๐Ÿ“Š Contoh Tema Proyek

BidangStudi Kasus
PendidikanDashboard mahasiswa
RetailAnalisis penjualan
PerbankanFraud detection
KesehatanAnalisis pasien

๐Ÿ–ผ๏ธ Studi Kasus Data Analytics

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๐ŸŸข 5. Analisis Kebutuhan Sistem

๐Ÿ“Œ Pengertian

Menentukan:

  • kebutuhan pengguna,
  • data yang dibutuhkan,
  • laporan yang dihasilkan.

๐Ÿ” Narasi

Tahap ini sangat penting karena menentukan arah pembangunan Data Warehouse.


๐Ÿ“Š Contoh Kebutuhan

PenggunaKebutuhan
PimpinanDashboard KPI
AdminLaporan data
DosenStatistik mahasiswa

๐Ÿ–ผ๏ธ Requirement Analysis

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๐ŸŸข 6. Perancangan Arsitektur Data Warehouse

๐Ÿ“Œ Komponen Arsitektur

  • Data Source
  • ETL
  • Staging Area
  • Data Warehouse
  • Data Mart
  • BI Dashboard

๐Ÿ” Narasi

Arsitektur menjadi fondasi utama proyek Data Warehouse.


๐Ÿ–ผ๏ธ Arsitektur DW

https://images.openai.com/static-rsc-4/MhmqZ4bnvI5qQkaRzvryCM4XHeopp_cTxS7yPXaemNTLbwZFGkOFPhXYYrhOCvwZL8V3SO_Ns-3D-geVVbxI3vlJLOl54xjzmSjz3NNvOhV409DKiPi9Q5BXNmKc9bURvkfciUThCiPgfSuSXujFbrHqiY47sHYjEgYTWwNfO8cmSPxgjkuupxno3qMP0Gpj?purpose=fullsize
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๐ŸŸข 7. Desain Database dan Schema

๐Ÿ“Œ Jenis Schema

  • Star Schema
  • Snowflake Schema

๐Ÿ” Narasi

Schema menentukan:

  • performa query,
  • struktur analitik,
  • kemudahan dashboard.

๐Ÿ“Š Contoh Struktur

TabelFungsi
Fact TableMenyimpan transaksi
Dimension TableMenyimpan atribut

๐Ÿ–ผ๏ธ Star Schema Diagram

https://images.openai.com/static-rsc-4/O7JdpDFTQKXRzcCYDQH0wt1byIFS0CiHJT2msgoMDr8XN_r6n3x6aNLMU8Rkq8m3vNmNh1-O8WZm-ikdbz6tMyGG5RBT7dp5L54jAEpBtSrzY8nf-Yusqga9jFTcPKEwptPipwv28OAYCj6MDT3-Egesh2-08oFBy68uV3e633g6SaDv5WSkQ6K0A9sXtmBL?purpose=fullsize
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๐ŸŸข 8. Implementasi ETL

๐Ÿ“Œ Pengertian ETL

ETL:

  • Extract,
  • Transform,
  • Load.

๐Ÿ” Narasi

ETL digunakan untuk:

  • mengambil data,
  • membersihkan data,
  • integrasi data,
  • load ke warehouse.

๐Ÿ“Š Tahapan ETL

TahapFungsi
ExtractMengambil data
TransformMembersihkan data
LoadMemasukkan data

๐Ÿ–ผ๏ธ ETL Workflow

https://images.openai.com/static-rsc-4/aCmnoYvTd9hhAv-hrXsQ4WAEK2gz6S_ts4TfmYUKLfqRl9qb2txaKV6O7DjbwAdsYrzneJPJCKPhppn21v9P3TlohVU8QCVUQzx14nk_cS9LBpyNti-fvkQzmT8Q-J9rv7CaS9zxt1vD2ugC3AYipRz8ygIXVyjja7WfeufwSZxiqpeuG-c2f4mqb6eAOrVb?purpose=fullsize
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๐ŸŸข 9. Dashboard dan Visualisasi

๐Ÿ“Œ Tujuan Dashboard

  • menampilkan insight,
  • monitoring KPI,
  • visualisasi data.

๐Ÿ” Narasi

Dashboard membantu pengguna memahami data dengan cepat melalui:

  • grafik,
  • tabel,
  • KPI,
  • visualisasi interaktif.

