This title slide is titled "Data Preprocessing dalam Data Mining dan Machine Learning." Its subtitle welcomes viewers with "Selamat Datang: Pelajari Tahap Krusial Analisis Data," introducing the crucial preprocessing stage in data analysis.

Data Preprocessing dalam Data Mining dan Machine Learning

Selamat Datang: Pelajari Tahap Krusial Analisis Data

Source: Topik: Data Preprocessing dalam Data Mining dan Machine Learning

This slide presents the agenda for a data preprocessing presentation, covering its definition as initial data cleaning and transformation. It includes the importance for ML accuracy, key steps and techniques like imputation and normalization, plus real-world cases in finance/medicine and conclusions.

Agenda Presentasi

1. 1. Pengertian Data Preprocessing

Tahap awal pembersihan dan transformasi data mentah menjadi format terstruktur.

2. 2. Pentingnya Data Preprocessing

Mengurangi kesalahan analisis dan tingkatkan akurasi model machine learning.

3. 3. Langkah-langkah Preprocessing

Data cleaning, integration, transformation, reduction, dan discretization.

4. 4. Teknik & Metode

Imputasi missing values, outliers, normalisasi, dan encoding kategori.

5. 5. Contoh Kasus & Kesimpulan

Kasus nyata di keuangan dan kedokteran beserta ringkasan akhir. Source: Data Preprocessing dalam Data Mining dan Machine Learning

Data preprocessing is the initial stage of data analysis, involving cleaning and transforming raw data into a structured format ready for analysis. It is crucial for ensuring error-free data and preparing it for machine learning models.

Pengertian Data Preprocessing

• Tahap awal analisis data: pembersihan & transformasi data mentah
• Menjadi format terstruktur siap analisis
• Krusial untuk data bebas kesalahan
• Siap untuk model machine learning

Data preprocessing is essential as raw data is often imperfect. It reduces analysis errors, boosts ML model accuracy, and yields more reliable results.

Pentingnya Data Preprocessing

• Data mentah sering tidak sempurna
• Mengurangi kesalahan analisis
• Meningkatkan akurasi model ML
• Menghasilkan hasil lebih dapat dipercaya

Source: Data Preprocessing dalam Data Mining dan Machine Learning

The slide outlines the workflow for data preprocessing, covering five key steps: Data Cleaning, Integration, Transformation, Reduction, and Discretization. Each step includes a brief description and specific techniques, such as imputation for missing values, dataset merging, normalization/encoding, PCA, and binning.

Langkah-langkah Data Preprocessing

{ "headers": [ "Langkah", "Deskripsi", "Teknik" ], "rows": [ [ "Data Cleaning", "Pembersihan data dari missing values, duplikat, outliers", "Imputasi missing values, deteksi & penanganan outliers" ], [ "Data Integration", "Menggabungkan data dari berbagai sumber menjadi dataset konsisten", "Merging datasets, schema integration" ], [ "Data Transformation", "Mengubah skala/format data untuk keseragaman", "Normalisasi, standarisasi, encoding (One-Hot, Label)" ], [ "Data Reduction", "Mengurangi dimensi data tanpa hilang info penting", "Principal Component Analysis (PCA)" ], [ "Data Discretization", "Konversi data kontinu ke diskrit", "Binning, entropy-based discretization" ] ] }

Source: Data Preprocessing dalam Data Mining dan Machine Learning

The slide "Teknik dan Metode Utama" outlines four key data preprocessing techniques in a feature grid. It covers handling missing values via imputation or deletion, detecting and removing outliers, normalizing data for uniformity and performance, and encoding categorical variables with One Hot or Label methods.

Teknik dan Metode Utama

{ "features": [ { "icon": "❓", "heading": "Atasi Missing Values", "description": "Imputasi atau penghapusan data yang hilang." }, { "icon": "🔍", "heading": "Deteksi Outliers", "description": "Identifikasi dan hapus data ekstrem." }, { "icon": "⚖️", "heading": "Normalisasi Data", "description": "Ubah skala untuk keseragaman dan performa." }, { "icon": "🔢", "heading": "Encoding Kategorikal", "description": "One Hot atau Label Encoding variabel." } ] }

Source: Data Preprocessing dalam Data Mining dan Machine Learning

This slide showcases real-world examples of data preprocessing benefits under "Contoh Kasus Nyata." In financial analysis, it cleans historical stock data to boost prediction accuracy by 20% and investment reliability; in medicine, it integrates patient data for more accurate diagnoses and fewer errors.

Contoh Kasus Nyata

This slide, titled "Manfaat Utama," presents a quote from Prof. Dr. Ahmad Santoso, an expert in Data Mining & Machine Learning. The quote emphasizes that well-processed data yields more accurate and reliable models, positioning preprocessing as the core foundation of data analysis success.

Manfaat Utama

> Data yang diproses dengan baik menghasilkan model yang lebih akurat dan dapat diandalkan. Preprocessing adalah fondasi utama kesuksesan analisis data.

— Prof. Dr. Ahmad Santoso, Pakar Data Mining & Machine Learning

Source: Data Preprocessing dalam Data Mining dan Machine Learning

The conclusion slide states that data preprocessing is essential for successful data mining and machine learning, urging the application of discussed steps and techniques for optimal results. It closes with "Thank you! Q&A?"

Kesimpulan

Data Preprocessing esensial untuk sukses Data Mining & ML. Terapkan langkah & teknik ini untuk hasil optimal.

Terima kasih! Q&A?

Source: Topik: Data Preprocessing dalam Data Mining dan Machine Learning