Articles
<![CDATA[Research Methodology for Analysis of E-Commerce User Activity Based on User Interest using Web Usage Mining ]]>
<![CDATA[Saucha Diwandari]]>;
<![CDATA[Adhistya Erna Permanasari]]>;
<![CDATA[Indriana Hidayah]]>
<![CDATA[ Journal of ICT Research and Applications Vol. 12 No. 1 (2018) ]]>
Publisher : <![CDATA[LPPM ITB ]]>
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DOI: 10.5614/itbj.ict.res.appl.2018.12.1.4
<![CDATA[Visitor interaction with e-commerce websites generates large amounts of clickstream data stored in web access logs. From a business standpoint, clickstream data can be used as a means of finding information on user interest. In this paper, the authors propose a method to find user interest in products offered on e-commerce websites based on web usage mining of clickstream data. In this study, user interest was investigated using the PIE approach coupled with clustering and classification techniques. The experimental results showed that the method is able to assist in analyzing visitor behavior and user interest in e-commerce products by identifying those products that prompt visitor interest.]]>
<![CDATA[Sistem Pakar Deteksi Minat Untuk Pemilihan Jenjang Karir Menggunakan Metode Certainty Factor ]]>
<![CDATA[M. Tsana'uddin Farid]]>;
<![CDATA[Hanung Adi Nugroho]]>;
<![CDATA[Indriana Hidayah]]>
<![CDATA[ Journal of Computer System and Informatics (JoSYC) Vol 2 No 3 (2021): Mei 2021 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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<![CDATA[Interests have an important role in the career path because interests will affect a person's performance, passion and abilities during their educational studies. The problem that occurs is that based on a survey conducted by the Indonesian Career Center Network, it was found that 87% of students in Indonesia felt that they were wrong in deciding which major they were taking. This problem is because the majors they take do not match their interests. This problem can be prevented by detecting interest early on so that career paths can be planned appropriately. This study aims to provide a solution to this problem by building an interest detection expert system to support career path selection. Certainty Factor methods are used in determining the output of the expert system to be built with the adaptation of the interest test tool, called RIASEC. Testing is done by comparing the system output results with expert consultation. The test results showed that the expert detection system was able to answer the interest with an accuracy of 93%. Based on the test results, the solution in interest detection expert system could be used as a reference assessment in planning career path choices]]>
<![CDATA[Tinjauan Pustaka Sistematis: Implementasi Metode Deep Learning pada Prediksi Kinerja Murid ]]>
<![CDATA[Muhammad Haris Diponegoro]]>;
<![CDATA[Sri Suning Kusumawardani]]>;
<![CDATA[Indriana Hidayah]]>
<![CDATA[ Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 10 No 2: Mei 2021 ]]>
Publisher : <![CDATA[Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada ]]>
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DOI: 10.22146/jnteti.v10i2.1417
<![CDATA[The use of machine learning, which is one of the implementations in the field of artificial intelligence, has penetrated into various fields, including education. By using a combination of machine learning techniques, statistics, and databases, educational data mining can be carried out to find out the patterns that exist in a particular dataset. One use of educational data mining is to predict student performance. The results of student performance predictions can be used as an instrument for monitoring and evaluating the learning process so that it can help determine further steps in order to improve the learning process. This study aims to determine the state of the art implementation of deep learning which is part of machine learning in the context of educational data mining, especially regarding student performance predictions. In this study, a systematic literature review is presented to determine the variation of deep learning techniques or algorithms used and their performance. Twenty scientific publications were found and the average performance achieved in making predictions was 89.85%. The majority of the techniques used are Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) with demographic, behavioral, and academic data features.]]>
<![CDATA[Regresi Linear untuk Mengurangi Bias Sistem Penilaian Uraian Singkat ]]>
<![CDATA[Silmi Fauziati]]>;
<![CDATA[Adhistya Erna Permanasari]]>;
<![CDATA[Indriana Hidayah]]>;
<![CDATA[Eko Wahyu Nugroho]]>;
<![CDATA[Bobby Rian Dewangga]]>