๐Ÿ“Š Jenis Visualisasi

VisualisasiFungsi
Bar ChartPerbandingan
Line ChartTren
Pie ChartPersentase
KPI CardRingkasan

๐Ÿ–ผ๏ธ Dashboard BI

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๐ŸŸข 10. Analisis dan Insight Data

๐Ÿ“Œ Pengertian Insight

Insight adalah informasi penting yang diperoleh dari analisis data.


๐Ÿ” Narasi

Tujuan utama warehouse bukan hanya menyimpan data, tetapi menghasilkan insight bisnis.


๐Ÿ“Œ Contoh Insight

  • mahasiswa dengan IPK rendah,
  • tren penjualan meningkat,
  • pelanggan paling aktif.

๐Ÿ–ผ๏ธ Data Analytics Insight

https://images.openai.com/static-rsc-4/vRzyWnUPXH8j8MERIGXaF6tXw_vvzgiTGIBT4gch7cDsAkL0DV5sRW6SR2hFvBCrQ2HM9ZeWqBXdr8FoDtsKjiF7Hth-ztMTzB5vJWAvlYiReSQagNOVYIK0MOEAkPDZ5ZKMPT6nHsKBcC8NpLf6LFh0kiOkZeSn0ODPwIBjhGgtiLtz-AKNnLbkcGetwcQm?purpose=fullsize
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๐ŸŸข 11. Presentasi Dashboard

๐Ÿ“Œ Tips Presentasi Dashboard

  • Gunakan visual sederhana
  • Fokus pada KPI utama
  • Jelaskan insight
  • Gunakan warna konsisten
  • Hindari dashboard terlalu penuh

๐Ÿ” Narasi

Dashboard yang baik harus:

  • mudah dipahami,
  • cepat dibaca,
  • menarik secara visual.

๐Ÿ–ผ๏ธ Executive Dashboard

https://images.openai.com/static-rsc-4/mPI6_nyJXkLny4SXHPafYaIDxvq5RpaCRAL9QZCorm7KFJ0SYd7tI2t0_pp8NHzJxVfM1IRdjNqUUBoDitHR4qntu3EROYWbqvlbU9i6n7JuBaQ_NDKn12reog0BKsJ5IkV_nJ5j2DbbxbV4qXMOGz7GZxrlmsUSjomYFy0s2slbJWqyIziJItf3EO7Ha_u2?purpose=fullsize
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๐ŸŸข 12. Teknik Presentasi Profesional

๐Ÿ“Œ Teknik Penting

  1. Public speaking
  2. Eye contact
  3. Storytelling data
  4. Slide sederhana
  5. Komunikasi jelas

๐Ÿ” Narasi

Data storytelling membantu audiens memahami:

  • masalah,
  • analisis,
  • solusi,
  • hasil.

๐Ÿ“Š Kesalahan Umum Presentasi

KesalahanDampak
Slide penuh teksMembosankan
Dashboard rumitSulit dipahami
Tidak menjelaskan insightPresentasi lemah

๐Ÿ–ผ๏ธ Professional Presentation

https://images.openai.com/static-rsc-4/9QMojRMJ6ZgHs_rkKHpj24zmGthUXXH2iVuEjJuDFpngGJCAgph0ecZJtol4OvbnTMHdFqZMQx7Pf_aqtrkmUxyJbvlAxJFvJTcJOdtXxihn5c3WybaOX480RFPHbFcm7spCFDUyeVnoE4CGXXqp9QtFIpHdnlZeJjKjUGE-z3Lv8e0-e6gGaOwY2H8ZRBAh?purpose=fullsize
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๐ŸŸข 13. Evaluasi dan Penilaian Proyek

๐Ÿ“Œ Aspek Penilaian

  • Desain warehouse
  • Implementasi ETL
  • Dashboard BI
  • Analisis data
  • Presentasi
  • Dokumentasi

๐Ÿ“Š Rubrik Penilaian

AspekBobot
Database & Schema20%
ETL20%
Dashboard25%
Analisis20%
Presentasi15%

๐Ÿ–ผ๏ธ Project Evaluation

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๐ŸŸข 14. Studi Kasus Lengkap

๐Ÿ“Œ Studi Kasus: Dashboard Akademik Kampus

๐Ÿ” Permasalahan

Pimpinan kampus kesulitan:

  • melihat statistik mahasiswa,
  • monitoring IPK,
  • analisis kelulusan.

