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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) CommIT (Communication & Information Technology) International Journal of Advances in Intelligent Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Communications in Science and Technology IJITEE (International Journal of Information Technology and Electrical Engineering) Indonesian Journal of Electrical Engineering and Computer Science Narra J Jurnal Nasional Teknik Elektro dan Teknologi Informasi Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Igi Ardiyanto
Universitas Gadjah Mada
Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Mathematics Medicine & Pharmacology Public Health

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Journal : IJITEE (International Journal of Information Technology and Electrical Engineering)

 1 
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.
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.
User Curiosity Factor in Determining Serendipity of Recommender System Arseto Satriyo Nugroho; Igi Ardiyanto; Teguh Bharata Adji
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 5, No 3 (2021): September 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.67553

Abstract

Recommender rystem (RS) is created to solve the problem by recommending some items among a huge selection of items that will be useful for the e-commerce users. RS prevents the users from being flooded by information that is irrelevant for them.Unlike information retrieval (IR) systems, the RS system's goal is to present information to the users that is accurate and preferably useful to them. Too much focus on accuracy in RS may lead to an overspecialization problem, which will decrease its effectiveness. Therefore, the trend in RS research is focusing beyond accuracy methods, such as serendipity. Serendipity can be described as an unexpected discovery that is useful. Since the concept of a recommendation system is still evolving today, formalizing the definition of serendipity in a recommendation system is very challenging.One known subjective factor of serendipity is curiosity. While some researchers already addressed curiosity factor, it is found that the relationships between various serendipity component as perceived by the users and their curiosity levels is still yet to be researched. In this paper, the method to determine user curiosity model by considering the variation of rated items was presented, then relation to serendipity components using existing user feedback data was validated. The finding showed that the curiosity model was related to some user-perceived values of serendipity, but not all. Moreover, it also had positive effect on broadening the user preference. 
Co-Authors Adhistya Erna Permanasari Ahmad Fauzi Mabrur Ahmad Nasikun Aras, Rezty Amalia Atmaja Perdana, Chandra Ramadhan Dian Nova Kusuma Hardani Elfrida Ratnawati Erwin Setyo Nugroho Hanung Adi Nugroho Luky Hidayat Made Rahmawaty, Made Muhammad Fawaz Saputra Nasikun, Ahmad Noor Akhmad Setiawan Nugroho, Arseto Satriyo Nugroho, Erwin S. Nugroho, Hanung A. Nurcahyani Wulandari Nuryono, Aninditya Anggari Oktoeberza, Widhia KZ Persada, Anugerah Galang Rizky Naufal Perdana Rudy Hartanto Setialana, Pradana Silvester Tena Siti Helmyati Sulis Setiowati Sulis Setiowati Sunu Wibirama Teguh Bharata Adji Tri Lestari Woraratpanya, Kuntpong Yenny Rahmawati Yuli Triyani Yulyanti, Vesi