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All Journal International Journal of Electrical and Computer Engineering International Journal of Informatics and Communication Technology (IJ-ICT) Indonesian Journal of Electronics and Instrumentation Systems IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) INFORMAL: Informatics Journal Creative Information Technology Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Indonesian Journal of Electrical Engineering and Computer Science Kaunia: Integration and Interconnection of Islam and Science Journal
MHD. Reza M.I. Pulungan
Department Of Computer Science And Electronics, FMIPA UGM, Yogyakarta
Religion Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Library & Information Science Mathematics Mechanical Engineering Physics Transportation

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Journal : International Journal of Electrical and Computer Engineering

 1 
Deep Belief Networks for Recognizing Handwriting Captured by Leap Motion Controller Abas Setiawan; Reza Pulungan
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 6: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (677.119 KB) | DOI: 10.11591/ijece.v8i6.pp4693-4704

Abstract

Leap Motion controller is an input device that can track hands and fingers position quickly and precisely. In some gaming environment, a need may arise to capture letters written in the air by Leap Motion, which cannot be directly done right now. In this paper, we propose an approach to capture and recognize which letter has been drawn by the user with Leap Motion. This approach is based on Deep Belief Networks (DBN) with Resilient Backpropagation (Rprop) fine-tuning. To assess the performance of our proposed approach, we conduct experiments involving 30,000 samples of handwritten capital letters, 8,000 of which are to be recognized. Our experiments indicate that DBN with Rprop achieves an accuracy of 99.71%, which is better than DBN with Backpropagation or Multi-Layer Perceptron (MLP), either with Backpropagation or with Rprop. Our experiments also show that Rprop makes the process of fine-tuning significantly faster and results in a much more accurate recognition compared to ordinary Backpropagation. The time needed to recognize a letter is in the order of 5,000 microseconds, which is excellent even for online gaming experience.
Development of a Spatial Path-Analysis Method for Spatial Data Analysis Wiwin Sulistyo; Subanar Subanar; Reza Pulungan
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 4: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1162.002 KB) | DOI: 10.11591/ijece.v8i4.pp2456-2467

Abstract

Path analysis is a method used to analyze the relationship between independent and dependent variables to identify direct and indirect relationship between them. This method is developed by Sewal Wright and initially only uses correlation analysis results in identifying the variables' relationship. Path analysis method currently is mostly used to deal with variables with non-spatial data type. When analyzing variables that have elements of spatial dependency, path analysis could result in a less precise model. Therefore, it is necessary to build a path analysis model that is able to identify and take into account the effects of spatial dependencies. Spatial autocorrelation and spatial regression methods can be used to develop path analysis method so as to identify the effects of spatial dependencies. This paper proposes a method in the form of path analysis method development to process data that have spatial elements. This study also discusses our effort on establishing a method that could be used to identify and analyze the spatial effect on data in the framework of path analysis; we call this method spatial path analysis.
Co-Authors A Afiahayati Abas Setiawan Agfianto Eko Putra Agus Halid Aji Purwinarko, Aji Anindita Suryarasmi Ardhi Wiratama Baskara Yudha Aslan Alwi Dian Tjondronegoro Dian Tri Wiyanti Gede Saindra Santyadi Gede Sukadarmika Helmi Kurniawan Hermawan Syahputra I Dewa I Dewa Md. Bayu A. Darmawan I Dewa Made Bayu Atmaja Darmawan I Nym Saputra Wahyu Wijaya Ika Oktavia Suzanti Jazi Eko Istiyanto La Surimi Linawati Lutfan Lazuardi Mardhani Riasetiawan Moh Edi Wibowo Muhamad Radzi Rathomi Muhammad Asghar Nazal Muhammad Ihsan Munirah M Ni Made Ary Esta Dewi Wirastuti Nila Feby Puspitasari Nova El Maidah Pande Nyoman Ariyuda Semadi Pramudita, Yoga Dwitya Putu Indah Ciptayani Rachmat Wahid Saleh Insani Radjabaycolle, Jefri Siti Mutmainah Sri Hartati Subanar . Tri Bowo Atmojo Triyono, Gandung Vinod Chandran Wiwin Sulistyo