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All Journal Majalah Forum Teknik UGM Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Prosiding Semnastek Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) Edu Komputika Journal Journal of Information Technology and Computer Science Indonesian Journal of Information System Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) IJID (International Journal on Informatics for Development) METIK JURNAL JISKa (Jurnal Informatika Sunan Kalijaga) IJITEE (International Journal of Information Technology and Electrical Engineering) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Gadjah Mada Journal of Psychology (GamaJoP) Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Jurnal INFOTEL
Sri Suning Kusumawardani
Universitas Gadjah Mada
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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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.
Accessibility Analysis of Learning Management System Websites Dwi Fithriyaningrum; Kusumawardani, Sri Suning; Wibirama, Sunu
IJID (International Journal on Informatics for Development) Vol. 11 No. 1 (2022): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2022.3485

Abstract

In the digital era, Learning Management System is widely used to spread information in higher education, particularly at the university level. There are, however, issues with the Learning Management System's accessibility for users with disabilities. This research aims to investigate the accessibility issues of learning management websites of 30 universities in Indonesia. The top 30 universities in Indonesia according to Webometrics 2022 are the basis for this study. Accessibility issues will be identified and examined using the Wave evaluation tool which is in accordance with the Web Content Accessibility Guidelines 2.1 used by ISO 40500. Web Content Accessibility Guideline 2.1 has principles that any web should follow: Perceivable, Operable, Understandable, and Robust. Based on the research findings, The low contrast ratio between text and background, the absence of text explanations in the images, the lack of descriptive text on the links, the absence of text labels on the form, and the absence of text description on the button were the most frequently encountered accessibility issues.
KUBERNETES CLUSTER MANAGEMENT FOR CLOUD COMPUTING PLATFORM: A SYSTEMATIC LITERATURE REVIEW Huda, Aris Nurul; Kusumawardani, Sri Suning
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 20, No. 2, July 2022
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v20i2.a1103

Abstract

Kubernetes is designed to automate the deployment, scaling, and operation of containerized applications. With the scalability feature of Kubernetes technology, container automation processes can be implemented according to the number of concurrent users accessing them. Therefore, this research focuses on how Kubernetes as cluster management is implemented on several cloud computing platforms. Standard literature review method employing a manual search for several journals and conference proceedings. From 15 relevant studies, 5 addressed Kubernetes performance and scalability. Seven literature review addressed Kubernetes deployments. Two articles addressed Kubernetes comparison and the rest is addressed Kubernetes in IoT. Regarding the cloud computing cluster management challenges that must be overcome using Kubernetes: it is necessary to ensure that all configuration and management required for Docker containers are successfully set up on on-premises systems before deploying to the cloud or on-premises. Data from Kubernetes deployments can be leveraged to support capacity planning and design Kubernetes-based elastic applications.
Co-Authors Adhistya Erna Permanasari Amin Siddiq Sumi Andrew B. Osmond Anggi Destiyarto Ari Primaned Arti, Shindy B. Osmond, Andrew Budho Setyonugroho Budi Setiyanto Budi Setiyanto Cahya Damarjati Chairani Chairani Chairani Chairani Chandra, Daryus Daryus Chandra Desi Windi Sari Destiyarto, Anggi Dicky Rutanaji Dirko G. S. Ruindungan Dwi Fithriyaningrum Dwi Fithriyaningrum Ebedia Hilda Am, Ebedia Hilda Eka Ismantohadi Emi Iryanti Faradina Harumi Feddy Setio Pribadi Fitrah Sugita, Muhammad Fransisca Julia Kusuma Deviyanti Haryanti Mustika Huda, Aris Nurul Ida Bagus Verry Hendrawan Manuaba Indriana Hidayah Irwin Wijaya Lahusen Ismantohadi, Eka Jaeni Jaeni Kadek Dwi Pradnyani Novianti Kadek Dwi Pradnyani Novianti, Kadek Dwi Pradnyani La Ode Mohamad Zulfiqar Lukito Edi Nugroho Maksum Ro’is Adin Saf Marsetyo Wisaksono Marvianto, Ramadhan Dwi Muhammad Fitrah Sugita Muhammad Haris Diponegoro Noor Akhmad Setiawan Olivia Kembuan Paulus Insap Santosa Prastyo, Pulung Hendro Primaned, Ari Pulung Hendro Prastyo Ridi Ferdiana Risanuri Hidayat Saf, Maksum Ro’is Adin Selo Sulistyo, Selo Stefani, Brillian Stefani, Brillian Sulistyohati, Aprilia Sumi, Amin Siddiq Sunu Wibirama Syukron Abu Ishaq Alfarozi Teguh Bharata Adji Uswatun Hasanah Verry Hendrawan Manuaba, Ida Bagus Widyawan