Agregasi Data dengan Rapidminer - Seri Perkuliahan Data Analytic & Data Mining #08

[@CimolOk-nz5yj]

🎯 Key Takeaways for quick navigation: 00:00 🎓 *Introduction to Data Aggregation Using Rapidminer* - Introduction to the video and the topic of data aggregation in data analytics. - Overview of the video's focus on the 'aggregate' operator in Rapidminer. 01:22 💾 *Loading and Preparing the Data* - Loading a large dataset from Google for data analysis. - Adjusting data types and ensuring data integrity. 02:26 🛠️ *Discussing the Aggregate Operator* - Explanation of the aggregate operator's functionality in data analysis. - Importance of the operator for extracting key information from data. 03:37 📁 *Storing Data in Rapidminer Repository* - Storing the dataset in Rapidminer's repository for efficient access. - Benefits of repository storage for data processing speed. 06:09 📈 *Utilizing the Aggregate Operator for Data Analysis* - Application of the aggregate operator to summarize and extract essential data. - Demonstrating the use of statistical functions in aggregation. 11:04 🔍 *Detailed Explanation of the Aggregate Function* - In-depth tutorial on setting up and using the aggregate function. - Examples of different statistical functions within the aggregate operator. 14:17 🚫 *Filtering Out Irrelevant Data* - Filtering process to remove irrelevant or erroneous data from the dataset. - Techniques for refining data before aggregation. 18:32 📊 *Aggregating Data by Brand* - Aggregating sales data by brand to analyze income per brand. - Illustrating the process of grouping data for detailed analysis. 22:20 📉 *Trend Analysis Using Aggregate Data* - Analyzing trends in sales data based on brand aggregation. - Visualizing data to identify sales trends for different brands. 25:04 📅 *Aggregating Data by Date* - Adjusting the aggregate function to analyze daily sales data. - Techniques for refining date data for accurate aggregation. 30:19 ⚙️ *Modifying Data Types for Accurate Aggregation* - Converting and formatting date data for proper aggregation. - Importance of data type manipulation in accurate data analysis. 33:52 📆 *Daily Sales Trend Analysis* - Analysis of daily sales data to identify trends and patterns. - Visualization of sales trends over different days. 36:20 🔄 *Seasonal Trend Analysis* - Identifying seasonal trends in sales data using aggregate functions. - Discussion on the implications of these trends for business strategies. 38:09 🛒 *Aggregating Sales Data by Month* - Shifting focus to monthly sales data for broader trend analysis. - Techniques for adjusting data aggregation parameters for monthly analysis. 40:33 🏷️ *Brand Sales Volume Analysis* - Aggregating data to analyze the volume of sales per brand each month. - Utilizing count function in aggregation for quantity analysis. 44:56 📚 *Conclusion and Further Learning Opportunities* - Summary of the aggregate operator's capabilities and benefits. - Invitation for further exploration and learning in data analytics. Made with HARPA AI

[@sauchadiwandari8401]

Permisi pak. KZbinnya kok gak update2 yak

[@KuliahInformatika]

Hehehe... mohon maaf ya buk saucha, belum sempet update lagi.. Tapi insya Allah sudah ada rencana update dalam waktu dekat :)

[@srinuryantizebuazebua2035]

Pak bagaimana cara kita mendapatkan hasilnya

[@KuliahInformatika]

Mudahnya, tabel resultnya diseleksi semua (Ctrl + A), lalu copy, dan paste ke Ms. Excel. Atau bisa menggunakan operator "Write to Excel".

[@estherbpkp3633]

mantap pak

[@KuliahInformatika]

Halo Bu Ester... tengkyu sudah mampir ya bu. Senang bisa kenal bu ester. Mudah2an jumpa lagi di lain kesempatan 😁

[@coba6979]

Permisi pak ijin mau bertanya cara menjumlahkan atau meng rata-ratakan nilai/harga di sebuah data bagaimana ya pak, jika type data nya polynominal dan tidak bisa di ubah ke numeric(akan menjadi "?" Jika diubah) dan hanya bisa di ubah ke nominal saja 🙏

[@KuliahInformatika]

kalo datanya bertipe nominal, tidak bisa dihitung nilai rata-ratanya mas :) artinya data tersebut mengandung nilai yang non-numerik (nilai selain angka). Fungsi aggregate yang biasanya dipakai untuk data nominal biasanya count (menghitung cacah). Coba datanya dicek lagi, itu data 'harga'-nya mungkin mengandung format currency (ada tulisan Rp atau mata uang lainnya), sehingga sebelum diagregasikan, harus dihapus dulu tulisan Rp atau simbol mata uangnya. Semoga membantu

[@ayt_daily]

Permisi pak, apakah dalam proses agregasi dalam data mining sejatinya memang menghilangkan atribut yang tidak terpakai?

[@KuliahInformatika]

Bukan menghilangkan, tapi meringkas, mengolah dari sejumlah data yang ada untuk menghasilkan informasi yang lebih ringkas dan mudah dipahami. Misalnya kita punya data rapor siswa, kita bisa ringkas dengan menghitung rata2nya supaya tau bagaimana rata2 nilai di kelas itu

[@didinvo6987]

bapak buka kelas khusus ?

[@KuliahInformatika]

Mohon maaf, sementara belum. Saya masih full ngajar di kampus 🙏

[@nyongcahngapak]

Materinya menarik, tapi ko saya ngantuk ya :D

[@KuliahInformatika]

hehe... mungkin harus cuci muka dulu 😁