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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) International Journal of Advances in Applied Sciences ComEngApp : Computer Engineering and Applications Journal Jupiter TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JSI: Jurnal Sistem Informasi (E-Journal) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Scientific Journal of Informatics ICON-CSE Annual Research Seminar Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Ilmiah Matrik JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Plastik Rekonstruksi Bioscientia Medicina : Journal of Biomedicine and Translational Research Indonesian Journal of Electrical Engineering and Computer Science Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Generic Bioscientia Medicina : Journal of Biomedicine and Translational Research Jurnal Penelitian Pendidikan

Siti Nurmaini

University Of Sriwijaya

Author-ID : 335335

Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Physics Other

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Articles

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Fuzzy Logic-Ant Colony Optimization for Explorer-Follower Robot with Global Optimal Path Planning Bambang Tutuko; Siti Nurmaini; Ganesha Ogi
Computer Engineering and Applications Journal Vol 7 No 1 (2018)
Publisher : Universitas Sriwijaya

Path planning is an essential task for the mobile robot navigation. However, such a task is difficult to solve, due to the optimal path needs to be rerouted in real-time when a new obstacle appears. It produces a sub-optimal path and the robot can be trapped in local minima. To overcome the problem the Ant Colony Optimization (ACO) is combined with Fuzzy Logic Approach to make a globally optimal path. The Fuzzy-ACO algorithm is selected because the fuzzy logic has good performance in imprecision and uncertain environment and the ACO produce simple optimization with an ability to find the globally optimal path. Moreover, many optimization algorithms addressed only at the simulation level. In this research, the real experiment is conducted with the low-cost Explorer-Follower robot. The results show that the proposed algorithm, enables them to successfully identify the shortest path without collision and stack in â€œlocal minimaâ€.

Localization of Leader-Follower Robot Using Extended Kalman Filter Siti Nurmaini; Sahat Pangidoan
Computer Engineering and Applications Journal Vol 7 No 2 (2018)
Publisher : Universitas Sriwijaya

Non-holonomic leader-follower robot must be capable to find its own position in order to be able to navigating autonomously in the environment this problem is known as localization. A common way to estimate the robot pose by using odometer. However, odometry measurement may cause inaccurate result due to the wheel slippage or other small noise sources. In this research, the Extended Kalman Filter (EKF) is proposed to minimize the error or the inaccuracy caused by the odometry measurement. The EKF algorithm works by fusing odometry and landmark information to produce a better estimation. A better estimation acknowledged whenever the estimated position lies close to the actual path, which represents a system without noise. Another experiment is conducted to observe the influence of numbers of landmark to the estimated position. The results show that the EKF technique is effective to estimate the leader pose and orientation pose with small error and the follower has the ability traverse close to leader based-on the actual path.

Swarm Intelligent in Bio-Inspired Perspective: A Summary Nyayu Husni Latifah; Ade Silvia; Ekawati Prihatini; Siti Nurmaini; Irsyadi Yani
Computer Engineering and Applications Journal Vol 7 No 2 (2018)
Publisher : Universitas Sriwijaya

This paper summarizes the research performed in the field of swarm intelligent in recent years. The classification of swarm intelligence based on behavior is introduced. The principles of each behaviors, i.e. foraging, aggregating, gathering, preying, echolocation, growth, mating, clustering, climbing, brooding, herding, and jumping are described. 3 algorithms commonly used in swarm intelligent are discussed. At the end of summary, the applications of the SI algorithms are presented.

Real-Time Lighting Control System with Fuzzy-Mamdani for Smart Home Application Dimas Budianto; Siti Nurmaini; Bambang Tutuko; Sarifah Putri Raflesia
Computer Engineering and Applications Journal Vol 7 No 3 (2018)
Publisher : Universitas Sriwijaya

The use of pervasive computing in the context of home automation equipment will greatly facilitate life. Several building still use manual switch to turn on or turn off the lighting system. It becomes ineffective if the house has a lot of lights, due to it sometimes forget to turn off. Hence, the real-time control system for automatic lighting processing is desirable. An automatic control system will allow to control the illumination and it will decrease the energy costs. In this paper, the Fuzzy logic system-based Mamdani style is used to adjust the intensity of the lights. Based on simple algorithm the controller board is working in a real-time condition. As a result found, the implementation is successfully to control the lighting system with good performance. Thus, the fuzzy system can be built smart home concept that facilitate the human life.

