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Risanuri Hidayat
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Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada Jl. Grafika No 2. Kampus UGM Yogyakarta 55281
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Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Published by Universitas Gadjah Mada
ISSN : 23014156     EISSN : 24605719     DOI : 10.22146/jnteti
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering
Topics cover the fields of (but not limited to): 1. Information Technology: Software Engineering, Knowledge and Data Mining, Multimedia Technologies, Mobile Computing, Parallel/Distributed Computing, Artificial Intelligence, Computer Graphics, Virtual Reality 2. Power Systems: Power Generation, Power Distribution, Power Conversion, Protection Systems, Electrical Material 3. Signals, Systems, and Electronics: Digital Signal Processing Algorithm, Robotic Systems and Image Processing, Biomedical Instrumentation, Microelectronics, Instrumentation and Control 4. Communication Systems: Management and Protocol Network, Telecommunication Systems, Wireless Communications, Optoelectronics, Fuzzy Sensor and Network
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 11 Documents
 1   2 
Search results for , issue "Vol 9 No 4: November 2020" : 11 Documents clear
Kajian Penggunaan Data Log Mahasiswa untuk Berbagai Permasalahan Analisis Pembelajaran Sri Suning Kusumawardani; Syukron Abu Ishaq Alfarozi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 4: November 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1134.372 KB) | DOI: 10.22146/jnteti.v9i4.779

Abstract

An online learning system is a very crucial thing nowadays to prevent the spread of COVID-19 virus. However, this system is very difficult to maintain student motivation and engagement because there is no direct interaction between teacher and student. This study reviewed the use of student log data for the needs of learning analytics to predict student performance or drop-out trends from a course by looking at the student interaction log data with the system and student demographic data using open data, namely the Open University Learning Analytics Dataset (OULAD). From reviews of several research articles that refer to these data, we can see: 1) the common problems, i.e., prediction of drop-out student, prediction of student performance and engagement; 2) the features used during modeling, i.e., demographics and interactions, either summarized daily or weekly with various feature representations; 3) learning analysis methods that use machine learning algorithm, i.e., Decision Tree (DT), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM). This paper also discusses the risk mitigation process of students, planning and designing data systems that support learning analytics, and problems that are often encountered during the modeling process.

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