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All Journal ComEngApp : Computer Engineering and Applications Journal Perfecting a Video Game with Game Metrics Bulletin of Electrical Engineering and Informatics Annual Research Seminar Proceeding of the Electrical Engineering Computer Science and Informatics Altius: Jurnal Ilmu Olahraga dan Kesehatan Indonesian Journal of Electrical Engineering and Computer Science Jurnal Generic
Firdaus Firdaus
Universitas Sriwijaya
Computer Science & IT Education Electrical & Electronics Engineering Engineering Medicine & Pharmacology Public Health Social Sciences Other

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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

Abstract

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

Abstract

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

Abstract

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.
Convolutional neural network for semantic segmentation of fetal echocardiography based on four-chamber view M. N. Rachmatullah; Siti Nurmaini; A. I. Sapitri; A. Darmawahyuni; B. Tutuko; Firdaus Firdaus
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.3060

Abstract

The acute shortage of trained and experienced sonographers causes the detection of congenital heart defects (CHDs) extremely difficult. In order to minimize this difficulty, an accurate fetal heart segmentation to the early location of such structural heart abnormalities prior to delivery is essential. However, the segmentation process is not an easy task due to the small size of the fetal heart structure. Moreover, the manual task for identifying the standard cardiac planes, primarily based on a four-chamber view, requires a well-trained clinician and experience. In this paper, a CNN method using U-Net architecture was proposed to automate fetal cardiac standard planes segmentation from ultrasound images. A total of 519 fetal cardiac images was obtained from three videos. All data is divided into training and testing data. The testing data consist of 106 slices of the four-chamber segmentation tasks, i.e. atrial septal defect (ASD), ventricular septal defect (VSD), and normal. The segmentation of the post-processing method is needed to enhanced the segmentation result. In this paper, a combination technique with U-Net and Otsu thresholding gives the best performances with 99.48%-pixel accuracy, 96.73% mean accuracy, 94.92% mean intersection over union, and 0.21% segmentation error. In the future, the implementation of Deep Learning in the study of CHDs holds significant potential for identifying novel CHDs in heterogeneous fetal hearts.
Coronary Heart Disease Interpretation Based on Deep Neural Network Annisa Darmawahyuni; Siti Nurmaini; Firdaus Firdaus
Computer Engineering and Applications Journal Vol 8 No 1 (2019)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (484.945 KB) | DOI: 10.18495/comengapp.v8i1.288

Abstract

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.
Pelatihan Pengenalan Aplikasi Robotika pada Siswa SMP Negeri 1 Palembang Bambang Tutuko; Firdaus Firdaus; Ahmad Zarkasi
Annual Research Seminar (ARS) Vol 4, No 2 (2018): Special Issue : Pengabdian Kepada Masyarakat
Publisher : Annual Research Seminar (ARS)

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Abstract

Pelaksanaan pengabdian kepada masyarakat ni menjelaskan tentang hasil pelatihan perakitan robot Halang Rintang (avoider) sebagai media pengenalan bidang robotika bagi siswa sekolah menengah pertama Negeri 1 Palembang. Pelatihan ini diikuti oleh 20 orang siswa dari utusan sekolah menengah pertama Negeri 1 Palembang. Pelatihan ini akan dititikberatkan pada bagaiamana mensetting perangkat dan membuat program aplikasi robot beroda dan sensor infra merah sebagai pendeteksi. Infra merah akan diatur jaraknya sesuai dengan materi yang diajarkan. Keluran sensor akan menjadi penggerak untuk sistem aktuator. Motor akan digerakkan sesuai dengan data referensi sensor, yang telah diprogram dalam mikrokontroler. Robot halang rintang menggunakan board arduino uno, dengan mikrokontroler Atmega32 sebagai pengendali keseluruhan sistem. Hasil yang diperoleh sesuai dengan yang diharapkan, dengan tingkat pemahaman siswa setelah post test diatas 80%
Penerapan Metode Pelatihan Langsung (Doing by Learning) untuk Siswa SMA di Kecamatan Ilir Timur 1 Palembang Siti Nurmaini; Huda Ubaya; Bambang Tutuko; Firdaus Firdaus; Ahmad Zarkasi
Annual Research Seminar (ARS) Vol 2, No 2 (2016): Special Issue : Penelitian, Pengabdian Masyarakat
Publisher : Annual Research Seminar (ARS)

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Abstract

Robot halang rintang (avoider)  sebagai media pengenalan bidang robotika bagi bagi sekolah menengah atas Muhammadiyah 1 Palembang. Pelatihan ini akan dititik beratkan pada bagaiamana merakit dan membuat suatu aplikasi robot sederhana yang dilengkapi dengan sensor infra merah, kendali motor yang pemrogramannya menggunakan bahasa pemrograman C. Metode kegiatan yang digunakan dalam kegiatan ini yaitu metode pelatihan langsung (learning by doing) berupa pemaparan/presentasi, tutorial serta diskusi tentang bidang ilmu robotika, teknik disain sedernaha sebuah robot dan menjalankan aplikasi robot Halang Rintang (avoider) yang telah ditentukan. Sebelumnya diadakan pre test yang berupa tanya jawab, untuk mengetahui sejauh mana pengetahuan peserta sebelum mengikuti pelatihan dan praktek yang menitikberatkan pada implementasi robot Halang Rintang (avoider).  Post test dilakukan dengan menjalankan aplikasi yang telah dibuat oleh siswa pada halangan yang telah disiapkan . Untuk mengetahui tingkat penyerapan terhadap materi pelatihan yang diberikan oleh para pengajar atau pelatih dan bimbingan konsultasi, robot yang telah dibuat harus bekerja dengan baik sesuai dengan arahan yang diberikan.
Information Framework of Pervasive Real Time Monitoring System: Case of Peat Land Forest Fires and Air Quality in South Sumatera, Indonesia Siti Nurmaini; Reza Firsandaya Malik; Deris Stiawan; Firdaus Firdaus; Saparudin Saparudin
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 3: EECSI 2016
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (716.552 KB) | DOI: 10.11591/eecsi.v3.1163

