datamining dengan rapidminer

Summary

In this video, Herdin Abdillah from Informatics Engineering at Krishna Dwipayana University demonstrates an anime recommendation system using RapidMiner, based on popularity and collaborative filtering. The process starts by importing a dataset from Kaggle, cleaning and filtering the data, and identifying popular anime based on member counts. The video then transitions to adding user ratings for anime, with the system incorporating both user preferences and popularity. The final output presents a top 10 list of anime with the highest member counts and ratings, showcasing the integration of data filtering and collaboration.

Takeaways

• 😀 Import and preprocess the dataset by cleaning column names and data types (e.g., changing 'mall ID' to 'anime ID').
• 😀 Use RapidMiner to design the recommendation system and import the necessary dataset from Kaggle's Anime Recommendation Database 2020.
• 😀 Filter the dataset by selecting relevant attributes, such as genre, anime ID, and number of members.
• 😀 Identify the popularity of anime by focusing on the number of members and filtering those with more than a specific number of members (e.g., 10,000).
• 😀 Sort the anime based on the number of members in descending order to highlight the most popular ones.
• 😀 Use the 'Filter Example Range' operator to select the top 10 most popular anime based on member count.
• 😀 Introduce collaborative filtering by incorporating ratings from users, such as personal ratings and lecturer ratings.
• 😀 Import the ratings data into the system and join it with the anime dataset using 'Join' operators in RapidMiner.
• 😀 Filter ratings to focus on anime with ratings above a certain threshold (e.g., above 8.0).
• 😀 Merge the processed dataset (anime data and ratings) to generate a final list of recommended anime based on both popularity and ratings.
• 😀 End the process by summarizing the system's functionality and recommending anime using both collaborative filtering and popularity metrics.

Q & A

What is the main focus of the video tutorial?

The main focus of the video tutorial is to demonstrate how to build an anime recommendation system using RapidMiner, specifically using two methods: popularity-based recommendation and collaborative filtering.

Which dataset is used in the tutorial?

The dataset used in the tutorial is the 'Anime Recommendation Database 2020' from Kaggle.

What are the key preprocessing steps for the dataset in RapidMiner?

The key preprocessing steps include replacing 'mall ID' with 'anime ID', ensuring the anime names are in English, and converting data types like rank and members to integers.

What does the 'Select Attribute' operation do in RapidMiner?

The 'Select Attribute' operation allows the user to choose specific columns from the dataset, such as anime ID, name, genre, and members, for further analysis, ensuring only relevant data is processed.

How does the tutorial filter the data based on popularity?

The data is filtered by selecting only animes with more than 10,000 members, and then the dataset is sorted by the number of members in descending order to identify the most popular anime.

What technique is used to extract the top 10 most popular animes?

The tutorial uses a filter operation to select only the top 10 animes with the highest number of members after sorting the dataset by the number of members.

What is collaborative filtering in the context of this tutorial?

Collaborative filtering in this tutorial refers to the method of recommending animes based on user ratings, where users (Herdin Abdillah and a lecturer) rate various animes, and these ratings are used to recommend anime to other users.

How are the ratings data integrated with the anime dataset?

The ratings data, which includes user ID, anime ID, and rating values, is joined with the main anime dataset based on the anime ID using a 'Join Attribute' operation in RapidMiner.

How does the tutorial filter out low-rated animes?

The tutorial filters out animes with ratings below 8.0, keeping only those with a rating of 8.0 or higher using a filter operation.

What is the final output of the tutorial's recommendation system?

The final output of the recommendation system consists of two parts: the top 10 most popular animes based on the number of members, and the highest-rated animes based on collaborative filtering and user ratings.