<![CDATA[ Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 10 No 3: Agustus 2021 ]]>
Publisher : <![CDATA[Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada ]]>
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DOI: 10.22146/jnteti.v10i3.1983
<![CDATA[This study is aimed to improve the performance of a short essay scoring system. The improvement is executed by integrating a simple linear regression to the output of a combined cosine similarity method (with weighted term frequency using Term Frequency –Inverse Document Frequency (TF-IDF) method) and term-matching mechanism.The linear regression is conducted by taking the short essay score (resulting from the combined cosine similarity and termmatching) as a regressor variable. In order to demonstrate the effectivenessof the proposedscoring system, the performance of the scoring system is measured relative to manual scoring by a lecturer.The results show that prior to linear regression, the scoring system tends to give higher score(biased score) compared to the manual score,which is problematic. The following scoring system with linear regression tackles this problem as the scoring bias is significantly reduced, that is, no tendency to givehigher or less scorecompared to the manual score.That the scoring bias is significantly reduced using a simple approach, linear regression,is expected to contribute in the acceleration of implementingautomatedessay scoring system on online learning technologiessuch as e-learning.]]>
<![CDATA[Metode Imputasi pada Data Debit Daerah Aliran Sungai Opak, Provinsi DI Yogyakarta ]]>
<![CDATA[Fahmi Dhimas Irnawan]]>;
<![CDATA[Indriana Hidayah]]>;
<![CDATA[Lukito Edi Nugroho]]>
<![CDATA[ Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 10 No 4: November 2021 ]]>
Publisher : <![CDATA[Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada ]]>
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DOI: 10.22146/jnteti.v10i4.2430
<![CDATA[The data availability of water resources in Indonesia has several complex problems related to the perfection of data. The problems taking place when collecting data in several Indonesian agencies are the accuracy and completeness of the data. There are several methods that can be used to handle missing value imputation, such as k-Nearest Neighbors Imputation (k-NNi) and Multivariate Imputation by Chained Equation (MICE). This study seeks to compare and find the most appropriate method using the Opak watershed dataset in Special Region of Yogyakarta. The characteristics of the Opak watershed lies in its fan shape that provides a lower concentration-time and produces a higher flow. The results of the statistical validation comparison showed that the most consistent average value of RMSE and MAE was the k-NNi method with a value of k = 28. As for the comparison of R-Squared values, the k-NNi method with a value of k = 28 obtained the best average value with 80%, followed by the k-NNi method of k = 7 as the default k value with a percentage of 73%. Among the applied methods, the MICE comparison method obtained the lowest average percentage value with 63%.]]>
<![CDATA[Penerapan Metode Certainty Factor Dalam Diagnosis Gangguan Depresi ]]>
<![CDATA[Septian Rico Hernawan]]>;
<![CDATA[Hanung Adi Nugroho]]>;
<![CDATA[Indriana Hidayah]]>
<![CDATA[ Journal of Computer System and Informatics (JoSYC) Vol 3 No 2 (2022): Februari 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/josyc.v3i2.643
<![CDATA[Globalization can cause several problems and pressures of mind for both individuals and groups. Various kinds of problems can certainly lead to psychological disorders, one of which is depression. Indonesia itself is one of the countries with a high number of people with depressive disorders. Depressive disorder itself can have many consequences ranging from lack of enthusiasm to even death. Facing these serious problems, the government should be able to address the mental disorder that is currently happening. However, the reality is still far from this. Inadequate infrastructure, equality problems for each region, and shortages of experts are the main problems at this time. The expert system is considered to be a solution in solving these problems. Web-based expert systems can replace the role of experts in the process of initial diagnosis of depressive disorders, patients can also access them easily. The calculation method implemented is the Certainty Factor method. This method is considered suitable in the diagnosis of depression. The implementation of the CF method in the diagnosis of depression can provide a confidence level of up to 94.9%. The expert system is expected to be able to eliminate human errors, speed up the diagnostic process, make it easier for health workers, and provide standards for related parties in handling mental disorders]]>
