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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Perfecting a Video Game with Game Metrics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik 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) Water Circulation Monitoring System in The Vanname Shrimp Cultivation based on Internet of Thing 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,

Author-ID : 2453157

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

Published : 24 Documents Claim Missing Document

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Tinjauan Pustaka Sistematis: Implementasi Metode Deep Learning pada Prediksi Kinerja Murid Muhammad Haris Diponegoro; Sri Suning Kusumawardani; Indriana Hidayah
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 10 No 2: Mei 2021
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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.

Regresi Linear untuk Mengurangi Bias Sistem Penilaian Uraian Singkat Silmi Fauziati; Adhistya Erna Permanasari; Indriana Hidayah; Eko Wahyu Nugroho; Bobby Rian Dewangga
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 10 No 3: Agustus 2021
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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.

Metode Imputasi pada Data Debit Daerah Aliran Sungai Opak, Provinsi DI Yogyakarta Fahmi Dhimas Irnawan; Indriana Hidayah; Lukito Edi Nugroho
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 10 No 4: November 2021
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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%.

Co-Authors Adhistya Erna Permanasari Astrie Kusuma Dewi Bobby Rian Dewangga Cahyani Windarto Diah Priyawati Ebedia Hilda Am Eko Nugroho Eko Wahyu Nugroho Emi Iryanti Fachrul Budi Prayoga Fahmi Dhimas Irnawan Faizal M. Priyowibowo Hanung Adi Nugroho Hanung Adi Nugroho 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 Shindy Arti Silmi Fauziati Sri Kustanti Sri Suning Kusumawardani Stefani, Brillian Stefani, Brillian Tari Mardiana Teguh Bharata Adji Widiyanti, Tanty Yanuar Winarno Winarno

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