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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 Advances in Applied Sciences ComEngApp : Computer Engineering and Applications Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) JUSIFO : Jurnal Sistem Informasi Annual Research Seminar Proceeding of the Electrical Engineering Computer Science and Informatics Journal of Information Systems and Informatics SULUH: Jurnal Abdimas Indonesian Journal of Electrical Engineering and Computer Science Jurnal Generic Jurnal Abdimas Bina Bangsa Smart: Journal of Sharia, Tradition, and Modernity

Bambang Tutuko

Sriwijaya University

Author-ID : 407016

Religion Automotive Engineering Computer Science & IT Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Articles

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Intelligent Robotics Navigation System: Problems, Methods, and Algorithm Siti Nurmaini; Bambang Tutuko
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 6: December 2017
Publisher : Institute of Advanced Engineering and Science

This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments.

Intelligent Mobile Olfaction of Swarm Robots Siti Nurmaini; Bambang Tutuko; Aulia Rahman Thoharsin
IAES International Journal of Robotics and Automation (IJRA) Vol 2, No 4: December 2013
Publisher : Institute of Advanced Engineering and Science

This work presents intelligent mobile olfaction design and experimental results of intelligent swarm robots to detection a gas/odour source in an indoor environment by using multi agent based on hybrid algorithm. We examine the problem for deciding when, how and where the gas/odour sensor should be activated. Simple form of cooperation between Interval Type-2 Fuzzy Logic and Particle Swarm Optimization (IT2FL-PSO) algorithm is implemented in the olfaction strategies. The real experiments performed on smaller five mobile robots equipped with dynamic gas/odour sensor TGS2600 and three infra-red sensors. The results show that single robot-based olfaction system with 5 behaviors capable for searching source of a simulated chemical leak in unknown environment and flooking behavior can be done by 3 robots to find the source of gas/odour.

Bigram feature extraction and conditional random fields model to improve text classification clinical trial document Jasmir Jasmir; Siti Nurmaini; Reza Firsandaya Malik; 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.v19i3.18357

In the field of health and medicine, there is a very important term known as clinical trials. Clinical trials are a type of activity that studies how the safest way to treat patients is. These clinical trials are usually written in unstructured free text which requires translation from a computer. The aim of this paper is to classify the texts of cancer clinical trial documents consisting of unstructured free texts taken from cancer clinical trial protocols. The proposed algorithm is conditional random Fields and bigram features. A new classification model from the cancer clinical trial document text is proposed to compete with other methods in terms of precision, recall, and f-1 score. The results of this study are better than the previous results, namely 88.07 precision, 88.05 recall and f-1 score 88.06.

A New Classification Technique in Mobile Robot Navigation Siti Nurmaini; Bambang Tutuko
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 9, No 3: December 2011
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v9i3.736

This paper presents a novel pattern recognition algorithm that use weightless neural network (WNNs) technique.This technique plays a role of situation classifier to judge the situation around the mobile robot environment and makes control decision in mobile robot navigation. The WNNs technique is choosen due to significant advantages over conventional neural network, such as they can be easily implemented in hardware using standard RAM, faster in training phase and work with small resources. Using a simple classification algorithm, the similar data will be grouped with each other and it will be possible to attach similar data classes to specific local areas in the mobile robot environment. This strategy is demonstrated in simple mobile robot powered by low cost microcontrollers with 512 bytes of RAM and low cost sensors. Experimental result shows, when number of neuron increases the average environmental recognition ratehas risen from 87.6% to 98.5%.The WNNs technique allows the mobile robot to recognize many and different environmental patterns and avoid obstacles in real time. Moreover, by using proposed WNNstechnique mobile robot has successfully reached the goal in dynamic environment compare to fuzzy logic technique and logic function, capable of dealing with uncertainty in sensor reading, achieving good performance in performing control actions with 0.56% error rate in mobile robot speed.

Improving Posture Accuracy of Non-Holonomic Mobile Robot System with Variable Universe of Discourse Siti Nurmaini; Bambang Tutuko; Kemala Dewi; Velia Yuliza; Tresna Dewi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 3: September 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i3.6078

This paper presents a method to decrease imprecision and inaccuracy that have the tendency to influence the posture of non-holonomic mobile by using the adaptive tuning of universe of discourse. As such, the primary objective of the study is to force the posture error of x(t), y(t) and θ(t) towards zero. Hence, for each step of tuning the fuzzy domain, about 20% of imprecision and inaccuracy had been added automatically into the variable universe fuzzy, while the control input was bound via scaling gain. Furthermore, the experimental results showed that the tuning of universe fuzzy parameters could increase the performance of the system from the aspects of response time and error for steady state through better control of inaccuracy. Besides, the domains of universe fuzzy input [-4,4] and output [0,6] exhibited good performance in inching towards zero values as the steady state error was about 1% for x(t) position, 0.02% for y(t) position, and 0.16% for θ(t) orientation, whereas the posture error in the given reference was about 0.0002%.

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.

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€.

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.

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.

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 Abied, Wildan Ade Iriani Sapitri Aditya Aditya Aditya, Aditya Agus Triadi Agus Triadi Ahmad Dahlan Malik Ahmad Hudaifah, Ahmad Ahmad Rifai Ahmad Rifai Ahmad Zarkasi Ahmad Zarkasi Ananda, Dea Agustria Anik Kustirini, Anik Annisa Darmawahyuni Arum, Akhiar Wista Asyhad, M. Aulia Rahman Thoharsin Bayu Wijaya Putra Borisman Richardson Darmawahyuni, Annisa Darmawahyuni, Annisa Dewi, Kemala Dimas Budianto Dwi, Galih Malik Fajri, Faisal Febrita, Suci Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Ganesha Ogi Hanif Habibie Supriansyah Huda Ubaya Isdwanta, Rendy Islami, Anggun Ivall, Mochammad Jasmir Jasmir Kemala Dewi Khairunnisa, Cholidah Zuhroh Maharani, Masayu Nadila Marcelino, Jordan Muhammad Afif Muhammad Fachrurrozi Muhammad Irham Rizki Fauzi Muhammad Naufal Rachmamtullah Muhammad Naufal Rachmatullah PATIYUS AGUSTIANSYAH, PATIYUS PP Aditya, PP, Aditya, PP Pratama, Yogi Tiara Pudjihardjo, Hari Setijo Pudjihardjo, Hari Setijo Purwanto Purwanto Rachmamtullah, Muhammad Naufal Reza Firsandaya Malik Rizal Sanif Rizky, Anandhita Rossi Passarella Samsuryadi Samsuryadi Sapitri, Ade Iriani Sari, Ririn Purnama Sarifah Putri Raflesia Sarmayanta Sembiring Sastradinata, Irawan Setyati Budiningrum, Diah Siti Nurmaini Sukemi Sukemi Sutarno Sutarno Sutarno Sutarno Sutarno Sutrimo Sutrimo Tresna Dewi Triadi, Agus Velia Yuliza W, Prayogo Pandhu Wanto, Sri Winda Kurnia Sari

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