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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) International Journal of Advances in Applied Sciences ComEngApp : Computer Engineering and Applications Journal Jupiter TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Ilmu Komputer dan Agri-Informatika Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Scientific Journal of Informatics ICON-CSE Annual Research Seminar Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Ilmiah Matrik JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Plastik Rekonstruksi Bioscientia Medicina : Journal of Biomedicine and Translational Research Indonesian Journal of Electrical Engineering and Computer Science Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Generic Bioscientia Medicina : Journal of Biomedicine and Translational Research Jurnal Penelitian Pendidikan

Siti Nurmaini

University Of Sriwijaya

Author-ID : 335335

Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Physics Other

Published : 108 Documents Claim Missing Document

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Articles

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Automated ECG Waveform Annotation Based on Stacked Long Short-Term Memory Annisa Darmawahyuni; Siti Nurmaini; Muhammad Naufal Rachmatullah
Computer Engineering and Applications Journal Vol 9 No 2 (2020)
Publisher : Universitas Sriwijaya

The classification of electrocardiogram (ECG) waveform segmentation techniques can be difficult due to physiological variation of heart rate and different characteristics of the different ECG waves in terms of shape, frequency, amplitude, and duration. The P-wave, PR-segment, QRS-complex, ST-segment, and T-wave are extracted as the feature for classification algorithm to diagnose specified cardiac disorders. This requires the implementation of algorithms that identify specific points within the ECG wave. Some previous computational algorithms for automatic classification of ECG segmentation are proposed to overcome limitations of manual inspection of the ECG. This study presents new insight into the ECG semantic segmentation problem is surmounted by a deep learning approach for automatic ECG wave-form. Long short-term memory (LSTM) is proposed for this task. This experimental study has been performed for six different waveforms of ECG signal that represents cardiac disorders obtained from the Physionet: QT database. Overall, LSTM performance achieved accuracy, sensitivity, specificity, precision, F1-score, is 93.36%, 86.85%, 95.78%, 81.79%, and 83.09%, respectively.

The Artificial Intelligence Readiness for Pandemic Outbreak COVID-19: Case of Limitations and Challenges in Indonesia Siti Nurmaini
Computer Engineering and Applications Journal Vol 10 No 1 (2021)
Publisher : Universitas Sriwijaya

Artificial intelligence (AI) technologies continue to play significant roles during the Coronavirus 2019 (COVID-19) pandemic in the world. However, health is an area where the rules are stringent and inflexible. This can be justified because it deals with human life. Nevertheless, at the same time, a large number of tests, certifications, and panels will lead to innovations in AI for healthcare that are longer, more complex, and difficult to incorporate into real-world applications. Indonesia has a lot of AI research, which is challenging to commercialize in medicine. These researches are not yet effective due to several limitations in terms of (i) the readiness of a skilled workforce to develop and use AI, (ii) the readiness of regulations that regulate the ethics of using and utilizing responsibly, (iii) the readiness of computational infrastructure and supporting data for AI modeling, and (iv) readiness industry and the public sector in adopting AI innovations. In pandemic outbreak COVID-19, AI technology should help the medical industry more significantly, caused by such limitations, and it has not yet been impactful against COVID-19 in Indonesia. In the future, AI technology exists as a complementary facility to increase the productivity of medical personnel and acts as a disease prevention facility.

Cloud-based ECG Interpretation of Atrial Fibrillation Condition with Deep Learning Technique Bambang Tutuko; Rossi Passarella; Firdaus Firdaus; Muhammad Naufal Rachmatullah; Annisa Darmawahyuni; Ade Iriani Sapitri; Siti Nurmaini
Computer Engineering and Applications Journal Vol 10 No 1 (2021)
Publisher : Universitas Sriwijaya

The prevalent type of arrhythmia associated with an increased risk of stroke and mortality is atrial fibrillation (AF). It is a known priority to identify AF before the first complication occurs. No previous studies have explored the feasibility of conducting AF screening using a deep learning (DL) algorithm (integrated cloud-computing) telehealth surveillance system. Hence, we address this problem. The goal of this research was to determine the feasibility of AF screening using an embedded cloud-computing algorithm in nonmetropolitan areas using a telehealth surveillance system. By using a single-lead electrocardiogram (ECG) recorder, we performed a prospective AF screening study. Both ECG measurements were evaluated and interpreted by the cloud-computing algorithm and a cardiologist on the telehealth monitoring system. The proposed cloud-computing based on Convolutional Neural Network (CNN) algorithm for AF detection had an accuracy of 99% sensitivity of 98%, and specificity of 99%. The overall satisfaction performance for the process of AF screening, and it is feasible to conduct AF screening by using a telehealth monitoring system containing an embedded cloud-computing algorithm.

