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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmiah Kursor REKAYASA Jurnal Ilmiah Pangabdhi Jurnal Tekno Insentif Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT
Noor Ifada
University Of Trunojoyo Madura
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science Physics Social Sciences Other

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

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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.
Dampak pembobotan pada metode hybrid user-based dan item-based untuk sistem rekomendasi film Fitriyeh, Fadetul; Haqqi, Nuskhatul; Choiriyah, Layla Mufah; Ifada, Noor
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The rapid development of technology means that information is becoming more abundant and diverse, including in the movie industry. The number of movies each year can reach tens of thousands with various genres, making it difficult for potential viewers to choose a movie that suits their interests. One solution to the problem is the existence of a recommendation system that can provide movie recommendations based on information on movies that have been watched previously. Collaborative Filtering is a widely used approach in recommendation systems. Collaborative Filtering offers recommendations based on the similarity between users for User-based methods and the similarity between items for Item-based methods. However, the similarity value can be high for data with high dispersion even though only one item is in common. The Hybrid method can be a solution to overcome this by combining User-based and Item-based methods and adding genre information from the items. The final prediction result is obtained from the prediction results of all methods combined using linear combination. The combination is done by giving weight to each method and then summarising it. This study aims to determine the impact of weighting variations on Hybrid User-based and Item-based Collaborative Filtering methods. The results obtained from this study show that More Dominant User-based and Very Dominant User-based weightings are superior to other weightings because they show good performance for a smaller list of recommendations.
Co-Authors Choiriyah, Layla Mufah Faqih, Fauziah Nur Fitriyeh, Fadetul Haqqi, Nuskhatul Husni Husni Mochammad Kautsar Sophan Mulaab Mulaab Nugraha, Maulidhan Ady Nur Fitriani Dwi Putri Pratama, Rizki Ashuri Rahmady Liyantanto Rahmatullah, Asfani Rokhim, Imam Fadhkur Safi'i, Yunus Sobihah, Anis Satus Sri Wahyuni Susi Susanti Syafrurrizal Naridho