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IJITEE (International Journal of Information Technology and Electrical Engineering)
Published by Universitas Gadjah Mada
ISSN : -     EISSN : 25500554     DOI : https://doi.org/10.22146/ijitee.48545
Core Subject : Engineering,
Electrical & Electronics Engineering
IJITEE (International Journal of Information Technology and Electrical Engineering), with registered number ISSN 2550-0554 (Online), is a peer-reviewed journal published four times a year (March, June, September, December) by Department of Electrical engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada. IJITEE (International Journal of Information Technology and Electrical Engineering) invites manuscripts in the various topics include, but not limited to, Information Technology, Power Systems, Digital Signal Processing, Communication Systems
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 5 Documents
 1 
Search results for , issue "Vol 3, No 3 (2019): September 2019" : 5 Documents clear
Remote Sensing Technology for Land Farm Mapping Based on NDMI, NDVI, and LST Feature Ahmad Fauzi Mabrur; Noor Akhmad Setiawan; Igi Ardiyanto
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 3, No 3 (2019): September 2019
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1513.335 KB) | DOI: 10.22146/ijitee.47430

Abstract

Remote Sensing is a reliable and efficient data acquisition techniques. This technique is widely used for land image processing. This technique has many advantages, especially in terms of cost and time. In this study, the classification between dry and irrigated land from irrigation canals is presented. Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST) values obtained from satellite imagery data are used in this process. It is expected that through this method, the distribution and control of irrigation water can optimize existing agricultural potential. Ground Check (GC) is used for validation process. The results showed that the error rate based on the moon was not so large, i.e., 18%. The highest errors occur in February and March. This happens because those months are the rainy season, so the measured temperature is mostly the temperature above the cloud layer. On the other hand, the lowest error occurs in November. Also, it can be seen that this method can function optimally when detecting residential areas or highways.
Deep Learning Methods for EEG Signals Classification of Motor Imagery in BCI Muhammad Fawaz Saputra; Noor Akhmad Setiawan; Igi Ardiyanto
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 3, No 3 (2019): September 2019
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1033.036 KB) | DOI: 10.22146/ijitee.48110

Abstract

EEG signals are obtained from an EEG device after recording the user's brain signals. EEG signals can be generated by the user after performing motor movements or imagery tasks. Motor Imagery (MI) is the task of imagining motor movements that resemble the original motor movements. Brain Computer Interface (BCI) bridges interactions between users and applications in performing tasks. Brain Computer Interface (BCI) Competition IV 2a was used in this study. A fully automated correction method of EOG artifacts in EEG recordings was applied in order to remove artifacts and Common Spatial Pattern (CSP) to get features that can distinguish motor imagery tasks. In this study, a comparative studies between two deep learning methods was explored, namely Deep Belief Network (DBN) and Long Short Term Memory (LSTM). Usability of both deep learning methods was evaluated using the BCI Competition IV-2a dataset. The experimental results of these two deep learning methods show average accuracy of 50.35% for DBN and 49.65% for LSTM.
Autocorrelation Method for Cyclic Prefix OFDM Estimation Desti Madya Saputri
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 3, No 3 (2019): September 2019
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1342.438 KB) | DOI: 10.22146/ijitee.48257

Abstract

A radio system design providing various data service needs becomes one of the Software Defined Radio (SDR) system advantages. SDR technology applies software functions further to be run in hardware platforms. The need for services with greater data rates can be resolved by using multi-carrier transmission techniques, one of which is the Orthogonal Frequency Division Multiplexing (OFDM) technique. This paper discusses the detection of OFDM signals and their parameters. Multi-carrier transmission can prevent Inter-Symbol Interference (ISI) occurrence due to multi-path fading effect. The recognition can classify the correctly received signals, including the signal conditions mixed with AWGN noise. The autocorrelation method was used to estimate the OFDM parameters, namely the one symbol duration and the cyclic prefix duration. The detected cyclic prefix durations were 1/2, 1/4, 1/8, and 1/16. This method is very simple, because with the cyclic prefix presence, a different signal peak will be detected to further estimate the cyclic prefix duration. The results show the correlation method performance can detect one symbol duration with 100%, accuracy, starting at SNR 0 dB, whereas the cyclic prefix duration accuracy rate is getting more accurate by using a less cyclic prefix duration, which is 1/16 of the total symbol duration.
Designing a Smart Mirror as a Laboratory Information Media Using Raspberry Pi Denny Hardiyanto; Galang Wicaksono; Anggoro S Pramudyo; Rian Fahrizal; Romi Wiryadinata
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 3, No 3 (2019): September 2019
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1379.643 KB) | DOI: 10.22146/ijitee.48436

Abstract

Development of microprocessor technology provides new ideas for creating smart devices, one of which is in the field of smart home. Smart home is a concept of a home integrated with a smart system and supported by technology that enables all work to be more effective and efficient. Mirror is a household device that is beneficial to humans. In this paper, a research on smart mirrors is explained. A smart mirror is a mirror integrated with an intelligent system so that it can display multimedia data originating from the internet using Raspberry as a computing tool, PIR sensor as a tool to control monitors, and DC fans as a tool to control temperature system. In this paper, the mirror was able to display information about time, weather, academic calendar, lab work schedules, prayer schedules, and academic news. A PIR sensor has a good accuracy when the device is placed at 180 cm above the ground and the distance between mirror and humans when mirroring is 70 cm. A DC fan was utilized to stabilize the system temperature in a range of 40 to 50 oC.
Khmer Treebank Construction via Interactive Tree Visualization Bonpagna Kann; Thodsaporn Chay-intr; Hour Kaing; Thanaruk Theeramunkong
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 3, No 3 (2019): September 2019
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1182.611 KB) | DOI: 10.22146/ijitee.48545

Abstract

Despite the fact that there are a number of researches working on Khmer Language in the field of Natural Language Processing along with some resources regarding words segmentation and POS Tagging, we still lack of high-level resources regarding syntax, Treebanks and grammars, for example. This paper illustrates the semi-automatic framework of constructing Khmer Treebank and the extraction of the Khmer grammar rules from a set of sentences taken from the Khmer grammar books. Initially, these sentences will be manually annotated and processed to generate a number of grammar rules with their probabilities once the Treebank is obtained. In our experiments, the annotated trees and the extracted grammar rules are analyzed in both quantitative and qualitative way. Finally, the results will be evaluated in three evaluation processes including Self-Consistency, 5-Fold Cross-Validation, Leave-One-Out Cross-Validation along with the three validation methods such as Precision, Recall, F1-Measure. According to the result of the three validations, Self-Consistency has shown the best result with more than 92%, followed by the Leave-One-Out Cross-Validation and 5-Fold Cross Validation with the average of 88% and 75% respectively. On the other hand, the crossing bracket data shows that Leave-One-Out Cross Validation holds the highest average with 96% while the other two are 85% and 89%, respectively.

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