Segmentation of Squamous Columnar Junction on VIA Images using U-Net Architecture Akhiar Wista Arum; Siti Nurmaini; Dian Palupi Rini; Patiyus Agustiansyah; Muhammad Naufal Rachmatullah
Computer Engineering and Applications Journal Vol 10 No 3 (2021)
Publisher : Universitas Sriwijaya

Cervical cancer is the second most common cancer that affects women, especially in developing countries including Indonesia. Cervical cancer is a type of cancer found in the cervix, precisely in the squamous columnar junction (SCJ). Early screening for cervical cancer can be reduce the risk of cervical cancer. One of the popular screening tool methods for the detection of cervical pre-cancer is the Visual Inspection with Acetic Acid (VIA) method. This is due to the level of effectiveness, convenience and low cost. This paper proposes a method for the detection and segmentation of the SCJ region on VIA images using U-Net. This study is the first research conducted using the CNN method to perform segmentation tasks in the SCJ region. The best performance results are shown from the Pixel Accuracy, Mean IoU, Mean Accuracy, Dice coefficient, Precision and Sensitivity values, namely 90.86%, 56.5%, 75.69%, 34.09%, 41.24%, and 56.91%. Keywords: Cervical Pre-cancer, Screening VIA, SCJ, U-Net.

Multiclass Segmentation of Pulmonary Diseases using Convolutional Neural Network Muhammad Arnaldo; Siti Nurmaini; Hadipurnawan Satria; Muhammad Naufal Rachmatullah
Computer Engineering and Applications Journal Vol 11 No 1 (2022)
Publisher : Universitas Sriwijaya

Pulmonary disease has affected tens of millions of people in the world. This disease has also become the cause of death of millions of its sufferers every year. In addition, lung disease has also become the cause of other respiratory complications, which also causes the death of the sufferer. The diagnosis of pulmonary diseases through medical imaging is a significant challenge in computer vision and medical image processing. The difficulty is due to the wide variety in infected areas' shape, dimension, and location. Another challenge is to differentiate one lung disease from the other. Discriminating pulmonary diseases is a notable concern in the diagnosis of pulmonary disease. We have adopted the deep learning convolutional neural network in this study to address these challenges. Seven models were constructed using the Mask Region-based Convolutional Neural Network (Mask-RCNN) architecture to detect and segment infected areas within the lung region from CT scan imagery. The evaluation results show that the best model obtained scores of 91.98%, 85.25%, and 93.75% for DSC, MIoU, and mAP, respectively. The segmentation results are then visualized.

Identification of Indonesian Authors Using Deep Neural Networks Firdaus Firdaus; Irvan Fahreza; Siti Nurmaini; Annisa Darmawahyuni; Ade Iriani Sapitri; Muhammad Naufal Rachmatullah; Suci Dwi Lestari; Muhammad Fachrurrozi; Mira Afrina; Bayu Wijaya Putra
Computer Engineering and Applications Journal Vol 11 No 1 (2022)
Publisher : Universitas Sriwijaya

Author Name Disambiguation (AND) is a problem that occurs when a set of publications contains ambiguous names of authors, i.e. the same author may appear with different names (synonyms) in other published papers, or author (authors) who may be different who may have the same name (homonym). In this final project, we will design a model with a Deep Neural Network (DNN) classifier. The dataset used in this final project uses primary data sourced from the Scopus website. This research focuses on integrating data from Indonesian authors. Parameters accuracy, sensitivity and precision are standard benchmarks to determine the performance of the method used to solve AND problems. The best DNN classification model achieves 99.9936% Accuracy, 93.1433% Sensitivity, 94.3733% Precision. Then for the highest performance measurement, the case of Non Synonym-Homonym (SH) has 99.9967% Accuracy, 96.7388% Sensitivity, and 97.5102% Precision.

Performance Comparison of Feature Face Detection Algorithm on The Embedded Platform Ahmad Zarkasi; Siti Nurmaini; Deris Stiawan; Bhakti Yudho Suprapto; Huda Ubaya; Rizki Kurniati
Computer Engineering and Applications Journal Vol 11 No 2 (2022)
Publisher : Universitas Sriwijaya

The intensity of light will greatly affect every process carried out in image processing, especially facial images. It is important to analyze how the performance of each face detection method when tested at several lighting levels. In face detection, various methods can be used and have been tested. The FLP method automates the identification of the location of facial points. The Fisherface method reduces the dimensions obtained from PCA calculations. The LBPH method converts the texture of a face image into a binary value, while the WNNs method uses RAM to process image data, using the WiSARD architecture. This study proposes a technique for testing the effect of light on the performance of face detection methods, on an embedded platform. The highest accuracy was achieved by the LBPH and WNNs methods with an accuracy value of 98% at a lighting level of 400 lx. Meanwhile, at the lowest lighting level of 175 lx, all methods have a fairly good level of accuracy, which is between 75% to 83%.

