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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Simetris Jurnal Pengembangan Kota Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Sisfokom (Sistem Informasi dan Komputer) JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Krea-TIF: Jurnal Teknik Informatika Jurnal Riset Informatika Progresif: Jurnal Ilmiah Komputer Jurnal Informatika: Jurnal Pengembangan IT
Arif Dwi Laksito
Universitas Amikom Yogyakata
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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The Hybrid Recommender System of the Indonesian Online Market Products using IMDb weight rating and TF-IDF Muhammad Johari; Arif Laksito
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 5 (2021): Oktober2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (451.522 KB) | DOI: 10.29207/resti.v5i5.3486

Abstract

Today, consumers are faced with an abundance of information on the internet; accordingly, it is hard for them to reach the vital information they need. One of the reasonable solutions in modern society is implementing information filtering. Some researchers implemented a recommender system as filtering to increase customers’ experience in social media and e-commerce. This research focuses on the combination of two methods in the recommender system, that is, demographic and content-based filtering, commonly it is called hybrid filtering. In this research, item products are collected using the data crawling method from the big three e-commerce in Indonesia (Shopee, Tokopedia, and Bukalapak). This experiment has been implemented in the web application using the Flask framework to generate products’ recommended items. This research employs the IMDb weight rating formula to get the best score lists and TF-IDF with Cosine similarity to create the similarity between products to produce related items.
Content Based VGG16 Image Extraction Recommendation Arif Laksito; Muhammad Royyan Saputra
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 3 (2022): Juni 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (439.557 KB) | DOI: 10.29207/resti.v6i3.3909

Abstract

Data transfer across numerous platforms has increased dramatically due to the enormous number of visitors or users of the present e-commerce platform. With the rise of increasingly massive data, consumers are finding it challenging to obtain the right data. The recommendation engine may be used to make it simpler to find information that is relevant to the user's needs. Clothing, gadgets, autos, furniture, and other e-commerce items rely on product visualization to entice shoppers. There are millions of images in these items. Displaying the information sought by clients based on visual data is a difficult challenge to address. One strategy that is simple to use in a recommendation system is content-based filtering. This approach will eventually make suggestions to consumers based on previously accessible goods or product descriptions. Content-based filtering works by searching for similarities based on the properties of a product item. User interactions with a product will be recorded and analyzed in order to recommend certain similarities to users. Text-based datasets are used in the majority of content-based filtering studies. In this study, however, we attempt to leverage a dataset received from Kaggle in the form of images of futsal shoes. Then, VGG16 architecture is used to extract the image dataset. The top 5 most relevant item rankings are generated by this recommendation method using cosine similarity. In addition, the NDCG (Normalized Discounted Cumulative Gain) approach is used to assess the results of the suggestions. The NDCG was evaluated in ten test scenarios, with an average NDCG value of 0.855, indicating that the system delivers a reasonable performance suggestion.
Co-Authors Acihmah Sidauruk Adninda, Gardyas Bidari Anas Fikri Hanif Burhanudin, Nazil Ilham Darmawan, Briga Eli Pujastuti, Eli Ervan Triyanto Hafizh Fauzi Fathurahman Hayaty, Mardhiya Heri Sismoro Humam, Choirul Irfan Rifani Kartikakirana, Renindya Azizza Mahanggara, Andika Mahanggara, Andika Muhammad Darwis Arifin Muhammad Resa Arif Yudianto Muhammad Royyan Saputra Nurcholis, Muhammad Tofa Rahmi, Alfie Nur Sasongko, Theopilus Bayu Sumarni Adi Syauqani Juliansyah