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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Indonesian Journal of Electrical Engineering and Computer Science Sriwijaya Journal of Informatics and Applications Jurnal Mandiri IT

Muhammad Naufal Rachmatullah

Universitas Sriwijaya

Author-ID : 2406579

Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Other

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Deep ensemble learning for skin lesions classification with convolutional neural network Renny Amalia Pratiwi; Siti Nurmaini; Dian Palupi Rini; Muhammad Naufal Rachmatullah; Annisa Darmawahyuni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i3.pp563-570

One type of skin cancer that is considered a malignant tumor is melanoma. Such a dangerous disease can cause a lot of death in the world. The early detection of skin lesions becomes an important task in the diagnosis of skin cancer. Recently, a machine learning paradigm emerged known as deep learning (DL) utilized for skin lesions classification. However, in some previous studies by using seven class images diagnostic of skin lesions classification based on a single DL approach with CNNs architecture does not produce a satisfying performance. The DL approach allows the development of a medical image analysis system for improving performance, such as the deep convolutional neural networks (DCNNs) method. In this study, we propose an ensemble learning approach that combines three DCNNs architectures such as Inception V3, Inception ResNet V2 and DenseNet 201 for improving the performance in terms of accuracy, sensitivity, specificity, precision, and F1-score. Seven classes of dermoscopy image categories of skin lesions are utilized with 10015 dermoscopy images from well-known the HAM10000 dataset. The proposed model produces good classification performance with 97.23% accuracy, 90.12% sensitivity, 97.73% specificity, 82.01% precision, and 85.01% F1-Score. This method gives promising results in classifying skin lesions for cancer diagnosis.

Unidirectional-bidirectional recurrent networks for cardiac disorders classification Annisa Darmawahyuni; Siti Nurmaini; Muhammad Naufal Rachmatullah; Firdaus Firdaus; Bambang Tutuko
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 3: June 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i3.18876

The deep learning approach of supervised recurrent network classifiers model, i.e., recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent units (GRUs) are used in this study. The unidirectional and bidirectional for each cardiac disorder (CDs) class is also compared. Comparing both phases is needed to figure out the optimum phase and the best model performance for ECG using the Physionet dataset to classify five classes of CDs with 15 leads ECG signals. The result shows that the bidirectional RNNs method produces better results than the unidirectional method. In contrast to RNNs, the unidirectional LSTM and GRU outperformed the bidirectional phase. The best recurrent network classifier performance is unidirectional GRU with average accuracy, sensitivity, specificity, precision, and F1-score of 98.50%, 95.54%, 98.42%, 89.93% 92.31%, respectively. Overall, deep learning is a promising improved method for ECG classification.

Neural network technique with deep structure for improving author homonym and synonym classification in digital libraries Firdaus Firdaus; Siti Nurmaini; Varindo Ockta Keneddi Putra; Annisa Darmawahyuni; Reza Firsandaya Malik; Muhammad Naufal Rachmatullah; Andre Herviant Juliano; Tio Artha Nugraha
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 4: August 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i4.18878

Author name disambiguation (AND), also recognized as name-identification, has long been seen as a challenging issue in bibliographic data. In other words, the same author may appear under separate names, synonyms, or distinct authors may have similar to those referred to as homonyms. Some previous research has proposed AND problem. To the best of our knowledge, no study discussed specifically synonym and homonym, whereas such cases are the core in AND topic. This paper presents the classification of non-homonym-synonym, homonym-synonym, synonym, and homonym cases by using the DBLP computer science bibliography dataset. Based on the DBLP raw data, the classification process is proposed by using deep neural networks (DNNs). In the classification process, the DBLP raw data divided into five features, including name, author, title, venue, and year. Twelve scenarios are designed with a different structure to validate and select the best model of DNNs. Furthermore, this paper is also compared DNNs with other classifiers, such as support vector machine (SVM) and decision tree. The results show DNNs outperform SVM and decision tree methods in all performance metrics. The DNNs performances with three hidden layers as the best model, achieve accuracy, sensitivity, specificity, precision, and F1-score are 98.85%, 95.95%, 99.26%, 94.80%, and 95.36%, respectively. In the future, DNNs are more performing with the automated feature representation in AND processing.

