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TeleOTIVA: Advanced AI-Powered Automated Screening System for Early Detection of Precancerous Lesions Rachmamtullah, Muhammad Naufal; Nurmaini, Siti; Agustiansyah, Patiyus; Sanif, Rizal; Sastradinata, Irawan; Arum, Akhiar Wista; Firdaus, Firdaus; Darmawahyuni, Annisa; Tutuko, Bambang; Sapitri, Ade Iriani; Islami, Anggun
Computer Engineering and Applications Journal Vol 14 No 1 (2025)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v14i1.1197

In 2023, the Indonesian Ministry of Health launched the Rencana Aksi Nasional (RAN) to enhance the detection and management of cervical cancer in Indonesia. One of the main pillars in this movement is the implementation of early screening for precancerous lesions aimed at identifying and treating these lesions before they develop into cervical cancer. This effort includes improving public access to healthcare services, providing education and awareness about the importance of early detection, and utilizing the latest technology in screening procedures. It is hoped that, through these targeted and effective interventions, the incidence of cervical cancer can be significantly reduced. This research aims to facilitate the early detection screening process for cervical precancerous lesions, particularly in difficult areas for medical experts to reach. This study also seeks to assist obstetricians and gynecologists in detecting precancerous lesions automatically, quickly, and accurately. By developing an advanced technology-based screening system, it is hoped that early detection of precancerous lesions can be carried out more efficiently, thereby increasing the chances of timely treatment and reducing the incidence of cervical cancer across various regions in Indonesia. This system is designed to provide reliable and user-friendly diagnostic support as it is developed on a mobile platform that can be accessed anytime and anywhere. This research developed a system for early screening called Tele-OTIVA. The Tele-OTIVA application system is an advanced platform that uses artificial intelligence (AI) based approaches to provide optimal services in early detection of precancerous lesions. This application is designed for mobile, allowing users to access and use its advanced features anytime and anywhere. With the integration of AI technology, Tele-OTIVA can detect and analyze cervical precancerous lesions accurately and quickly to provide accurate and efficient screening results. The Tele-OTIVA application system is capable of providing satisfactory detection results. The performance of the proposed model achieves accuracy, sensitivity, and specificity levels above 90%. With this high performance, Tele-OTIVA ensures that the detection of precancerous lesions is carried out with high reliability and precision, instilling greater confidence in healthcare professionals and users during the screening and diagnosis process. The implementation of our application model offers numerous advantages over traditional methods. It significantly enhances efficiency by automating processes, reduces human error through rigorous error-checking mechanisms, and accelerates the processing of large datasets. These improvements streamline operations and ensure more reliable and rapid data analysis.

Analyzing Co-Authorship Networks in Indonesian PTN-BH Institution Through Social Network Analysis Firdaus, Firdaus; Nurmaini, Siti; Darmawahyuni, Annisa; Rachmatullah, Muhammad Naufal; Raflesia, Sarifah Putri; Lestarini, Dinda
Computer Engineering and Applications Journal Vol 14 No 1 (2025)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v14i1.1265

This study involved an examination of bibliographic information from Indonesia. Our approach centered on utilizing social network analysis to explore the co-authorship relationships among Indonesian authors, focused on the co-authorship network within the context of authors affiliated with Indonesian state universities known as "PTN-BH," which specialize in higher education and legal studies. To conduct our analysis, we gathered publication data from the Scopus database, spanning a time frame from 1948 to 2020. The primary methodology entailed constructing a graph composed of nodes and edges, representing the co-authorship connections among these authors. By employing the Louvain method, we were able to identify prominent communities within this graph. We carried out a comprehensive analysis at both macro and micro levels, involving measurement techniques tailored to these perspectives. Through this approach, we revealed and examined the collaboration patterns among authors associated with PTN-BH institutions, as illuminated by the co-authorship network analysis.