<![CDATA[Improving Data Quality and Data Governance Using Master Data Management: A Review ]]>
<![CDATA[Sanny Hikmawati]]>;
<![CDATA[Paulus Insap Santosa]]>;
<![CDATA[Indriana Hidayah]]>
<![CDATA[ IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 5, No 3 (2021): September 2021 ]]>
Publisher : <![CDATA[Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM ]]>
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DOI: 10.22146/ijitee.66307
<![CDATA[Master data management (MDM) is a method of maintaining, integrating, and harmonizing master data to ensure consistent system information. The primary function of MDM is to control master data to keep it consistent, accurate, current, relevant, and contextual to meet different business needs across applications and divisions. MDM also affects data governance, which is related to establishing organizational actors’ roles, functions, and responsibilities in maintaining data quality. Poor management of master data can lead to inaccurate and incomplete data, leading to lousy stakeholder decision-making. This article is a literature review that aims to determine how MDM improves the data quality and data governance and assess the success of MDM implementation. The review results show that MDM can overcome data quality problems through the MDM process caused by data originating from various scattered sources. MDM encourages organizations to improve data management by adjusting the roles and responsibilities of business actors and information technology (IT) staff documented through data governance. Assessment of the success of MDM implementation can be carried out by organizations to improve data quality and data governance by following the existing framework.]]>
<![CDATA[The Evaluation of AR Mobile App as a Learning Media for Children ]]>
<![CDATA[Adhistya Erna Permanasari]]>;
<![CDATA[Indriana Hidayah]]>;
<![CDATA[Faizal M. Priyowibowo]]>;
<![CDATA[M. Arifin Hidayat]]>;
<![CDATA[Fachrul Budi Prayoga]]>;
<![CDATA[Intan Sulistyaningrum Sakkinah]]>
<![CDATA[ IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 5, No 3 (2021): September 2021 ]]>
Publisher : <![CDATA[Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM ]]>
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DOI: 10.22146/ijitee.68757
<![CDATA[A user evaluation stage is an integral part of designing an application. A practical evaluation can provide an overview of the problems that arise in the application and improve the user experience. The Kupuku application is an augmented reality (AR)-based game application for learning about butterflies. The Kupuku application is specifically intended for children aged 6-13 years. The user sample was selected using a purposive sampling method with the criteria for users of elementary school-age children for the child user segment and their companions as the adult user segment. This study aims to evaluate the usability of the Kupuku game application to users. User evaluation was carried out to measure the application’s usability. The evaluation process was conducted on two user segments, namely 20 child users and 16 adult users. Assessment of children employed the Fun Toolkit and usability factor-based question - Nielsen method. The obtained results showed positive feedbacks. In contrast, the assessment for adult users utilized the system usability scale (SUS) and the user experience questionnaire (UEQ). The SUS score of 76 was included in the good category, and the UEQ score produced an excellent average. The test results indicate that this application can be accepted by users, both children, and adults.]]>
<![CDATA[Kesesuaian Minat Mahasiswa dengan Judul Tesis Mahasiswa Menggunakan Metode Fuzzy Mamdani ]]>
<![CDATA[Astrie Kusuma Dewi]]>;
<![CDATA[Adhistya Erna Permanasari]]>;
<![CDATA[Indriana Hidayah]]>
<![CDATA[ Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 10 No. 1 (2016) ]]>
Publisher : <![CDATA[Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung ]]>
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DOI: 10.23960/elc.v10n1.188
<![CDATA[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.]]>
<![CDATA[Tracing Knowledge States through Student Assessment in a Blended Learning Environment ]]>
<![CDATA[Hidayah, Indriana]]>;
<![CDATA[Am, Ebedia Hilda]]>
<![CDATA[ Jurnal Teknik Elektro Vol 15, No 2 (2023): Jurnal Teknik Elektro ]]>
Publisher : <![CDATA[Jurusan Teknik Elektro, Fakultas Teknik, Universitas Negeri Semarang ]]>
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DOI: 10.15294/jte.v15i2.47861
<![CDATA[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.]]>
