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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Teknik Elektro Jurnal Pekommas Jurnal Teknologi Informasi dan Ilmu Komputer Journal of ICT Research and Applications Seminar Nasional Informatika (SEMNASIF) Edu Komputika Journal Journal of Information Technology and Computer Science IJID (International Journal on Informatics for Development) Jurnal Akuntansi dan Bisnis IJITEE (International Journal of Information Technology and Electrical Engineering) Journal of Computer System and Informatics (JoSYC) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Proceeding ISETH (International Summit on Science, Technology, and Humanity) Electrician : Jurnal Rekayasa dan Teknologi Elektro
Indriana Hidayah
Department Of Electrical Engineering And Information Technology, Faculty Of Engineering, Universitas Gadjah Mada, Bulaksumur, Yogyakarta 55281,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Medicine & Pharmacology Social Sciences Other

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Kesesuaian Minat Mahasiswa dengan Judul Tesis Mahasiswa Menggunakan Metode Fuzzy Mamdani Astrie Kusuma Dewi; Adhistya Erna Permanasari; Indriana Hidayah
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 10 No. 1 (2016)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v10n1.188

Abstract

Intisari — Pemilihan minat tesis yang sesuai dengan minat mahasiswa dapat membantu mahasiswa dalam proses pengerjaan tesis. Selain minat, dibutuhkan juga motivasi sebagai dorongan dari dalam diri mahasiswa. Data dalam penelitian ini menggunakan kuesioner minat dan kuesioner motivasi. Data dari kuesioner tersebut diolah menggunakan fuzzy Mamdani. Dalam penelitian ini fuzzy mamdani digunakan untuk mengetahui kesesuaian minat tesis mahasiswa, dari 80 mahasiswa sebagai responden diketahui bahwa sebanyak  51,06 % mahasiswa memiliki minat yang sesuai dengan proposal tesis dan sekitar 48,94 % mahasiswa memiliki minat yang tidak sesuai dengan proposal tesis mahasiswa. Kata kunci — Minat dan motivasi, Logika Fuzzy, Metode Fuzzy Mamdani Abstract — Selection of interest in accordance with the thesis that the interest of students to help students in the process of thesis. In addition to interest, it needed a boost of motivation as the students themselves. The data in this study using questionnaires interest and motivation questionnaire. Data from the questionnaires were processed using fuzzy Mamdani. In this study, fuzzy mamdani used to determine the suitability of interest thesis students, 80 students as respondents note that as many as 51.06% of the students have an interest in accordance with the thesis proposal and approximately 48.94% of the students have an interest that is not in accordance with the student's thesis proposal. Keywords— Interest and motivation, Fuzzy Logic,  Fuzzy mamdani method.
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.
Research Trend of Causal Machine Learning Method: A Literature Review Arti, Shindy; Hidayah, Indriana; Kusumawardani, Sri Suning
IJID (International Journal on Informatics for Development) Vol. 9 No. 2 (2020): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09208

Abstract

Machine learning is commonly used to predict and implement  pattern recognition and the relationship between variables. Causal machine learning combines approaches for analyzing the causal impact of intervention on the result, asumming a considerably ambigous variables. The combination technique of causality and machine learning is adequate for predicting and understanding the cause and effect of the results. The aim of this study is a systematic review to identify which causal machine learning approaches are generally used. This paper focuses on what data characteristics are applied to causal machine learning research and how to assess the output of algorithms used in the context of causal machine learning research. The review paper analyzes 20 papers with various approaches. This study categorizes data characteristics based on the type of data, attribute value, and the data dimension. The Bayesian Network (BN) commonly used in the context of causality. Meanwhile, the propensity score is the most extensively used in causality research. The variable value will affect algorithm performance. This review can be as a guide in the selection of a causal machine learning system.
Co-Authors Adhistya Erna Permanasari Arti, Shindy Astrie Kusuma Dewi Bobby Rian Dewangga Cahyani Windarto Diah Priyawati Ebedia Hilda Am, Ebedia Hilda Eko Nugroho Eko Wahyu Nugroho Emi Iryanti Fachrul Budi Prayoga Fahmi Dhimas Irnawan Faizal M. Priyowibowo Hanung Adi Nugroho Indah Soesanti Intan Sulistyaningrum Sakkinah La Ode Mohamad Zulfiqar Lukito Edi Nugroho M. Arifin Hidayat M. Tsana'uddin Farid Muhammad Haris Diponegoro Nasrokhah Noviati Paulus Insap Santosa Rio Jumardi Ripto Mukti Wibowo Sanny Hikmawati Saucha Diwandari Septian Rico Hernawan Silmi Fauziati Siti Helmyati Sri Kustanti Sri Suning Kusumawardani Stefani, Brillian Stefani, Brillian Tari Mardiana Teguh Bharata Adji Widiyanti, Tanty Yanuar Winarno Winarno