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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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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.
Pengukuran Kepuasan Pengguna E-Learning Menggunakan Metode Evaluasi Heuristik dan System Usability Scale Iryanti, Emi; Zulfiqar, La Ode Mohamad; Kusumawardani, Sri Suning; Hidayah, Indriana
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 3: Juni 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022924631

Abstract

Pandemik COVID-19 yang terjadi saat ini mempengaruhi banyak aspek kehidupan termasuk pendidikan, dimana pembelajaran dilakukan dari rumah untuk mengurangi resiko penularan virus corona dengan menerapkan e-learning. Hal ini yang membuat implementasi e-learning harus baik, oleh karenanya harus dilakukan evaluasi agar e-learning mudah digunakan. Salah satu aspek penting yang harus dievaluasi yakni dari sisi usability-nya dimana dapat diketahui kepuasan pengguna dari sisi “kebergunaan”nya. Penelitian ini menggunakan dua metode evaluasi usability yaitu System Usability Scale (SUS) dan evaluasi heuristik (HE) digunakan untuk hal ini. Penggunaan kedua metode ini dilakukan untuk mendapatkan hasil evaluasi yang lebih mendalam agar dapat dilakukan perbaikan oleh pihak terkait. Dalam evaluasi usability menggunakan HE, evaluator yang dipilih adalah lima user expert yang ahli dalam bidang usability (tiga orang) ahli dalam bidang IT dan pengembangan pembelajaran (dua orang), sedangkan lingkup evaluasi pada penelitian ini yaitu proses login, edit profile, organisasi perkuliahan, dan aktivitas perkuliahan. Sedangkan pada evaluasi menggunakan kuesioner SUS diperoleh skor 63,3 (grade C-) dengan 162 responden, dengan hasil uji realibitas sebesar 0,818 dan uji validitas semua item pertanyaan di atas 0,129 yang berarti bersifat realible dan valid. Hasil evaluasi usability menggunakan HE, didapatkan bahwa terdapat satu prinsip yang dianggap sebagai permasalahan mayor oleh user expert yaitu prinsip user control and freedom, dimana sistem (e-learning) tidak memfasilitasi fungsi undo dan redo yang menyebabkan pengguna kebingungan apabila dengan sengaja/tidak memilih menu yang tidak dikehendaki. AbstractThe COVID-19 pandemic affects many aspects including education, where learning is carried out from home to reduce the risk of coronavirus transmission by implementing e-learning. One important aspect that must be evaluated is from the usability side, where we can find out the user satisfaction from the "usability" side. This study uses two usability evaluation methods, namely the System Usability Scale (SUS) and the heuristic evaluation (HE). The use of these two methods is carried out to obtain more in-depth evaluation results so that related parties can improve them. In the usability evaluation using HE, the selected evaluators are five user experts who are experts in the field of usability (three people) who are experts in the field of IT and learning development (two people), while the scope of evaluation in this study is the login process, edit profile, lecture organization, and lecture activities. While the evaluation using the SUS questionnaire obtained a score of 63.3 (grade C-) with 162 respondents. The results of the usability evaluation using HE, it was found that there is one principle that is considered a major problem by user experts, namely the principle of user control and freedom, where the system (e-learning) does not facilitate the undo and redo functions which causes confusion if the user click unwanted menu.
Decision Support Systems with Profile Matching Method in Selection of Achievement Marketing Officer at BRI Katamso Yogyakarta Wibowo, Ripto Mukti; Permanasari, Adhistya Erna; Hidayah, Indriana
Proceeding ISETH (International Summit on Science, Technology, and Humanity) 2015: Proceeding ISETH (International Conference on Science, Technology, and Humanity)
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/iseth.2372

Abstract

Appreciating outstanding employee by providing reward and recognition is a method to enhance the turnover of the company as well as to motivate the employee’s performance of employees. BRI Katamso constantly encourages the Marketing Officer (MO) to maintain and extent the achievement of targets and performance in order to increase the company's turnover. Pursuing augmentation in turnover and deterioration in MO, BRI has led the ability of MO to be represented to the public. The assessment criteria are set as the MO achievement including: outstanding credit, non-performance loan, Britama achievement, Simpedes attainment, current accounts achievement, deposits achievement, the sum of all achievement, debtors, creditors, the establishment of a blacklist and inclusion on the blacklist. Marketing officers are, in fact, distributed in each BRI Branch Office and numbers of BRI Unit and BRITeras. With the Decision Support System (DSS), it is expected that the computerization system would objectively and precisely assess the MO’s performances. The purpose of this research was to develop SPK in BRI Katamso by applying Profile Matching method.
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 Lukito Edi Nugroho M. Arifin Hidayat M. Tsana'uddin Farid Muhammad Haris Diponegoro Nasrokhah Noviati Paulus Insap Santosa Rio Jumardi 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 Wibowo, Ripto Mukti Widiyanti, Tanty Yanuar Winarno Winarno Zulfiqar, La Ode Mohamad