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All Journal Jurnal Informatika Jurnal Masyarakat Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Simantec Scan : Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknologi Informasi dan Ilmu Komputer REKAYASA Seminar Nasional Informatika (SEMNASIF) Jurnal Ilmiah Pangabdhi Panrita Abdi - Jurnal Pengabdian pada Masyarakat Sistemasi: Jurnal Sistem Informasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Jurnal Layanan Masyarakat (Journal of Public Service) International Journal of Science, Engineering and Information Technology Asia Pacific Journal of Management and Education Journal of International Conference Proceedings Jurnal Ilmiah Edutic : Pendidikan dan Informatika Innovative: Journal Of Social Science Research Bulletin of Social Informatics Theory and Application
Mochammad Kautsar Sophan
Universitas Trunojoyo
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Physics Public Health Social Sciences Other

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Journal : REKAYASA

 1 
Multi-criteria based Item Recommendation Methods Noor Ifada; Syafrurrizal Naridho; Mochammad Kautsar Sophan
Rekayasa Vol 12, No 2: Oktober 2019
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (420.836 KB) | DOI: 10.21107/rekayasa.v12i2.5913

Abstract

This paper comprehensively investigates and compares the performance of various multi-criteria based item recommendation methods. The development of the methods consists of three main phases: predicting rating per criterion; aggregating rating prediction of all criteria; and generating the top-  item recommendations. The multi-criteria based item recommendation methods are varied and labelled based on what approach is implemented to predict the rating per criterion, i.e., Collaborative Filtering (CF), Content-based (CB), and Hybrid. For the experiments, we generate two variations of datasets to represent the normal and cold-start conditions on the multi-criteria item recommendation system. The empirical analysis suggests that Hybrid and CF are best implemented on the normal and cold-start item conditions, respectively. On the other hand, CB should never be (solely) implemented in a multi-criteria based item recommendation system on any conditions.
Normalization based Multi-Criteria Collaborative Filtering Approach for Recommendation System Noor Ifada; Nur Fitriani Dwi Putri; Mochammad Kautsar Sophan
Rekayasa Vol 13, No 3: December 2020
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v13i3.8545

Abstract

A multi-criteria collaborative filtering recommendation system allows its users to rate items based on several criteria. Users instinctively have different tendencies in rating items that some of them are quite generous while others tend to be pretty stingy.  Given the diverse rating patterns, implementing a normalization technique in the system is beneficial to reveal the latent relationship within the multi-criteria rating data. This paper analyses and compares the performances of two methods that implement the normalization based multi-criteria collaborative filtering approach. The framework of the method development consists of three main processes, i.e.: multi-criteria rating representation, multi-criteria rating normalization, and rating prediction using a multi-criteria collaborative filtering approach. The developed methods are labelled based on the implemented normalization technique and multi-criteria collaborative filtering approaches, i.e., Decoupling normalization and Multi-Criteria User-based approach (DMCUser) and Decoupling normalization and Multi-Criteria User-based approach (DMCItem). Experiment results using the real-world Yelp Dataset show that DMCItem outperforms DMCUser at most  in terms of Precision and Normalized Discounted Cumulative Gain (NDCG). Though DMCUser can perform better than DMCItem at large , it is still more practical to implement DMCItem rather than DMCUser in a multi-criteria recommendation system since users tend to show more interest to items at the top list.
Co-Authors A. K., Siti Isnaini Achmad Jauhari Alamsyah, Nurwahyu Andharini Dwi Cahyani Ariesta Kartika Sari Ariesta Kartika Sari Arif Muntasa Arik Kurniawati Firdaus, Hariri Hendrawan, Yonathan Ferry Idealetika Yunia Fitri Ikhsan, Achmad Faris Indah Agustien Sirajudin Indah Rodiatun Mardiyah Indah Rodiatun Mardiyah Kurniawa Eka Permana Kurniawan Eka Permana Mardiyah, Indah Rodiatun Merlien N. Febriyati moch nasrul ulum Muchamad Arif Muchamad Arif Muchamad Arif, Muchamad Muhammad Amfahtori Wijarnoko Muhammad Yusuf Nakkas, Haythem Noor Ifada Novia Sulviatin, Novia Nur Fitriani Dwi Putri Permana, Kuniawan Eka Puji Rahayu Ningsih Puji Rahayu Ningsih Qutsiah, Siti Alifatul Rahmat, Abdullah Basuki Rika Yunitarini Rima Tri Wahyuningrum Santhi Yulia Retnani Sari, Putri Pradnyawidya Sariyanti Astutik selia resita Siradjuddin, Indah Agustien Siswanto A, Zaenal Syafrurrizal Naridho Taufiqur Rohman Vivi Tri Widyaningrum Wanda Ramansyah Yeni Kustiyahningsih Yonathan Ferry Hendrawan Yonathan Ferry Hendrawan