Author identification in bibliographic data using deep neural networks Firdaus Firdaus; Siti Nurmaini; Reza Firsandaya Malik; Annisa Darmawahyuni; Muhammad Naufal Rachmatullah; Andre Herviant Juliano; Tio Artha Nugraha; Varindo Ockta Keneddi Putra
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 3: June 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i3.18877

Author name disambiguation (AND) is a challenging task for scholars who mine bibliographic information for scientific knowledge. A constructive approach for resolving name ambiguity is to use computer algorithms to identify author names. Some algorithm-based disambiguation methods have been developed by computer and data scientists. Among them, supervised machine learning has been stated to produce decent to very accurate disambiguation results. This paper presents a combination of principal component analysis (PCA) as a feature reduction and deep neural networks (DNNs), as a supervised algorithm for classifying AND problems. The raw data is grouped into four classes, i.e., synonyms, homonyms, homonyms-synonyms, and non-homonyms-synonyms classification. We have taken into account several hyperparameters tuning, such as learning rate, batch size, number of the neuron and hidden units, and analyzed their impact on the accuracy of results. To the best of our knowledge, there are no previous studies with such a scheme. The proposed DNNs are validated with other ML techniques such as Naïve Bayes, random forest (RF), and support vector machine (SVM) to produce a good classifier. By exploring the result in all data, our proposed DNNs classifier has an outperformed other ML technique, with accuracy, precision, recall, and F1-score, which is 99.98%, 97.98%, 97.86%, and 99.99%, respectively. In the future, this approach can be easily extended to any dataset and any bibliographic records provider.

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

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

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.

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.

Co-Authors Abdurahman Ade Iriani Sapitri Ade Iriani Sapitri Ahmad Rifai Ahmad Rizky Fauzan Akhiar Wista Arum Akhtiar W Arum Al Farissi Ananda, Dea Agustria Andre Herviant Juliano Anggun Islami Anggun Islami Anita Desiani Annisa Darmawahyuni Annisa Darmawahyuni Armansyah, Risky Arnaldo, Muhammad Arum, Akhiar Wista Bambang Tutuko Bambang Tutuko Bambang Tutuko Bayu Wijaya Putra Darmawahyuni, Annisa Darmawahyuni, Annisa Desty Rodiah Dewi Chayanti Dian Palupi Rini Dian Palupi Rini Dinda Lestarini Dite Geovanni Erwin, Erwin Fadel Muhammad, Fadel Fahreza, Irvan Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Hadipurnawan Satria Hanif Habibie Supriansyah Irvan Fahreza Islami, Anggun Kurniawan, Anggy Tias M. Fachrurrozi . Maharani, Masayu Nadila Masayu Nadila Maharani Mira Afrina Muhammad Akmal Shidqi Muhammad Arnaldo Muhammad Fachrurrozi Muhammad Fachrurrozi Muhammad Gibran Al-Filambany Muhammad Irham Rizki Fauzi Muhammad Taufik Roseno, Muhammad Taufik Novi Yusliani Patiyus Agustiansyah PATIYUS AGUSTIANSYAH, PATIYUS Putri Mirani Rahmat Fadli Isnanto Raihan Mufid Setiadi Raihan Mufid Setiadi Renny Amalia Pratiwi Reza Firsandaya Malik Ricy Firnando Ricy Firnando Rossi Passarella Samsuryadi Samsuryadi Sapitri, Ade Iriani Saputra, Tommy Sari, Ririn Purnama Sarifah Putri Raflesia, Sarifah Putri Sastradinata, Irawan Setiadi, Raihan Mufid Siti Nurmaini Sri Indra Maiyanti Suci Dwi Lestari Suci Dwi Lestari Sugandi Yahdin Sukemi Sukemi Sukemi Sutarno Sutarno Sutarno Syaputra, Hadi Tio Artha Nugraha Varindo Ockta Keneddi Putra Yesi Novaria Kunang

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