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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Resona : Jurnal Ilmiah Pengabdian Masyarakat Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Jurnal Repositor
Sumadi, Fauzi Dwi Setiawan
Universitas Muhammadiyah Malang
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Environmental Science Public Health Social Sciences

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Classification of batik patterns using K-Nearest neighbor and support vector machine Agus Eko Minarno; Fauzi Dwi Setiawan Sumadi; Hardianto Wibowo; Yuda Munarko
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (664.555 KB) | DOI: 10.11591/eei.v9i3.1971

Abstract

This study is proposed to compare which are the better method to classify Batik image between K-Nearest neighbor and support vector machine using minimum features of GLCM. The proposed steps are started by converting image to grayscale and extracting colour feature using four features of GLCM. The features include energy, entropy, contras, correlation and 0o, 45o, 90o, and 135o. The classifier features consist of 16 features in total. In the experimental result, there exist comparison of previous works regarding the classification KNN and SVM using multi texton histogram (MTH). The experiments are carried out in the form of calculation of accuracy with data sharing and cross-validation scenario. From the test results, the average accuracy for KNN is 78.3% and 92.3% for SVM in the cross-validation scenario. The scenario for the highest accuracy of data sharing is at 70% for KNN and at 100% for SVM. Thus, it is apparent that the application of the GLCM and SVM method for extracting and classifying batik motifs has been effective and better than previous work.
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. 
Co-Authors - Syaifuddin Abyan Faishal Reza Achmad Irfani Nur Iman Agung Wahyu Purnadi Agus Eko Minarno Agus Eko Minarno Ahmad Akbar Maulana Ahmad Fahri Jamaluddin Ahmad Rizky Habibullah Akbi, Denar Regata Alrizal Rakhmat Widagdo Amrul Faruq Atthariq, Muhammad Daffa Basuki, Setio Chandranegara, Didih Rizki Christian Sri Kusuma Aditya Diah Risqiwati Edi H. Kalabo Flora Salfa Salsabila Hanung Adi Nugroho Hardianto Wibowo Haris Zulfikar Islam Hariyady Hidayat, Rizky Fauzi Ari Ibrahim, Zaidah Irfan, Muhammad Irmawati Feren Kilwalaga Jurdan Wahyu Adi Saputra Lailis Syafa’ah Lucky Nurfiqin Mahar Faiqurahman Mauridhi Hery Purnomo Mirza Maulana Azmi Moch Rizky Wibowo Muhamad Fathul Azis Muhammad Abizar Muhammad Ferry Septian Ihzanor Syahputra Muhammad Nur Ichsan Muhammad Zain Iqbal Muzakki Muhammad Zidan Najmuddin Tsaqib Nur Armita Nuryasin, Ilyas Oxicusa Gugi Housman Sadida, Medina Kaulan Said imam Baihaqqi Setiyo Kantomo, Ilham Sri Kusuma Aditya, Christian Syafa'ah, Lailis Syafaah, Lailis Syaifuddin Syaifuddin Syaifuddin Syaifuddin Syaifuddin, Syaifuddin Vera Suryani Wahyuli Dwiki Nanda Yoga Pamungkas Yuda Munarko Yusril Izza Rizqony Zamah Sari