Coronary Heart Disease Interpretation Based on Deep Neural Network Darmawahyuni, Annisa
Computer Engineering and Applications Journal Vol 8 No 1 (2019)
Publisher : Universitas Sriwijaya

Coronary heart disease (CHD) population increases every year with a significant number of deaths. Moreover, the mortality from coronary heart disease gets the highest prevalence in Indonesia at 1.5 percent. The misdiagnosis of coronary heart disease is a crucial fundamental that is the major factor that caused death. To prevent misdiagnosis of CHD, an intelligent system has been designed. This paper proposed a simulation which can be used to diagnose the coronary heart disease in better performance than the traditional diagnostic methods. Some researches have developed a system using conventional neural network or other machine learning algorithm, but the results are not a good performance. Based on a conventional neural network, deeper neural network (DNN) is proposed to our model in this work. As known as, the neural network is a supervised learning algorithm that good in the classification task. In DNN model, the implementation of binary classification was implemented to diagnose CHD present (representative “1”) or CHD absent (representative “0”). To help performance analysis using the UCI machine learning repository heart disease dataset, ROC Curve and its confusion matrix were implemented in this work. The overall predictive accuracy, sensitivity, and specificity acquired was 96%, 99%, 92%, respectively.

Analyzing Co-Authorship Networks in Indonesian PTN-BH Institution Through Social Network Analysis Firdaus, Firdaus; Nurmaini, Siti; Kurniawan, Anggy Tyas; Darmawahyuni, Annisa; Naufal, Muhammad; Raflesia, Sarifah Putri; Lestarini, Dinda
Computer Engineering and Applications Journal Vol 14 No 1 (2025)
Publisher : Universitas Sriwijaya

This study involved an examination of bibliographic information from Indonesia. Our approach centered on utilizing social network analysis to explore the co-authorship relationships among Indonesian authors, focused on the co-authorship network within the context of authors affiliated with Indonesian state universities known as "PTN-BH," which specialize in higher education and legal studies. To conduct our analysis, we gathered publication data from the Scopus database, spanning a time frame from 1948 to 2020. The primary methodology entailed constructing a graph composed of nodes and edges, representing the co-authorship connections among these authors. By employing the Louvain method, we were able to identify prominent communities within this graph. We carried out a comprehensive analysis at both macro and micro levels, involving measurement techniques tailored to these perspectives. Through this approach, we revealed and examined the collaboration patterns among authors associated with PTN-BH institutions, as illuminated by the co-authorship network analysis.

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

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

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.

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

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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 ShortTerm 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.

TeleOTIVA: Advanced AI-Powered Automated Screening System for Early Detection of Precancerous Lesions Rachmatullah, Muhammad Naufal; Nurmaini, Siti; Agustiansyah, Patiyus; Sastradinata, Irawan; Arum, Akhiar Wista; Firdaus; Darmawahyuni, Annisa; Tutuko, Bambang; Sapitri, Ade Iriani; Islami, Anggun
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 1 (2025)
Publisher : Universitas Sriwijaya

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

In 2023, the Indonesian Ministry of Health launched the Rencana Aksi Nasional (RAN) to enhance the detection and management of cervical cancer in Indonesia. One of the main pillars in this movement is the implementation of early screening for precancerous lesions aimed at identifying and treating these lesions before they develop into cervical cancer. This effort includes improving public access to healthcare services, providing education and awareness about the importance of early detection, and utilizing the latest technology in screening procedures. It is hoped that, through these targeted and effective interventions, the incidence of cervical cancer can be significantly reduced. This research aims to facilitate the early detection screening process for cervical precancerous lesions, particularly in difficult areas for medical experts to reach. This study also seeks to assist obstetricians and gynecologists in detecting precancerous lesions automatically, quickly, and accurately. By developing an advanced technology-based screening system, it is hoped that early detection of precancerous lesions can be carried out more efficiently, thereby increasing the chances of timely treatment and reducing the incidence of cervical cancer across various regions in Indonesia. This system is designed to provide reliable and user-friendly diagnostic support as it is developed on a mobile platform that can be accessed anytime and anywhere. This research developed a system for early screening called TeleOTIVA. The TeleOTIVA application system is an advanced platform that uses artificial intelligence (AI) based approaches to provide optimal services in early detection of precancerous lesions. This application is designed for mobile, allowing users to access and use its advanced features anytime and anywhere. With the integration of AI technology, TeleOTIVA can detect and analyze cervical precancerous lesions accurately and quickly to provide accurate and efficient screening results. The TeleOTIVA application system is capable of providing satisfactory detection results. The performance of the proposed model achieves accuracy, sensitivity, and specificity levels above 90%. With this high performance, TeleOTIVA ensures that the detection of precancerous lesions is carried out with high reliability and precision, instilling greater confidence in healthcare professionals and users during the screening and diagnosis process. The implementation of our application model offers numerous advantages over traditional methods. It significantly enhances efficiency by automating processes, reduces human error through rigorous error-checking mechanisms, and accelerates the processing of large datasets. These improvements streamline operations and ensure more reliable and rapid data analysis.

