SISTEM REKOMENDASI PRODUCT EMINA COSMETICS DENGAN MENGGUNAKAN METODE CONTENT - BASED FILTERING

Content Based Filtering

Emina cosmetic merupakan produk kosmetik dari PT Paragon Technology and Innovation dengan mengusung konsep kosmetik untuk remaja dan dewasa muda. Seiring berjalannya waktu, produk emina tentunya akan bertambah varian. Dengan semakin bertambahnya jumlah produk, para customer terkadang akan merasa kesulitan dalam menentukan produk yang tepat untuk digunakan, maka dari itu dibutuhkan sistem yang dapat merekomendasikan produk sesuai dengan ketertarikan dan kebutuhan customer. Penelitian ini bertujuan untuk merancang sebuah aplikasi yang dapat memberikan rekomendasi kosmetik emina kepada customer berdasarkan produk yang dicari sebelumnya. Proses rekomedasi dilakukan dengan membandingkan kesamaan antara produk yang dicari customer dengan deskripsi produk yang ada. Penelitian ini menggunakan metode content-based filtering dimana metode tersebut dapat digunakan untuk merekomendasikan produk berdasarkan ketersediaan konten/ deskripsi produk. Untuk menghitung kesamaan antar kalimat menggunakan algoritma cosinesimilarity. Mulanya deskripsi produk akan dilakukan pembobotan dengan tfidf, lalu akan dihitung nilai similaritasnya dengan algoritma cosinesimilarity. Produk yang mengandung kata kunci akan dihitung nilai kemiripannya dan dilakukan perangkingan berdasarkan nilai similaritas tertinggi hingga terendah. Dalam penelitian ini, produk dengan similaritas tertinggi didapat dengan nilai sebesar 0,7195.

Style, Technology and Innovation in British Television Drama

Journal of British Cinema and Television ◽

10.3366/jbctv.2005.2.1.82 ◽

2005 ◽

Vol 2 (1) ◽

pp. 82-99 ◽

Cited By ~ 3

Author(s):

Lez Cooke

Keyword(s):

Television Drama ◽

Technology And Innovation

Protocol of the Working Group Meeting on the Interaction with the Interstate Council on Cooperation in Science, Technology and Innovation

Nauka ta innovacii ◽

10.15407/scin8.06.096 ◽

2012 ◽

Vol 8 (6) ◽

pp. 96-97

Keyword(s):

Working Group ◽

Group Meeting ◽

Science Technology ◽

Working Group Meeting ◽

Ukraine Science, Technology, and Innovation Public Expenditure Analysis

10.1596/28829 ◽

2017 ◽

Author(s):

Keyword(s):

Public Expenditure ◽

Science Technology ◽

Science, Technology and Innovation in Uganda

10.1596/978-0-8213-8672-9 ◽

2010 ◽

Author(s):

Sukhdeep Brar ◽

Sara E. Farley ◽

Robert Hawkins ◽

Caroline S. Wagner

Keyword(s):

Science Technology ◽

Hybrid Music Recommendation System Using Content-based Filtering and K-Mean Clustering Algorithm

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i7.14981501 ◽

2018 ◽

Vol 6 (7) ◽

pp. 1498-1501

Author(s):

Karishma Mandloi ◽

Amit Mittal

Keyword(s):

Clustering Algorithm ◽

Recommendation System ◽

Music Recommendation ◽

Content Based Filtering ◽

Music Recommendation System

Science, Technology and Innovation in Russia: 2019

10.37437/9785912941368-19-sb2 ◽

2019 ◽

Author(s):

V. Zavarukhin ◽

◽

I. Zinovyeva ◽

O. Solomentseva ◽

◽

...

Keyword(s):

Science Technology ◽

Externalizaciin de la Gestiin del Conocimiento: CCmo las Pollticas PPblicas en Ciencia, Tecnologga e Innovaciin pueden favorecen la Competitividad del Sector biotecnollgico en Colombia (Knowledge Process Outsourcing: How Public Policies on Science, Technology and Innovation May Enhance Competitiveness of Biotechnological Sector in Colombia)

SSRN Electronic Journal ◽

10.2139/ssrn.2361799 ◽

2012 ◽

Author(s):

Guillermo Rodrigo Corredor

Keyword(s):

Public Policies ◽

Knowledge Process ◽

Science Technology ◽

Regional Foresight for Bridging National Science, Technology and Innovation with Company Innovation: Experiences from Russia

SSRN Electronic Journal ◽

10.2139/ssrn.2513805 ◽

2014 ◽

Cited By ~ 4

Author(s):

Alexey Kindras ◽

Dirk Meissner ◽

Konstantin Vishnevskiy ◽

Mario Cervantes

Keyword(s):

Science Technology ◽

National Science ◽

Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network

Simulation Modelling Practice and Theory ◽

10.1016/j.simpat.2021.102375 ◽

2021 ◽

pp. 102375

Author(s):

Yassine Afoudi ◽

Mohamed Lazaar ◽

Mohammed Al Achhab

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Recommendation System ◽

Artificial Neural ◽

Collaborative Prediction ◽

Hybrid Recommendation ◽

Content Based Filtering

Improving Learner-Computer Interaction through Intelligent Learning Material Delivery Using Instructional Design Modeling

Entropy ◽

10.3390/e23060668 ◽

2021 ◽

Vol 23 (6) ◽

pp. 668

Author(s):

Christos Troussas ◽

Akrivi Krouska ◽

Cleo Sgouropoulou

Keyword(s):

Instructional Design ◽

Adaptive Learning ◽

Educational Software ◽

Learning Experience ◽

Learning Material ◽

Java Programming ◽

Undergraduate University Students ◽

Intelligent Tools ◽

Content Based Filtering ◽

Computer Interaction

This paper describes an innovative and sophisticated approach for improving learner-computer interaction in the tutoring of Java programming through the delivery of adequate learning material to learners. To achieve this, an instructional theory and intelligent techniques are combined, namely the Component Display Theory along with content-based filtering and multiple-criteria decision analysis, with the intention of providing personalized learning material and thus, improving student interaction. Until now, the majority of the research efforts mainly focus on adapting the presentation of learning material based on students’ characteristics. As such, there is free space for researching issues like delivering the appropriate type of learning material, in order to maintain the pedagogical affordance of the educational software. The blending of instructional design theories and sophisticated techniques can offer a more personalized and adaptive learning experience to learners of computer programming. The paper presents a fully operating intelligent educational software. It merges pedagogical and technological approaches for sophisticated learning material delivery to students. Moreover, it was used by undergraduate university students to learn Java programming for a semester during the COVID-19 lockdown. The findings of the evaluation showed that the presented way for delivering the Java learning material surpassed other approaches incorporating merely instructional models or intelligent tools, in terms of satisfaction and knowledge acquisition.