๐Ÿ“Œ Solusi

Membangun:

  • Data Warehouse,
  • dashboard Power BI,
  • ETL akademik.

๐Ÿ“Œ Hasil

Dashboard menampilkan:

  • jumlah mahasiswa,
  • IPK rata-rata,
  • performa dosen,
  • statistik kelulusan.

๐Ÿ–ผ๏ธ Dashboard Akademik

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๐ŸŸข 15. Tutorial Praktikum

๐Ÿ’ป Praktikum Presentasi Proyek Data Warehouse

๐Ÿ“Œ Tools

  • MySQL/PostgreSQL
  • Power BI
  • Canva/PowerPoint

๐ŸŸก Langkah 1 โ€” Membuat Database

CREATE DATABASE dw_kampus;

๐ŸŸก Langkah 2 โ€” Membuat Tabel

CREATE TABLE mahasiswa (
nim VARCHAR(10),
nama VARCHAR(100),
prodi VARCHAR(50),
ipk DECIMAL(3,2)
);

๐ŸŸก Langkah 3 โ€” Input Data

INSERT INTO mahasiswa VALUES
('22001','Andi','Informatika',3.80),
('22002','Budi','Sistem Informasi',3.60);

๐ŸŸก Langkah 4 โ€” Query Analitik

SELECT prodi, AVG(ipk)
FROM mahasiswa
GROUP BY prodi;

๐ŸŸก Langkah 5 โ€” Dashboard BI

Buat:

  • grafik mahasiswa,
  • KPI IPK,
  • dashboard akademik.

๐ŸŸก Langkah 6 โ€” Presentasi

Jelaskan:

  • arsitektur,
  • ETL,
  • insight,
  • dashboard.

๐Ÿ–ผ๏ธ Tutorial Dashboard Presentation

https://images.openai.com/static-rsc-4/q2EA-dsNSWAI2UiGHNJYalSUvJrr2s5aCAh-Bv8wzKOAquBS1zLOIioagihIqg_6lBopDPGNviSSlYqfkrFiUWscpvo9U4P_zug6N2VDIE0AslkeAfvQzOKkFpd2RX3aY5kGR0CjmEnBrKp_S3wJ8oAHQPbEIFUG9AStzyMEkbbelaLegJq8ayQ9l8RTAeFx?purpose=fullsize
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7


๐ŸŸข 16. Best Practice Proyek Data Warehouse

๐Ÿ“Œ Tips Sukses

  • Gunakan data realistis
  • Dashboard sederhana
  • Fokus pada insight
  • Gunakan visual menarik
  • Jelaskan manfaat bisnis

๐ŸŸข 17. Latihan Mahasiswa

๐ŸŽฏ Latihan Teori

  1. Jelaskan tujuan presentasi proyek!
  2. Apa fungsi dashboard BI?
  3. Mengapa insight penting?
  4. Apa fungsi ETL dalam proyek?
  5. Jelaskan pentingnya Data Warehouse!

๐ŸŽฏ Latihan Praktikum

Buat:

  • proyek DW sederhana,
  • dashboard akademik,
  • presentasi kelompok.

๐ŸŸข 18. Diskusi Kelas

๐Ÿ’ฌ Topik Diskusi

  1. Apa tantangan terbesar presentasi proyek?
  2. Dashboard seperti apa yang efektif?
  3. Mengapa storytelling penting?
  4. Bagaimana AI membantu dashboard modern?

๐ŸŸข 19. Kesimpulan

๐Ÿ“Œ Ringkasan

Presentasi proyek dan studi kasus merupakan tahap penting dalam implementasi Data Warehouse.

Mahasiswa tidak hanya belajar:

  • database,
  • ETL,
  • dashboard,

tetapi juga:

  • komunikasi,
  • analisis bisnis,
  • data storytelling,
  • presentasi profesional.

Kemampuan tersebut sangat dibutuhkan dalam dunia industri modern berbasis:

  • Big Data,
  • Business Intelligence,
  • Artificial Intelligence,
  • Data Analytics.