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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics JURNAL NASIONAL TEKNIK ELEKTRO JOIV : International Journal on Informatics Visualization Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Cyberspace: Jurnal Pendidikan Teknologi Informasi Jurnal Elektronika dan Telekomunikasi Jurnal Manajemen Informatika dan Sistem Informasi Community Development Journal: Jurnal Pengabdian Masyarakat
Amrul Faruq
Universitas Muhammadiyah Malang
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Public Health Other

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Diabetes prediction based on discrete and continuous mean amplitude of glycemic excursions using machine learning Lailis Syafaah; Setio Basuki; Fauzi Dwi Setiawan Sumadi; Amrul Faruq; Mauridhi Hery Purnomo
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2387

Abstract

Chronic hyperglycemia and acute glucose fluctuations are the two main factors that trigger complications in diabetes mellitus (DM). Continuous and sustainable observation of these factors is significant to be done to reduce the potential of cardiovascular problems in the future by minimizing the occurrence of glycemic variability (GV). At present, observations on GV are based on the mean amplitude of glycemic excursion (MAGE), which is measured based on continuous blood glucose data from patients using particular devices. This study aims to calculate the value of MAGE based on discrete blood glucose observations from 43 volunteer patients to predict the diabetes status of patients. Experiments were carried out by calculating MAGE values from original discrete data and continuous data obtained using Spline Interpolation. This study utilizes the machine learning algorithm, especially k-Nearest Neighbor with dynamic time wrapping (DTW) to measure the distance between time series data. From the classification test, discrete data and continuous data from the interpolation results show precisely the same accuracy value that is equal to 92.85%. Furthermore, there are variations in the MAGE value for each patient where the diabetes class has the most significant difference, followed by the pre-diabetes class, and the typical class. 
Adaptive infill sampling strategy for metamodeling: Challenge and future research directions Che Munira Che Razali; Shahrum Shah Abdullah; Amir Parnianifard; Amrul Faruq
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (515.189 KB) | DOI: 10.11591/eei.v9i5.2162

Abstract

The widespread use of computer experiments for design optimization has made the issue of reducing computational cost, improving accuracy, removing the “curse of dimensionality” and avoiding expensive function approximation becoming even more important. Metamodeling also known as surrogate modeling, can approximate the actual simulation model allowing for much faster execution time thus becoming a useful method to mitigate these problems. There are two (2) well-known metamodeling techniques which is kriging and radial basis function (RBF) discussed in this paper based on widely used algorithm tool from previous work in modern engineering design of optimization. An integral part of metamodeling is in the method to sample new data from the actual simulation model. Sampling new data for metamodeling requires finding the location (or value) of one or more new data such that the accuracy of the metamodel can be increased as much as possible after the sampling process. This paper discussed the challenges of adaptive sampling in metamodel and proposed an ensemble non-homogeneous method for best model voting to obtain new sample points.
Co-Authors Achmad Fauzan Hery Afirda Desember Riawati Agus Heriyanto Akhmad Hakam Sudrajat Aminaton Marto Aminaton Marto Amir Parnianifard Basuki, Setio Cahyadi, Basri Nor Chasrun Hasani Che Munira Che Razali Che Munira Che Razali Diding Suhardi Ermanu Azizul Hakim Erwin Nurdiansyah Fitri Yakub Guntur Alam Hasani, Chasrun Ilham Pakaya Indah Sulistiyowati Irfan, Muhammad Izzaty, Nadia Karima Khaeruddin Khaeruddin Khaeruddin Khaeruddin Khusnul Hidayat Kuye, Abidemi Tolulope M. Fauzi Nor Shah M. Irfan Machmud Effendy Machmud Effendy Machmud Effendy Mauridhi Hery Purnomo Merinda Lestandy Mochammad Arif Hidayatullah Mohd Anuar Abu Bakar Mohd Fauzi Nor Shah MOHD HUSSEIN, SHAMSUL FAISAL Mohd Sabre, Mohamad Syafiq Muhammad Irfan Muhammad Irfan Muhammad Nasar Noor Cahyadi, Basri Novendra Setyawan Nugroho, Herry Prasetyo Nur Alif Nur Alif Mardiyah Nur Alif Mardiyah, Nur Alif Nur Kasan, Nur Nuralif, Nuralif Nurdiansyah, Erwin Octavio, Abi Mufid Shahrum Shah Abdullah Shahrum Shah Abdullah Shahrum Shah Abdullah Shahrum Shah Abdullah Shahrum Shah Bin Abdullah Shamsul Faisal Mohd Hussein Sudrajat, Akhmad Hakam Sumadi, Fauzi Dwi Setiawan Syafaah, Lailis Syafaah, Lailis Vhirdausia, Nuri Wijaya, Frenischa Yincenia Zulfatman Zulfatman