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A Deep Learning Approach to Integrate Medical Big Data for Improving Health Services in Indonesia Bambang Tutuko; Siti Nurmaini; Muhammad Naufal Rachmatullah; Annisa Darmawahyuni; Firdaus Firdaus
Computer Engineering and Applications Journal Vol 9 No 1 (2020)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (426.189 KB) | DOI: 10.18495/comengapp.v9i1.328


Medical Informatics to support health services in Indonesia is proposed in this paper. The focuses of paper to the analysis of Big Data for health care purposes with the aim of improving and developing clinical decision support systems (CDSS) or assessing medical data both for quality assurance and accessibility of health services. Electronic health records (EHR) are very rich in medical data sourced from patient. All the data can be aggregated to produce information, which includes medical history details such as, diagnostic tests, medicines and treatment plans, immunization records, allergies, radiological images, multivariate sensors device, laboratories, and test results. All the information will provide a valuable understanding of disease management system. In Indonesia country, with many rural areas with limited doctor it is an important case to investigate. Data mining about large-scale individuals and populations through EHRs can be combined with mobile networks and social media to inform about health and public policy. To support this research, many researchers have been applied the Deep Learning (DL) approach in data-mining problems related to health informatics. However, in practice, the use of DL is still questionable due to achieve optimal performance, relatively large data and resources are needed, given there are other learning algorithms that are relatively fast but produce close performance with fewer resources and parameterization, and have a better interpretability. In this paper, the advantage of Deep Learning to design medical informatics is described, due to such an approach is needed to make a good CDSS of health services.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (425.63 KB) | DOI: 10.18495/comengapp.v9i2.341


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.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (319.534 KB) | DOI: 10.18495/comengapp.v10i3.387


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.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (402.465 KB) | DOI: 10.18495/comengapp.v11i1.398


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.
Paper Clustering Data Bibliografi menggunakan Algoritma DBSCAN dengan Author Matching Classifier Berbasis Deep Neural Network Ricy Firnando; Siti Nurmaini; Sukemi Sukemi; Firdaus Firdaus; Muhammad Naufal Rachmatullah
Jurnal Sistem Informasi Vol 14, No 2 (2022)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/jsi.v14i2.19089


Ambiguitas nama penulis atau author name ambiguity sering kali menjadi masalah yang dapat mempengaruhi kualitas layanan database bibliografi. Untuk mengatasi masalah ambiguitas nama penulis maka diciptakanlah disambiguasi nama penulis atau author name disambiguation. Metode yang digunakan dalam author name disambiguation umumnya menangani masalah ambiguitas nama penulis dengan pendekatan author matching, classification, dan clustering. Beberapa penelitian menggabungkan beberapa pendekatan seperti menggunakan author matching classifier berbasis algoritma random forest untuk pairwise classification dan algoritma DBSCAN sebagai algoritma clustering namun masih belum mendapatkan hasil atau performa yang optimal. Pada penelitian ini dibangun sebuah model author matching classifier berbasis deep neural network yang kemudian diimplementasikan dalam algoritma clustering DBSCAN. Berdasarkan percobaan yang dilakukan menggunakan dataset The Giles, model author matching cassifier berbasis deep neural network yang kami usulkan dapat menghasilkan performa sebesar 95.99% untuk pairwise classification dan 97.23% untuk clustering.
Securing Text File on Audio Files using Least Significant Bit (LSB) and Blowfish Ahmad Rizky Fauzan; Al Farissi; Muhammad Naufal Rachmatullah
Sriwijaya Journal of Informatics and Applications Vol 3, No 2 (2022)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar


Along with the development of technology, communication can be done in various ways, one of which is digital messages. But often the messages sent do not reach their destination and are obtained by irresponsible parties. This happens because of the lack of security in the file. For this reason, security is needed so that messages cannot be stolen or seen by other parties. There are various ways to secure messages, including Steganography and Cryptography techniques. This study uses a combination of the Least Significant Bit method and the Blowfish algorithm to secure secret messages in audio files. This research will measure encryption and decryption time, analysis of message file size changes after encryption and decryption, and PSNR value of audio files. The result of encryption using blowfish is a change in the size of the message file caused by the size of the message file is less than the block cipher size, so additional bytes are given so that the message size matches the block cipher size. The speed of the encryption and decryption process using the blowfish algorithm results in an average time for encryption of 547.98ms while the average time for decryption is 538.19ms. The longest time for the encryption process is 557.30ms and the fastest is 534.50ms, while the longest time for the decryption process is 548.74ms and the fastest is 531.46ms. Hiding messages in audio files using LSB produces PSNR values above 30dB.
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.