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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 5, No 1 (2021): March 2021" : 5 Documents clear
ECG Signal Classification Review Muhammad Rausan Fikri; Indah Soesanti; Hanung Adi Nugroho
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 5, No 1 (2021): March 2021
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijitee.60295

Abstract

The heart is an important part of the human body, functioning to pump blood through the circulatory system. Heartbeats generate a signal called an ECG signal. ECG signals or electrocardiogram signals are basic raw signals to identify and classify heart function based on heart rate. Its main task is to analyze each signal in the heart, whether normal or abnormal. This paper discusses some of the classification methods which most frequently used to classify ECG signals. These methods include pre-processing, feature extraction, and classification methods such as MLP, K-NN, SVM, CNN, and RNN. There were two stages of ECG classification, the feature extraction stage and the classification stage. Before ECG features were extracted, raw ECG signal data first processed in the pre-processing stage because ECG signals were not necessarily free of noise. Noise will cause a decrease in accuracy during the classification process. After features were extracted, ECG signals were then classified with the classification method. Neural Network methods such as CNN and RNN are best to use since they can give better accuracy. For further research, the machine learning method needs to be improved to get high accuracy and high precision in the ECG signals classification.
Modified Usability Test Scenario: User Story Approach to Evaluate Data Visualization Dashboard Nurul Tiara Kadir; Rudy Hartanto; Selo Sulistyo
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 5, No 1 (2021): March 2021
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijitee.61201

Abstract

The data processing results are commonly displayed in a dashboard with various graphic visualization forms to deliver new knowledge easier to understand by users. However, many data analysis dashboards cannot communicate the knowledge effectively and efficiently given the unsuitable design implementation. Therefore, research to measure the interface display's effectiveness in the data analysis system is deemed necessary. This research proposed a scenario modification in the usability test with a user story approach to measuring the system interface display in delivering the information to users. The approach of a usability test with the user story is expected to be capable of helping the researcher in understanding the user habits indirectly. There were 20 participants to validate the proposed method. Participants were asked to use the system and answer several questions to develop their user experience. After developing user experience for each user, the System Usability Scale (SUS) was conducted. SUS score results obtained from this research was 75.25. Besides, the researcher also measured the understanding level among the users using questionnaires. The questionnaire results were converted into numbers and resulted in a mean value of 91.8. Those two values indicate the users' ability to use the system well and obtain the new knowledge displayed in the data analysis dashboard.
A Review on Face Anti-Spoofing Rizky Naufal Perdana; Igi Ardiyanto; Hanung Adi Nugroho
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 5, No 1 (2021): March 2021
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijitee.61827

Abstract

The biometric system is a security technology that uses information based on a living person's characteristics to verify or recognize the identity, such as facial recognition. Face recognition has numerous applications in the real world, such as access control and surveillance. But face recognition has a security issue of spoofing. A face anti-spoofing, a task to prevent fake authorization by breaching the face recognition systems using a photo, video, mask, or a different substitute for an authorized person's face, is used to overcome this challenge. There is also increasing research of new datasets by providing new types of attack or diversity to reach a better generalization. This paper review of the recent development includes a general understanding of face spoofing, anti-spoofing methods, and the latest development to solve the problem against various spoof types.
Serendipity Identification Using Distance-Based Approach Widhi Hartanto; Noor Akhmad Setiawan; Teguh Bharata Adji
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 5, No 1 (2021): March 2021
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijitee.62344

Abstract

The recommendation system is a method for helping consumers to find products that fit their preferences. However, recommendations that are merely based on user preference are no longer satisfactory. Consumers expect recommendations that are novel, unexpected, and relevant. It requires the development of a serendipity recommendation system that matches the serendipity data character. However, there are still debates among researchers about the available common definition of serendipity. Therefore, our study proposes a work to identify serendipity data's character by directly using serendipity data ground truth from the famous Movielens dataset. The serendipity data identification is based on a distance-based approach using collaborative filtering and k-means clustering algorithms. Collaborative filtering is used to calculate the similarity value between data, while k-means is used to cluster the collaborative filtering data. The resulting clusters are used to determine the position of the serendipity cluster. The result of this study shows that the average distance between the recommended movie cluster and the serendipity movie cluster is 0.85 units, which is neither the closest cluster nor the farthest cluster from the recommended movie cluster.
Image Analysis for MRI-Based Brain Tumor Classification Using Deep Learning Krisna Nuresa Qodri; Indah Soesanti; Hanung Adi Nugroho
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 5, No 1 (2021): March 2021
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijitee.62663

Abstract

Tumors are cells that grow abnormally and uncontrollably, whereas brain tumors are abnormally growing cells growing in or near the brain. It is estimated that 23,890 adults (13,590 males and 10,300 females) in the United States and 3,540 children under the age of 15 would be diagnosed with a brain tumor. Meanwhile, there are over 250 cases in Indonesia of patients afflicted with brain tumors, both adults and infants. The doctor or medical personnel usually conducted a radiological test that commonly performed using magnetic resonance image (MRI) to identify the brain tumor. From several studies, each researcher claims that the results of their proposed method can detect brain tumors with high accuracy; however, there are still flaws in their methods. This paper will discuss the classification of MRI-based brain tumors using deep learning and transfer learning. Transfer learning allows for various domains, functions, and distributions used in training and research. This research used a public dataset. The dataset comprises 253 images, divided into 98 tumor-free brain images and 155 tumor images. Residual Network (ResNet), Neural Architecture Search Network (NASNet), Xception, DenseNet, and Visual Geometry Group (VGG) are the techniques that will use in this paper. The results got to show that the ResNet50 model gets 96% for the accuracy, and VGG16 gets 96% for the accuracy. The results obtained indicate that transfer learning can handle medical images.

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