Skin Lesion Classification Based on Convolutional Neural Networks Renny Amalia Pratiwi; Siti Nurmaini; Dian Palupi Rini
Computer Engineering and Applications Journal Vol 8 No 3 (2019)
Publisher : Universitas Sriwijaya

Melanoma causes the majority of skin cancer deaths. The population level of melanoma has increased over the past 30 years. It kills around 9.320 people in the US every year. Melanoma can often be found early, when it is most likely to be cured. Medical diagnoses using digital imaging with machine learning methods have become popular because of their ability to recognize patterns in digital images. Image diagnosis accuracy allows disease cured at an early stage. This paper proposes a simulation that can be used for early detection of skin cancer that can help dermatologists to distinguish melanomas from other pigmented lesions on the skin. Some researchers have developed a system using machine learning algorithms used to classify skin lesions from dermoscopy images of human skin. In this study, we proposed Convolutional Neural Network (CNN) to our model. CNN is very efficient for image processing because feature extractors can be optimized, applied to each feature image position. The results of skin lesion classification of benign nevi and melanoma based on CNN models produces high accuracy (area under the receiver operator characteristics (ROC) curve (AUC) is 92.59 %, sensitivity is 89.47%, specificity is 100.0%, precision is 100 % and F1 score is 94.44 %).

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.

Author Matching Classification with Anomaly Detection Approach for Bibliomethric Repository Data Zaqqi Yamani; Siti Nurmaini; Dian Palupi Rini
Computer Engineering and Applications Journal Vol 9 No 2 (2020)
Publisher : Universitas Sriwijaya

Authors name disambiguation (AND) is a complex problem in the process of identifying an author in a digital library (DL). The AND data classification process is very much determined by the grouping process and data processing techniques before entering the classifier algorithm. In general, the data pre-processing technique used is pairwise and similarity to do author matching. In a large enough data set scale, the pairwise technique used in this study is to do a combination of each attribute in the AND dataset and by defining a binary class for each author matching combination, where the unequal author is given a value of 0 and the same author is given a value of 1. The technique produces very high imbalance data where class 0 becomes 98.9% of the amount of data compared to 1.1% of class 1. The results bring up an analysis in which class 1 can be considered and processed as data anomaly of the whole data. Therefore, anomaly detection is the method chosen in this study using the Isolation Forest algorithm as its classifier. The results obtained are very satisfying in terms of accuracy which can reach 99.5%.

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.

The Artificial Intelligence Readiness for Pandemic Outbreak COVID-19: Case of Limitations and Challenges in Indonesia Siti Nurmaini
Computer Engineering and Applications Journal Vol 10 No 1 (2021)
Publisher : Universitas Sriwijaya

Artificial intelligence (AI) technologies continue to play significant roles during the Coronavirus 2019 (COVID-19) pandemic in the world. However, health is an area where the rules are stringent and inflexible. This can be justified because it deals with human life. Nevertheless, at the same time, a large number of tests, certifications, and panels will lead to innovations in AI for healthcare that are longer, more complex, and difficult to incorporate into real-world applications. Indonesia has a lot of AI research, which is challenging to commercialize in medicine. These researches are not yet effective due to several limitations in terms of (i) the readiness of a skilled workforce to develop and use AI, (ii) the readiness of regulations that regulate the ethics of using and utilizing responsibly, (iii) the readiness of computational infrastructure and supporting data for AI modeling, and (iv) readiness industry and the public sector in adopting AI innovations. In pandemic outbreak COVID-19, AI technology should help the medical industry more significantly, caused by such limitations, and it has not yet been impactful against COVID-19 in Indonesia. In the future, AI technology exists as a complementary facility to increase the productivity of medical personnel and acts as a disease prevention facility.

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.