Comparative Analysis Multi-Robot Formation Control Modeling Using Fuzzy Logic Type 2 – Particle Swarm Optimization Anggun Islami; Siti Nurmaini; Hadipurnawan Satria
Computer Engineering and Applications Journal Vol 11 No 3 (2022)
Publisher : Universitas Sriwijaya

Multi-robot is a robotic system consisting of several robots that are interconnected and can communicate and collaborate with each other to complete a goal. With physical similarities, they have two controlled wheels and one free wheel that moves at the same speed. In this Problem, there is a main problem remaining in controlling the movement of the multi robot formation in searching the target. It occurs because the robots have to create dynamic geometric shapes towards the target. In its movement, it requires a control system in order to move the position as desired. For multi-robot movement formations, they have their own predetermined trajectories which are relatively constant in varying speeds and accelerations even in sudden stops. Based on these weaknesses, the robots must be able to avoid obstacles and reach the target. This research used Fuzzy Logic type 2 – Particle Swarm Optimization algorithm which was compared with Fuzzy Logic type 2 – Modified Particle Swarm Optimization and Fuzzy Logic type 2 – Dynamic Particle Swarm Optimization. Based on the experiments that had been carried out in each environment, it was found that Fuzzy Logic type 2 - Modified Particle Swarm Optimization had better iteration, time and resource and also smoother robot movement than Fuzzy Logic type 2 – Particle Swarm Optimization and Fuzzy Logic Type 2 - Dynamic Particle Swarm Optimization.

Dermatitis Atopic and Psoriasis Skin Disease Classification by using Convolutional Neural Network Dwi Mei Rita Sari; Siti Nurmaini; Dian Palupi Rini; Ade Iriani Sapitri
Computer Engineering and Applications Journal Vol 12 No 1 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v12i1.419

Skin is the one of the body parts that play a large role in human physical body. There are so many functions of the skin such as offering protection against fungal infection, bacteria, allergy, viruses and controls the temperature of the body. But, the reported shown that the skin disease is the most common disease in humans among all age groups and a significant root of infection. The diagnosis of skin diseases involves several tests. Due to this, the diagnosis process is seen to be intensely laborious, time-consuming and requires an extensive understanding aspecially for the skin disease that have similar symptoms. Two skin diseases that have similar symptoms and most misdiagnosed are atopic dermatitis and psoriasis. Convolutional Neural Network for image processing and classifying have been developed for more accurate classification of skin diseases with different architectures. However, the accuracy in determining skin lesions using CNNs is on the average level. The factors that affect the accuracy result of a CNN is the depth where gradients vanished as the network goes deeper. Another factor is the variance in the training set which means the need of the large size of training set. Hence, in this study we tried 10 CNN architecture to get the best result for classifying dermatitis atopic and psoriasis. These are VGG 16, VGG 19, ResNet 50, ResNet 101, MobileNet, MobileNet V2, DenseNet 121, DenseNet 201, Inception and Xception. Experimental result shown that the inception V3 architecture give the best result with accuracy for data testing 84%, accuracy for unseen data 82% and confusion matrix with True positive obtained is 248, True Negative is 61, False positive is 54 and False Negative 298.

Forecasting Of Intensive Care Unit Patient Heart Rate Using Long Short-Term Memory Firdaus Firdaus; Muhammad Fachrurrozi; Siti Nurmaini; Bambang Tutuko; Muhammad Naufal Rachmatullah; Annisa Darmawahyuni; Ade Iriani Sapitri; Anggun Islami; Masayu Nadila Maharani; Bayu Wijaya Putra
Computer Engineering and Applications Journal Vol 12 No 3 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v12i3.457

Cardiac arrest remains a critical concern in Intensive Care Units (ICUs), with alarmingly low survival rates. Early prediction of cardiac arrest is challenging due to the complexity of patient data and the temporal nature of ICU care. To address this challenge, we explore the use of Deep Learning (DL) models, specifically Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU), for forecasting ICU patient heart rates. We utilize a dataset extracted from the MIMIC III database, which poses the typical challenges of irregular time series data and missing values. Our research encompasses a comprehensive methodology, including data preprocessing, model development, and performance evaluation. Data preprocessing involves regularizing and imputing missing values, as well as data normalization. The dataset is partitioned into training, testing, and validation sets to facilitate model training and evaluation. Fine-tuning of hyperparameters is conducted to optimize each DL architecture's performance. Our results reveal that the GRU architecture consistently outperforms LSTM and BiLSTM in predicting heart rates, achieving the lowest RMSE and MAE values. The findings underscore the potential of DL models, particularly GRU, in enhancing the early detection of cardiac events in ICU patients.

