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All Journal Jurnal Teknik Elektro Jurnal Script Journal of Information Technology and Computer Science
Ebedia Hilda Am, Ebedia Hilda
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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PENERAPAN METODE ANALYTICAL HIERARCHY PROCESS ( AHP ) PADA SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN MAHASISWA BERPRESTASI MENGGUNAKAN FRAMEWORK LARAVEL (STUDI KASUS : INSTITUT SAINS & TEKNOLOGI AKPRIND YOGYAKARTA) Am, Ebedia Hilda; Kumalasari, Erna; Rachmawati, Rr. Yuliana
Jurnal Script Vol 3, No 1 (2015): EDISI DESEMBER 2015
Publisher : Jurnal Script

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Abstract

ABSTRACTOutstanding Student Selection is one of the activities which is held once every year in AKPRIND Institute of Science & Technology Yogyakarta. The first winner will be sent to the Kopertis level. Outstanding Student Selection process that has lasted still not use computerized system. One of the systems that can be applied is the decision support system with a method and a tool. The method that implemented in this system is Analytical Hierarchy Process (AHP) method. This method chosen because it can break a complex problem into sub-sub problems and arrange it to an hierarchy. The tool that used in this system is Laravel framework.  This Decision Support System in Outstanding Student Selection can help Administration of Student and Alumnus Bureau with the result of the calculation that applying AHP method and the result of the calculation that applying Mawapres Sarjana 2015 guide. Decision Support System in Outstanding Student Selection is a system that could support the decision making process. This system is expected to make the decision making process more easy and faster. Keywords : Decision Support System, Analytical Hierarchy Process (AHP), Outstanding Student Selection, Laravel Framework.
Tracing Knowledge States through Student Assessment in a Blended Learning Environment Hidayah, Indriana; Am, Ebedia Hilda
Jurnal Teknik Elektro Vol 15, No 2 (2023): Jurnal Teknik Elektro
Publisher : Jurusan Teknik Elektro, Fakultas Teknik, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v15i2.47861

Abstract

Blended learning has recently acquired popularity in a variety of educational settings. This approach has the advantage of being able to autonomously monitor students' knowledge states using the collected learning data. Moodle is the most widely used learning management system in blended learning environments. Students can access Moodle to obtain supplementary materials, exercises, and assessments to complement their face-to-face meetings. However, its performance can be improved by more effectively tailoring students' skills and pace of learning. Several studies have been conducted on knowledge tracing; however, we have not discovered any studies that particularly investigate knowledge tracing in a blended learning setting with Moodle as a component. This study proposes a scheme for assessment using the features of the Moodle quiz platform. The assessment data is used to conduct knowledge tracing with the Bayesian Knowledge Tracing (BKT) model, which improves interpretability. The aforementioned data were collected from information engineering undergraduate students who completed 88 exercises that assessed 23 knowledge components within the course. We measure RMSE and MAE to evaluate the performance of the BKT model on our dataset. Furthermore, we compare the knowledge tracing performance to other well-known datasets. Our results show that the BKT model performed better with our dataset, with an RMSE of 0.314 and an MAE of 0.197. Moreover, the BKT model can be used to assess student performance and determine the level of mastery for each knowledge component. Thus, the outcomes can be applied to personalized learning in the future.
A Literature Review of Knowledge Tracing for Student Modeling : Research Trends, Models, Datasets, and Challenges Am, Ebedia Hilda; Hidayah, Indriana; Kusumawardani, Sri Suning
Journal of Information Technology and Computer Science Vol. 6 No. 2: August 2021
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (900.101 KB) | DOI: 10.25126/jitecs.202162344

Abstract

Modeling students' knowledge is a fundamental part of online learning platforms. Knowledge tracing is an application of student modeling which renowned for its ability to trace students' knowledge. Knowledge tracing ability can be used in online learning platforms for predicting learning performance and providing adaptive learning. Due to the wide uses of knowledge tracing in student modeling, this study aims to understand the state-of-the-art and future research of knowledge tracing. This study focused on reviewing 24 studies published between 2017 to the third quarter of 2021 in four digital databases. The selected studies have been filtered using inclusion and exclusion criteria. Several previous studies have shown that there are two approaches used in knowledge tracing, including probabilistic and deep learning. Bayesian Knowledge Tracing model is the most widely used in the probabilistic approach, while the Deep Knowledge Tracing model is the most popular model in the deep learning approach. Meanwhile, ASSISTments 2009–2010 is the most frequently tested dataset for probabilistic and deep learning approaches. In the future, additional studies are required to explore several models which have been developed previously. Therefore this study provides direction for future research of each existing approach.
Co-Authors Erna Kumalasari Indriana Hidayah Rr. Yuliana Rachmawati, Rr. Yuliana Sri Suning Kusumawardani