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Iis Hamsir Ayub wahab
Scopus ID : 57189354771, Department of Electrical Engineering, Khairun University, Ternate,
Computer Science & IT Electrical & Electronics Engineering Energy

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A Multimodal Deep Learning Framework for Early Detection of Congenital Heart Disease in Neonates Iis Hamsir Ayub wahab; Sri Yati
International Journal Of Electrical Engineering and Inteligent Computing Vol 2, No 2 (2025): International Journal Of Electrical Engineering And Intelligent Computing
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/ijeeic.v2i2.10059

Abstract

Congenital heart disease (CHD) is the most common congenital defect and still adds significantly to the neonatal morbidity and mortality rates. Classic echocardiography and ECG unimodal data traditional methods are often unable to analyze complex, multifunctional, and multifactorial cardiac pathologies in neonates. This paper presents an explainable multimodal deep learning framework that acquires four diverse sources of clinical data. Multimodal data includes echocardiogram videos, ECG, and other physiological and structured electronics health record (HER) data. We propose a self-attention-based late fusion transformer architecture that also uses self-attention mechanisms. The model trains and validates on benchmark datasets, which are transparently and reproducibly available (EchoNet-Dynamic, MIMIC-IV, PhysioNet Capnobase, and MIT-BIH). The results achieved using the proposed model mark an improvement over existing benchmarks with 93% accuracy, 95% sensitivity, and 0.96 area under the ROC curve. Using interpretability modules, features that were value added towards determining the diagnostic indicators that were incorporated in the neonatal infant care were shown to be critically relevant. Moreover, the model shows high performance consistency across several data sources and shifts. The research illustrates the use of explainable deep learning architectures for automation of early-stage heart defect detection in newborns. Some of the future work includes validation through clinical studies and multilingual electronic health record integration.
Design a Sign Language Translator Using Flexible Sensors Iis Hamsir Ayub Wahab; Zulaeha Mabud; Bujur Jalali
International Journal Of Electrical Engineering and Inteligent Computing Vol 1, No 1 (2023): International Journal of Electrical Engineering and Intelligent Computing
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/ijeeic.v1i1.7209

Abstract

In terms of communication skills, many of us have limitations and shortcomings or what we are more familiar with is speech impairment Speaking is the ability to pronounce articulated sounds or words to express, express and convey thoughts, ideas and feelings. Communication skills can include many ways, including using verbal skills, namely verbally and non-verbally. In Indonesia, there are two sign languages used, namely Indonesian sign language (BISINDO) and Indonesian sign system (SIBI). BISINDO is a sign language that appears naturally in Indonesian culture and is practical for use in everyday life so that BISINDO has several variations in each region. The flex sensor has a thin and densely curved shape so that the flex sensor can be used as a motion detection and finger curve. Flex sensor application for human movement detection, patient monitoring. Therefore, hand-to-letter/text sign language translators using flexible sensors is a very important problem today. The method carried out is system design using tools and components used in research. This tool using the working principle in this system is to translate the sign language of alphabetic letters using flexible sensors. Design sign language translation of alphabetic letters using flexible sensors. The test to display the letters of the alphabet A-Z has a total minimum and maximum resistance at a flexible session of 1000 ohms with a voltage of 5V each. The result of this data is that there is no error in the play because the range value does not violate each other with other range values
Prediction of the Number of Motorized Vehicles in Ternate City Using the Average Based Fuzzy Time Series Model Method Friyanti Friyanti; Iis Hamsir Ayub Wahab; Arbain Tata
International Journal Of Electrical Engineering and Inteligent Computing Vol 1, No 2 (2024): International Journal Of Electrical Engineering And Intelligent Computing
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/ijeeic.v1i2.9110

Abstract

Prediction or forecasting is one of the most important elements in decision-making. The impact caused is the number of motorized vehicles, residents, roads, and area area. By predicting the number of motor vehicles, the prediction data can be used from a program to reduce the impact of a high number of motor vehicles. This study aims to determine the Prediction of the Number of Motorized Vehicles in Ternate City using the Average Based Fuzzy Time Series Model Method in Ternate City from 2019 to 2024. Settlement using Average Based Method data and fuzzy time series interval numbers have been determined at the beginning of the calculation process, this process is very influential in the formation of fuzzyrelationship on each number to compare each other which will certainly have an impact on the difference in the results of the reduction calculation. The test results are known that the Fuzzy time series is one of the methods for prediction. One type of method is the average-based fuzzy time series with the average total value calculated using the Mean Absolute Percentage Error (MAPE) method obtained from the number of each indicator of 2.98% which shows that this study is included in the category of good used in the prediction of motor vehicles in Ternate City because it has an accuracy value of less than 20%. From the predictions carried out, the MAPE value of the test was 1.01%, the MSE value of forecasting was 1400.5, and the MAD value of forecasting was 27.93.
Co-Authors Arbain Tata Bujur Jalali Friyanti Friyanti Sri Yati Zulaeha Mabud