Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Timmie Siswandi
Laboratory for Enterprise Intelligent System, School of Industrial and System Engineering, Telkom University, Bandung, Jawa Barat 40257, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Pengembangan Web Cerdas untuk Social e-Learning di Pedesaan Seno Adi Putra; Timmie Siswandi; Dessy Yussela; Rinez Asprinola; Erin Karina; Mega Candra Dewi; Santi Al-arif
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 3: Agustus 2024
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v13i3.9872

Abstract

Social media technology affects the learning paradigm change towards social media-based learning, known as social e-learning. Social e-learning regards a person as a center of learning, dubbed people-centered learning. Here, people are encouraged to interact or communicate with others and produce their learning content. This work attempted to provide a solution model for rural e-learning social learning empowered with intelligent web technology. The proposed social e-learning includes several modules for development, such as personal space, collaboration space, and communication space modules. It also leverages intelligent web technologies currently implemented in today’s social media applications, such as article search, article recommendations, friendship recommendations, and document classification. In the searching module, the PageRank method was used to calculate the relevance score to determine the rating of the documents or articles. The similarity-based element calculation method was utilized to create articles’ suggestions and recommendations. The naïve Bayes algorithm, decision tree, and neural network were compared to find the best solution for article classification in agriculture, fisheries, animal husbandry, and plantations. When comparing these three algorithms, the result showed that the neural network was the most accurate classification, reaching 95.2% accuracy. A clustering algorithm, namely robust clustering using links (ROCK), was utilized for rural friendship recommendation. Thus, these algorithms (the PageRank, the similarity-based element, neural network, and the ROCK) were suitable and recommended for supporting intelligent web paradigms in social e-learning applications.
Co-Authors Dessy Yussela Erin Karina Mega Candra Dewi Rinez Asprinola Santi Al-arif Seno Adi Putra