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Clustering Classification K-Means SVM SIB6NF

Indonesia

Workflow ini dibuat dengan tujuan : melakukan segmentasi buku berdasarkan rating yang diberikan pengguna, maka dapat digunakan untuk mengidentifikasi tren populer dan preferensi pengguna berdasarkan rating dan jumlah ulasan. Sehingga bermanfaat untuk meningkatkan pengalaman pengguna.

Workflow ini sebagai pemenuhan tugas final project SIB-6 Academy Codeless Data Science NF Academy.

This workflow was created to segment books based on user ratings, in order to identify popular trends and user preferences based on ratings and number of reviews. This will be beneficial in enhancing user experience.

This workflow serves as the fulfillment of the final project assignment for SIB-6 Academy Codeless Data Science NF Academy.

Dataset : good_reads_top_1000_books.csv

Group-1 Kelompok-5
Ketua : Listyaningsih Duwi Cahyani, Elista Pakpahan, Nahari Ihsan, Zakiah Nabila

Classification Menggunakan Algoritma SVM (Lanjutan Clustering Sebelumnya) Clustering Menggunakan Algoritma K-Means Percobaan Classification Menggunakan Algoritma Naive Bayes Percobaan Clustering Menggunakan Algoritma K-Medoids Node 4Goodreads_top_1000_books.csvMin-Max NormalizationGroup into 4 ClustersNode 12Node 13Node 14Node 19Node 20Hasil_clustering_top_goodreads.csvNode 22Node 23Node 24Node 26Node 30Node 31Node 33Node 34Node 35Hasil_clustering_top_goodreads.csvNode 39Node 40Node 41Node 42Node 43Node 44Node 47Goodreads_top_1000_books.csvNode 49Node 51Node 53Node 54Node 56Node 57Node 59Node 60RecommendedRecommendedNicheNicheNode 75Node 76 Normalizer File Reader Normalizer k-Means Rule Engine Denormalizer Color Manager SilhouetteCoefficient CSV Writer CSV Reader Statistics View Rank Correlation Partitioning Normalizer Scorer (JavaScript) Naive Bayes Learner Naive BayesPredictor Column Filter Partitioning CSV Reader Rank Correlation Column Filter SVM Predictor Normalizer (Apply) Scorer (JavaScript) SVM Learner DashboardClustering Color Manager File Reader Statistics View Rule Engine DashboardClustering k-Medoids Numeric Distances Rank Correlation Statistics Rule Engine Rule Engine ROC Curve ROC Curve Statistik danDistribusi Data ROC Curve ROC Curve Rank Correlation SilhouetteCoefficient Classification Menggunakan Algoritma SVM (Lanjutan Clustering Sebelumnya) Clustering Menggunakan Algoritma K-Means Percobaan Classification Menggunakan Algoritma Naive Bayes Percobaan Clustering Menggunakan Algoritma K-Medoids Node 4Goodreads_top_1000_books.csvMin-Max NormalizationGroup into 4 ClustersNode 12Node 13Node 14Node 19Node 20Hasil_clustering_top_goodreads.csvNode 22Node 23Node 24Node 26Node 30Node 31Node 33Node 34Node 35Hasil_clustering_top_goodreads.csvNode 39Node 40Node 41Node 42Node 43Node 44Node 47Goodreads_top_1000_books.csvNode 49Node 51Node 53Node 54Node 56Node 57Node 59Node 60RecommendedRecommendedNicheNicheNode 75Node 76 Normalizer File Reader Normalizer k-Means Rule Engine Denormalizer Color Manager SilhouetteCoefficient CSV Writer CSV Reader Statistics View Rank Correlation Partitioning Normalizer Scorer (JavaScript) Naive Bayes Learner Naive BayesPredictor Column Filter Partitioning CSV Reader Rank Correlation Column Filter SVM Predictor Normalizer (Apply) Scorer (JavaScript) SVM Learner DashboardClustering Color Manager File Reader Statistics View Rule Engine DashboardClustering k-Medoids Numeric Distances Rank Correlation Statistics Rule Engine Rule Engine ROC Curve ROC Curve Statistik danDistribusi Data ROC Curve ROC Curve Rank Correlation SilhouetteCoefficient

Nodes

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: user
Created at: 2024-06-10
On NodePit since: 2024-06-28
Last update: 2024-11-21
Created with KNIME version: v5.2.1
Tags: classificationklasifikasiclusteringklasterisasisvmk-meansnaive bayescsv

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