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All Journal Jurnal Tekno Insentif Jurnal Computer Science and Information Technology (CoSciTech)
Pratama, Rizki Ashuri
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Computer Science & IT

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Studi Literatur Penerapan Clustering Data Numerik Untuk Sistem Rekomendasi Berbasis Collaborative Filtering Ifada, Noor; Pratama, Rizki Ashuri
Computer Science and Information Technology Vol 5 No 2 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i2.7087

Abstract

The recommendation system assists users in finding items that match their preferences from the large number of items that exist. Recommendation systems have two types of approaches: a content-based approach and a Collaborative Filtering (CF) approach. CF approaches can be categorized into model-based and memory-based CF. The problem faced in the CF method is the complexity or long computation time due to the large data dimensions, data sparsity, and accuracy. In overcoming the problems mentioned, several data mining and machine learning techniques are used in collaboration with traditional CF methods. Many studies are using numerical data clustering techniques on CF-based recommendation systems. However, to date, there is still no literature review regarding the implementation of clustering techniques to numerical data to develop recommendation system methods based on the CF approach. Therefore, a literature study was carried out regarding the implementation of clustering techniques to numerical data to develop recommendation system methods based on the CF approach using 20 related literature. As a result, the various clustering techniques used can be grouped into K-Means, Subspace Clustering, Bi-Clustering, Canopy Clustering, K-Medoids, Evolutionary Heterogeneous Clustering, Fuzzy, Self-Constructing Clustering (SCC), and Agglomerative Hierarchical Clustering (AHC). K-Means and Fuzzy clustering techniques are the most commonly found in the literature.
Perbandingan User-Based dan Item-Based pada Sistem Rekomendasi Film Kombinasi Teknik Reduksi Dimensi dan Clustering Pratama, Rizki Ashuri; Safi'i, Yunus; Nugraha, Maulidhan Ady; Sobihah, Anis Satus; Ifada, Noor
Jurnal Tekno Insentif Vol 19 No 1 (2025): Jurnal Tekno Insentif
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah IV

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36787/jti.v19i1.1662

Abstract

Sistem rekomendasi mampu menghasilkan daftar film hasil personalisasi yang mungkin menarik bagi user dengan mempelajari kegiatan user dalam memberikan rating. Sistem rekomendasi diklasifikasikan dalam tiga pendekatan: Content-Based Filtering, Collaborative Filtering (CF), dan Hybrid Filtering. Pendekatan CF lebih popular dibandingkan dua pendekatan lainnya. CF memiliki dua model, yakni CF user-based (UB) dan CF item-based (IB). Namun, pada CF terdapat permasalahan yaitu waktu komputasi yang lama karena dimensi data yang besar, kelangkaan data dan akurasinya. Untuk mengatasinya terdapat dua tahap yang dapat dikombinasikan pada CF, yaitu reduksi dimensi menggunakan algoritma Singular Value Decomposition (SVD) dan clustering menggunakan algoritma K-Means (KM). Tujuan dari penelitian ini adalah melakukan perbandingan hasil akurasi antara sistem rekomendasi film yang menggunakan metode SVD-KM-UB dan SVD-KM-IB pada dataset MovieLens. Hasil yang didapatkan pada dataset MovieLens, metode SVD-KM-UB lebih unggul daripada metode SVD-KM-IB. Metode SVD-KM-UB mengalami persentase kenaikan pada seluruh variasi dengan peningkatan terbesar pada , yaitu sebesar 5836,4%.
Co-Authors Noor Ifada Nugraha, Maulidhan Ady Safi'i, Yunus Sobihah, Anis Satus