Co-Authors A. Darmawahyuni A. I. Sapitri Ade Iriani Sapitri Ade Iriani Sapitri Ade Iriani Sapitri Ade Silvia Ade Silvia Ade Silvia Handayani Aditya Aditya Aditya, Aditya Agung Juli Anda Agus Triadi Agus Triadi Agus Triadi Ahmad Zarkasi Ahmad Zarkasi Ahmad Zarkasi Ahmad Zarkasih Akhiar Wista Arum Andre Herviant Juliano Anggun Islami Anggun Islami Annisa Darmawahyuni Ardy Hidayat Arief Cahyo Utomo Armansyah, Risky Arnaldo, Muhammad Arum, Akhiar Wista Aulia Rahman Thoharsin B. Tutuko Bambang Tutuko Bambang Tutuko Bayu Wijaya Putra Benedictus Wicaksono Widodo Bhakti Yudho Suprapto Bhakti Yudho Suprapto Bhakti Yudho Suprapto Cindy Kesty Darmawahyuni, Annisa Darmawahyuni, Annisa Deris Stiawan Dewi, Kemala Dewi, Tresna Dian Palupi Rini Dian Palupi Rini Dian Palupi Rini Dimas Budianto Dinda Lestarini Dodo Zaenal Abidin Dwi Mei Rita Sari Ekawati Prihatini Erliza Yuniarti Fachrudin Abdau Fahreza, Irvan Falah Yuridho Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus, Firdaus Firsandaya Malik, Reza Ganesha Ogi GITA FADILA FITRIANA Hadipurnawan Satria Hanif Habibie Supriansyah Huda Ubaya Huda Ubaya Huda Ubaya Husnawati Husnawati Husnawati Husnawati Husnawati Husni, Nyayu Latifah Husni, Nyayu Latifah Irfannuddin Irfannuddin Irsyadi Yani Irvan Fahreza Iryadi Yani Iryadi Yani, Iryadi Isdwanta, Rendy Islami, Anggun Jasmir Jasmir Jasmir Jasmir Jordan Marcelino Kemala Dewi Khairunnisa, Cholidah Zuhroh Krisna Murti Kurniawan, Anggy Tias Kurniawan, Anggy Tyas Legiran Legiran M. Hashim, Siti Zaiton M. N. Rachmatullah M. Naufal Rachmatullah Maharani, Masayu Nadila Marcelino, Jordan Masayu Nadila Maharani Mira Afrina Muhamad Akbar Muhammad Afif Muhammad Anshori Muhammad Arnaldo Muhammad Fachrurrozi Muhammad Fachrurrozi Muhammad Irham Rizki Fauzi Muhammad Naufal Rachmatullah Muhammad Naufal, Muhammad Muhammad Roriz Muhammad Taufik Roseno, Muhammad Taufik Muzakkie, Mufida Nabilah, Aini Nadia Ayu Oktabella, nadia ayu oktabella Novi Yusliani Nurqolbiah, Fatihani Nuswil Bernolian Nuswil Bernolian Nyayu Latifah Husni Nyayu Latifah Husni, Nyayu Latifah Oky Budiyarti Osvari Arsalan Passa, Rahma Satila Patiyus Agustiansyah PATIYUS AGUSTIANSYAH, PATIYUS Pola Risma PP Aditya, PP, Aditya, PP Pratama, Jimiria Putri Mirani Rachmamtullah, Muhammad Naufal Radiyati Umi Partan Radiyati Umi Partan Radiyati Umi Partan Radiyati Umi Partan, Radiyati Umi Rahma Satila Passa Rendy Isdwanta Renny Amalia Pratiwi Reza Firsandaya Malik Reza Firsandaya Malik Ria Nova Ricy Firnando Ricy Firnando Ricy Firnando Rizal Sanif Rizki Kurniati Rossi Passarella Sahat Pangidoan Samsuryadi Samsuryadi Saparudin Saparudin Saparudin, Saparudin Sapitri, Ade Iriani Saputra, Tommy Sari, Dwi Mei Rita Sarifah Putri Raflesia Sarifah Putri Raflesia, Sarifah Putri Sastradinata, Irawan Sigit Prasetyo Noprianto Siti Zaiton Siti Zaiton M. Hashim Soedjana, Hardi Siswo Sri Desy Siswanti Suci Dwi Lestari Suci Dwi Lestari Suhandono, Nugroho Sukemi Sukemi Sukemi Sukemi Sukemi Sukman Tulus Putra Sutarno Sutarno Syamsul Arifin Syaputra, Hadi Tio Artha Nugraha Tresna Dewi Tresna Dewi Tri Undari Triadi, Agus Triadi, Agus Varindo Ockta Keneddi Putra Velia Yuliza Winda Kurnia Sari Wisnu Adi Putra Yani, Iryadi Yesi Novaria Kunang Yurni Oktarina Zaqqi Yamani

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Journal : ComEngApp : Computer Engineering and Applications Journal