Article Per Year (5 Year)
p-Index From 2021 - 2026
2.177
P-Index
This Author published in this journals
All Journal Indonesian Journal on Computing (Indo-JC) Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Building of Informatics, Technology and Science Jurnal Pengabdian kepada Masyarakat Nusantara Charity : Jurnal Pengabdian Masyarakat Indonesian Community Journal Jurnal Pengabdian Masyarakat Akademisi eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT) Jurnal Abdimas UM Jambi
Selly Meliana
Telkom University
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Indonesian Journal on Computing (Indo-JC)

 1 
Non-Negative Matrix Factorization Based Recommender System using Female Daily Implicit Feedback Hani Nurrahmi; Agung Toto Wibowo; Selly Meliana
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 1 (2022): April, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.1.599

Abstract

Recommender Systems is widely used by e-commerce to provide recommendations of products that are probably to be the interest to users. One of the recommender system algorithms that can be implemented is Non-negative Matrix Factorization (NMF) which receives explicit feedback in the form of user ratings. Although this method is effective, there are problems faced by explicit feedback as input, e.g. there are users who act as grey-sheep or black-sheep by providing dishonest ratings as explicit feedback. On the opposite, dishonest feedback least frequently occurs in implicit feedback. Therefore, in this study, we used implicit feedback to recommend products by taking the implicit feedback obtained from Female Daily’s mobile application as a case study. There are three types of implicit feedback: View Product Detail, View Review Detail, and Add to Wishlist. We experimented with the NMF algorithm provided by Surprise library using two implicit ratings weighting scenarios: accumulative weighting and maximum weighting. We combined several NMF parameters and run our experiment in 5-fold cross-validation. The best performance result in accumulative weighting is MSE = 1,2969, RMSE = 1,1388, MAE = 0,7909. Meanwhile, the best performance result in maximum weighting is MSE = 0,6742, RMSE = 0,8211, MAE = 0,5924.
Co-Authors Ade Romadhony Afianti , Farah Agung Toto Wibowo Ardiyanto, Fajar Widhi Aryo Handono Bambang Pudjoatmodjo Bambang Pudjotatmodjo Bedy Purnama Bunyamin Bunyamin Dade Nurjanah Dodi Wisaksono Sudiharto Ema Rachmawati Ema Rachmawati Fat'hah Noor Prawira Fat’hah Noor Prawira Fat’hah Noor Prawira Fazmah Arif Yulianto Ferdian, Edward Fhira Nhita Hani Nurrahmi Hanifan, Abdullah Izzatul Ummah Muhammad Arzaki Prawita, Fat’hah Noor Putu Harry Gunawan Rimba Whidiana Ciptasari Satria Akbar Mugitama Yunanda, Gisela