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Novi Azman
Universitas Nasional & Universiti Teknikal Malaysia Melaka
Computer Science & IT Electrical & Electronics Engineering

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Development of Embedded System for Centralized Insomnia System Novi Azman; Mohd Khanapi Abd Ghani; Muhammad Haikal Satria; Muhammad Zillullah Mukaram
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (367.536 KB) | DOI: 10.11591/eecsi.v5.1600

Abstract

Insomnia is a common health problem in medical field as well as in psychiatry. The measurement of those factors could be collected by using polysomnography as one of the current standards. However, due to the routine of clinical assessment, the polysomnography is impractical and limited to be used in certain place. The rapid progress of electronic sensors to support IoT in health telemonitoring should provide the real time diagnosis of patient at home too. In this research, the development of centralized insomnia system for recording and analysis of patient with chronic-insomnia data is proposed. The system is composed from multi body sensors that connected to main IOT server. The test has been done for 5 patients and the result has been successfully retrieved in real time.
Artificial Neural Network Parameter Tuning Framework For Heart Disease Classification Mohamad Haider Abu Yazid; Haikal Satria; Shukor Talib; Novi Azman
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (345.041 KB) | DOI: 10.11591/eecsi.v5.1695

Abstract

Heart Disease are among the leading cause of death worldwide. The application of artificial neural network as decision support tool for heart disease detection. However, artificial neural network required multitude of parameter setting in order to find the optimum parameter setting that produce the best performance. This paper proposed the parameter tuning framework for artificial neural network. Statlog heart disease dataset and Cleveland heart disease dataset is used to evaluate the performance of the proposed framework. The results show that the proposed framework able to produce high classification accuracy where the overall classification accuracy for Cleveland dataset is 90.9% and 90% for Statlog dataset.
Co-Authors Haikal Satria Mohamad Haider Abu Yazid Mohd Khanapi Abd Ghani Muhammad Haikal Satria Muhammad Zillullah Mukaram Shukor Talib