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All Journal 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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Nutrition Therapy System untuk Penderita Diabetes Melitus 2 Burman Bagaskara; Firdaus Firdaus; Eni Triningsih
Generic Vol 11 No 1 (2019): Vol 11, No 1 (2019)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

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Abstract

Diabetes Militus tipe 2 merupakan penyakit yang di sebabkan oleh hormon insulin yang tidak berkerja dengan normal sehingga gula di dalam darah menjadi tidak stabil. Penyebab utama dari penyakit ini adalah pola hidup yang tidak sehat yang di ikuti dengan cara makan yang tidak teratur dan tidak sesuai kebutuhan nutrisi tubuh. Untuk mencegah atau mengurangi dari gejala penyakit ini dapat di lakukan dengan cara mengetahui kebutuhan nutrisi yang di butuhkan tubuh dan mampu mencatat segala aktifitas konsumsi nutrisi setiap harinya. Tentunya hal tersebut sangat sulit untuk di terapkan apa bila tidak ada sistem yang mampu mengelolanya. Dan juga untuk menambah preferensi makanan di butuhkan data saran makanan yang cocok untuk di konsumsi sesuai batas kebutuhan nutrisi perhari. Penggunaan Metode Algoritma Genetika ke dalam suatu sistem pendukung keputusan adalah solusi yang ditawarkan untuk mengatasi permasalahan tersebut. Dengan bantuan sistem pendukung keputusan, proses pembuatan saran makanan lebih cepat dan akurat sesuai dengan perhitungan nutrisi yang di butuhkan oleh pasien. Data aktifitas kebugaran juga dapat di jadikan parameter dalam menentukan saran makanan tersebut.
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.
KEPUASAN KONSUMEN TERHADAP PENYELENGGARAAN EVENT OLAHRAGA SEPAK BOLA Firdaus Firdaus; Yuliani Yuliani; Rudy Noor Muktamar; Sulastri Sulastri
Altius: Jurnal Ilmu Olahraga dan Kesehatan Vol 9, No 2 (2020): Altius: Jurnal Ilmu Olahraga dan Kesehatan
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/altius.v9i2.12305

Abstract

Penelitian ini bertujuan untuk mengetahui kepuasan konsumen kepuasan konsumen dalam penyelenggaraan event olahraga sepak bola di stadion Gelora Sriwijaya Jakabaring Sport City, khususnya dalam hal sarana prasarana. Penelitian ini merupakan studi deskriptif dengan metode survey. Sampel pada penelitian ini merupakan 40 orang penonton  yang pernah menyaksikan pertandingan secara langsung event olahraga sepak bola di Gelora Sriwijaya Jakabaring Sport City, yang dipilih menggunakan teknik sampel nonprobality sampling (purposive sampling). Data dikumpulkan dengan teknik angket sebagai data utama dan wawancara sebagai data pendukung, kemudian angket dianalisis dengan metode penelitian deskriptif survey. Hasil penelitian ini secara deskriptif yaitu  kepuasan konsumen terhadap penyelenggaraan event olahraga sepak bola di stadion Gelora Sriwijaya Jakabaring Sport City sangat tidak puas pada aspek responsiveness (daya tanggap), tidak puas pada aspek tangibles (kualitas berwujud), emphaty (kemudahan), reliability (keandalan), dan netral pada aspek assurances (jaminan). Selanjutnya dapat disimpulkan bahwa kepuasan konsumen dalam penyelenggaraan event olahraga sepak bola di Stadion Glora Sriwijaya JSC tidak puas. Saran penelitian ini agar dapat menjadi rekomendasi pada PT JSC agar ditingkatkan secara optimal guna memenuhi kepuasan konsumsen.
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.
Sistem Rekomendasi Makanan Untuk Penderita Diabetes Melitus 2 dengan Algoritma Content-based Filtering Caesar Rizky Kurniawan; Firdaus Firdaus; Sarifah Putri Raflesia
Generic Vol 10 No 2 (2018): Vol 10, No 2 (2018)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

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Abstract

Diabetes Melitus (DM) adalah penyakit kronis kompleks yang membutuhkan perawatan medis terus menerus dengan strategi pengurangan risiko multifactorial di luar kendali glikemik. Secara umum penyakit diabetes di klasifikasikan menjadi dua tipe, yakni diabetes tipe 1 yang disebabkan oleh kelainan gen dan tipe 2 yang terjadi karena pola hidup yang tidak sehat. Salah satu penyebab DM yakni konsumsi makanan yang tidak sehat dan jarang melakukan aktifitas olahraga. Untuk itu diperlukan sebuah sistem perekomendasi yang dapat memberikan saran-saran yang bersifat personal berdasarkan pada preferensi yang diberikan oleh pasien penderita DM 2. Sistem Perekomendasi dapat memberikan saran yang efisien untuk mempersempit ruang informasi sehingga pengguna diarahkan ke item yang sesuai dengan kebutuhan berdasarkan preferensi mereka. Dalam hal ini. Metode yang digunakan adalah Content Based Filtering. Metode Content Based Filtering merekomendasikan suatu item dengan cara mencari tingkat kesamaan antara item yang sebelumnya pernah di lihat, diberi like atau pun dipilih dengan item lain. Bahasa pemrograman yang digunakan adalah PHP dengan menggunakan framework Codeigniter. Berdasarkan hasil penelitian penulis menyimpulkan bahwa sistem ini dapat merekomendasikan makanan kepada pasien sesuai dengan preferensinya sehingga pasien dapat lebih efisien dalam menentukan menu makanan.
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.
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.
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.
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.
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%
Co-Authors A. Darmawahyuni A. I. Sapitri Ahmad Zarkasi Andre Herviant Juliano B. Tutuko Bambang Tutuko 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 Muhammad Irham Rizki Fauzi 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 .