Abstract

The information framework aims to holistically address the problems and issues posed by unwanted peat and land fires within the context of the natural environment and socio-economic systems. Informed decisions on planning and allocation of resources can only be made by understanding the landscape. Therefore, information on fire history and air quality impacts must be collected for future analysis. This paper proposes strategic framework based on technology approach with data fusion strategy to produce the data analysis about peat land fires and air quality management in in South Sumatera. The research framework should use the knowledge, experience and data from the previous fire seasons to review, improve and refine the strategies and monitor their effectiveness for the next fire season. Communicating effectively with communities and the public and private sectors in remote and rural landscapes is important, by using smartphones and mobile applications. Tools such as one-stop information based on web applications, to obtain information such as early warning to send and receive fire alerts, could be developed and promoted so that all stakeholders can share important information with each other.
Implementasi Reputation System pada Matangdipohon: E-Marketplace Produk Pertanian dengan Metode Electre Nurracmah Hakim Puar; Firdaus Firdaus
Generic Vol 10 No 1 (2018): Vol 10, No 1 (2018)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

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Abstract

Masalah kepercayaan adalah hal terpenting dalam E-Marketplace, oleh karena itu dibutuhkan Reputation System (Sistem Reputasi). Reputasi sistem itu sendiri merupakan sistem yang dibangun untuk menentukan reputasi user (penjual/pembeli) dasar aktifitas user tersebut. Sistem ini dirancang sedemikian rupa untuk mengurangi praktik penipuan online karena kurangnya informasi apakah user/member E-Marketplace tersebut benar- benar dapat dipercaya. Dengan adanya suatu reputasi maka penjual atau pembeli dapat mempertimbangkan apakah ingin tetap melanjutkan transaksi atau membatalkannya. dasar dari permasalahan yang telah diuraikan di atas, maka penulis bermaksud untuk melakukan analisis dan pengembangan lebih lanjut dari penelitian sebelumnya dimana fokus utama dari penelitian yang akan dilakukan adalah membangun reputation system.
Forecasting Harga Saham dengan Jaringan Saraf Tiruan Rina Yuniarti; Julian Supardi; Firdaus Firdaus
Generic Vol 10 No 1 (2018): Vol 10, No 1 (2018)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

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Abstract

Forecasting atau peramalan diperlukan untuk menetapkan kapan suatu peristiwa akan terjadi sehingga dapat dilakukan tindakan yang tepat. Proses forecasting dimanfaatkan untuk berbagai aspek kehidupan, salah satunya adalah forecasting harga saham. Forecasting harga saham dapat dilakukan dengan algoritma pada jaringan saraf tiruan, yaitu algoritma backpropagation. Pada forecasting dengan jaringan saraf tiruan dibentuk suatu pemodelan jaringan yang dapat memproses data masukan. Pada penelitian ini digunakan suatu pemodelan yang menganalisis faktor-faktor yang mempengaruhi harga saham dan perubahan nya. Faktor-faktor tersebut adalah faktor teknikal, faktor makro ekonomi dan faktor fundamental. Dengan menggunakan faktor-faktor tersebut pada pemodelan jaringan saraf tiruan dihasilkan suatu hasil forecasting yang cukup baik dan akurat, yaitu dengan kekuratan 98,90%. untuk nilai harga saham, 87,65% untuk perubahan harga saham dan 73,20% untuk selisih perubahan harga saham
Co-Authors A. Darmawahyuni A. I. Sapitri Ade Iriani Sapitri Ahmad Zarkasi Andre Herviant Juliano Anggun Islami Annisa Darmawahyuni B. Tutuko Bambang Tutuko Bambang Tutuko Bayu Wijaya Putra Burman Bagaskara Caesar Rizky Kurniawan Darmawahyuni, Annisa Deris Stiawan Desty Rodiah Dinda Lestarini Eni Triningsih Hanif Habibie Supriansyah Huda Ubaya Julian Supardi M. N. Rachmatullah Masayu Nadila Maharani Muhammad Fachrurrozi Muhammad Irham Rizki Fauzi Muhammad Naufal Rachmatullah Muhammad Naufal Rachmatullah Nurracmah Hakim Puar Reza Firsandaya Malik Rina Yuniarti Rudy Noor Muktamar Saparudin Saparudin Sarifah Putri Raflesia Siti Nurmaini Sulastri Tio Artha Nugraha Varindo Ockta Keneddi Putra Yuliani .