Analyzing Co-Authorship Networks in Indonesian PTN-BH Institution Through Social Network Analysis Firdaus; Nurmaini, Siti; Kurniawan, Anggy Tias; Darmawahyuni, Annisa; Rachmatullah, Muhammad Naufal; Raflesia, Sarifah Putri; Lestarini, Dinda
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 1 (2025)
Publisher : Universitas Sriwijaya

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

This study involved an examination of bibliographic information from Indonesia. Our approach centered on utilizing social network analysis to explore the co-authorship relationships among Indonesian authors, focused on the co-authorship network within the context of authors affiliated with Indonesian state universities known as "PTN-BH," which specialize in higher education and legal studies. To conduct our analysis, we gathered publication data from the Scopus database, spanning a time frame from 1948 to 2020. The primary methodology entailed constructing a graph composed of nodes and edges, representing the co-authorship connections among these authors. By employing the Louvain method, we were able to identify prominent communities within this graph. We carried out a comprehensive analysis at both macro and micro levels, involving measurement techniques tailored to these perspectives. Through this approach, we revealed and examined the collaboration patterns among authors associated with PTN-BH institutions, as illuminated by the co-authorship network analysis.

Exploration U-Net Architecture for Cervical Precancerous Lesions Segmentation Arum, Akhiar Wista; Rachmatullah, Muhammad Naufal; Tutuko, Bambang; Firdaus; Darmawahyuni, Annisa; Sapitri, Ade Iriani; Islami, Anggun; Ananda, Dea Agustria
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 2 (2025)
Publisher : Universitas Sriwijaya

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The automatic analysis of images for the early detection of cervical cancer relies on the segmentation of cervical precancerous lesions. This paper investigates the incorporation of various CNN-based backbones into a U-Net model for improved segmentation accuracy. A set of twelve backbones was tested, including VGG16, VGG19, ResNet50, ResNext50, EfficientNetB7, InceptionResNetv2, DenseNet201, InceptionV3, MobileNet V2, SE-ResNet50, SE-ResNext50, and SE-Net154. Evaluation metrics were computed using Intersection over Union, pixel accuracy, and Dice coefficient. The findings demonstrate that U-Net with EfficientNetB7 backbone outperforms all other models with an IoU of 73.13%, pixel accuracy of 89.92%, and a Dice coefficient of 77.64%. These results were visually confirmed; segmentation outputs were examined, showing accurate delineation of lesion borders. The dominating performance of EfficientNetB7 was observed to be due to high feature extraction efficiency coupled with powerful spatial information representation. The study is, however, limited by a lack of clinical validation and expert evaluation from trained medical personnel. The results demonstrate the effectiveness of combining the U-Net architecture with advanced CNN backbones towards designing automated systems to analyze medical images.

Deep Learning for ECG-Based Arrhythmia Classification Based on Time-Domain Features Sari, Ririn Purnama; Darmawahyuni, Annisa; Tutuko, Bambang; Firdaus; Rachmatullah, Muhammad Naufal; Sapitri, Ade Iriani; Islami, Anggun; Arum, Akhiar Wista
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 2 (2025)
Publisher : Universitas Sriwijaya

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

Arrhythmia is a disturbance in the electrical activity of the heart that can affect the rhythm and duration of the heartbeat. Early detection of arrhythmia is crucial to prevent more serious complications. Electrocardiogram (ECG) is an effective non-invasive diagnostic tool in detecting arrhythmia, but manual detection by experts takes time. To overcome this limitation, this research develops an arrhythmia classification system by utilizing deep learning. This study involves a series of stages, starting from pre-processing, feature extraction, and arrhythmia classification models using convolutional neural networks (CNN) and long short-term memory (LSTM). The results showed that feature extraction successfully improved model efficiency and accuracy. Evaluation of model performance using accuracy, recall, precision, specificity, and F1-score metrics showed that the LSTM model achieved 95% accuracy, 96% recall, 96% precision, 99% specificity, and 96% F1-score, outperforming the CNN model which achieved 91% accuracy, 90% recall, 89% precision, 98% specificity, and 89% F1-score. Thus, these results indicate that the LSTM model is superior in arrhythmia classification.

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