Co-Authors A. Darmawahyuni A. I. Sapitri Ade Iriani Sapitri Ade Iriani Sapitri Ade Iriani Sapitri Ade Silvia Ade Silvia Ade Silvia Handayani Aditya Aditya Aditya, Aditya Agung Juli Anda Agus Triadi Agus Triadi Agus Triadi Ahmad Zarkasi Ahmad Zarkasi Ahmad Zarkasi Ahmad Zarkasih Akhiar Wista Arum Andre Herviant Juliano Anggun Islami Anggun Islami Annisa Darmawahyuni Ardy Hidayat Arief Cahyo Utomo Armansyah, Risky Arnaldo, Muhammad Arum, Akhiar Wista Aulia Rahman Thoharsin B. Tutuko Bambang Tutuko Bambang Tutuko Bayu Wijaya Putra Benedictus Wicaksono Widodo Bhakti Yudho Suprapto Bhakti Yudho Suprapto Bhakti Yudho Suprapto Cindy Kesty Darmawahyuni, Annisa Darmawahyuni, Annisa Deris Stiawan Dewi, Kemala Dewi, Tresna Dian Palupi Rini Dian Palupi Rini Dian Palupi Rini Dimas Budianto Dinda Lestarini Dodo Zaenal Abidin Dwi Mei Rita Sari Ekawati Prihatini Erliza Yuniarti Fachrudin Abdau Fahreza, Irvan Falah Yuridho Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus, Firdaus Firsandaya Malik, Reza Ganesha Ogi GITA FADILA FITRIANA Hadipurnawan Satria Hanif Habibie Supriansyah Huda Ubaya Huda Ubaya Huda Ubaya Husnawati Husnawati Husnawati Husnawati Husnawati Husni, Nyayu Latifah Husni, Nyayu Latifah Irfannuddin Irfannuddin Irsyadi Yani Irvan Fahreza Iryadi Yani Iryadi Yani, Iryadi Isdwanta, Rendy Islami, Anggun Jasmir Jasmir Jasmir Jasmir Jordan Marcelino Kemala Dewi Khairunnisa, Cholidah Zuhroh Krisna Murti Kurniawan, Anggy Tias Kurniawan, Anggy Tyas Legiran Legiran M. Hashim, Siti Zaiton M. N. Rachmatullah M. Naufal Rachmatullah Maharani, Masayu Nadila Marcelino, Jordan Masayu Nadila Maharani Mira Afrina Muhamad Akbar Muhammad Afif Muhammad Anshori Muhammad Arnaldo Muhammad Fachrurrozi Muhammad Fachrurrozi Muhammad Irham Rizki Fauzi Muhammad Naufal Rachmatullah Muhammad Naufal, Muhammad Muhammad Roriz Muhammad Taufik Roseno, Muhammad Taufik Muzakkie, Mufida Nadia Ayu Oktabella, nadia ayu oktabella Novi Yusliani Nurqolbiah, Fatihani Nuswil Bernolian Nuswil Bernolian Nyayu Latifah Husni Nyayu Latifah Husni, Nyayu Latifah Oky Budiyarti Osvari Arsalan Passa, Rahma Satila Patiyus Agustiansyah PATIYUS AGUSTIANSYAH, PATIYUS Pola Risma PP Aditya, PP, Aditya, PP Pratama, Jimiria Putri Mirani Rachmamtullah, Muhammad Naufal Radiyati Umi Partan Radiyati Umi Partan Radiyati Umi Partan Radiyati Umi Partan, Radiyati Umi Rahma Satila Passa Rendy Isdwanta Renny Amalia Pratiwi Reza Firsandaya Malik Reza Firsandaya Malik Ria Nova Ricy Firnando Ricy Firnando Ricy Firnando Rizal Sanif Rizki Kurniati Rossi Passarella Sahat Pangidoan Samsuryadi Samsuryadi Saparudin Saparudin Saparudin, Saparudin Sapitri, Ade Iriani Saputra, Tommy Sari, Dwi Mei Rita Sarifah Putri Raflesia Sarifah Putri Raflesia, Sarifah Putri Sastradinata, Irawan Sigit Prasetyo Noprianto Siti Zaiton Siti Zaiton M. Hashim Soedjana, Hardi Siswo Sri Desy Siswanti Suci Dwi Lestari Suci Dwi Lestari Suhandono, Nugroho Sukemi Sukemi Sukemi Sukemi Sukemi Sukman Tulus Putra Sutarno Sutarno Syamsul Arifin Syaputra, Hadi Tio Artha Nugraha Tresna Dewi Tresna Dewi Tri Undari Triadi, Agus Triadi, Agus Varindo Ockta Keneddi Putra Velia Yuliza Winda Kurnia Sari Wisnu Adi Putra Yani, Iryadi Yesi Novaria Kunang Yurni Oktarina Zaqqi Yamani

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