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Classification of Physiological Signals for Emotion Recognition using IoT Tiwari, Sadhana; Agarwal, Sonali; Syafrullah, Muhammad; Adiyarta, Krisna
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1943

Abstract

Emotion recognition gains huge popularity now a days. Physiological signals provides an appropriate way to detect human emotion with the help of IoT. In this paper, a novel system is proposed which is capable of determining the emotional status using physiological parameters, including design specification and software implementation of the system. This system may have a vivid use in medicine (especially for emotionally challenged people), smart home etc. Various Physiological parameters to be measured includes, heart rate (HR), galvanic skin response (GSR), skin temperature etc. To construct the proposed system the measured physiological parameters were feed to the neural networks which further classify the data in various emotional states, mainly in anger, happy, sad, joy. This work recognized the correlation between human emotions and change in physiological parameters with respect to their emotion.
Diagnosis of Smear-Negative Pulmonary Tuberculosis using Ensemble Method: A Preliminary Research Rusdah, Rusdah; Syafrullah, Mohammad
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1944

Abstract

Indonesia is one of 22 countries with the highest burden of Tuberculosis in the world. According to WHO’s 2015 report, Indonesia was estimated to have one million new tuberculosis (TB) cases per year. Unfortunately, only one-third of new TB cases are detected. Diagnosis of TB is difficult, especially in the case of smear-negative pulmonary tuberculosis (SNPT). The SNPT is diagnosed by TB trained doctors based on physical and laboratory examinations. This study is preliminary research that aims to determine the ensemble method with the highest level of accuracy in the diagnosis model of SNPT. This model is expected to be a reference in the development of the diagnosis of new pulmonary tuberculosis cases using input in the form of symptoms and physical examination in accordance with the guidelines for tuberculosis management in Indonesia. The proposed SNPT diagnosis model can be used as a cost-effective tool in conditions of limited resources. Data were obtained from medical records of tuberculosis patients from the Jakarta Respiratory Center. The results show that the Random Forest has the best accuracy, which is 90.59%, then Adaboost of 90.54% and Bagging of 86.91%.
Testing Big Data Applications Punn, Narinder; Agarwal, Sonali; Syafrullah, Muhammad; Adiyarta, Krisna
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1952

Abstract

Today big data has become the basis of discussion for the organizations. The big task associated with big data stream is coping with its various challenges and performing the appropriate testing for the optimal analysis of the data which may benefit the processing of various activities, especially from a business perspective. Big data term follows the massive volume of data, (might be in units of petabytes or exabytes) exceeding the processing and analytical capacity of the conventional systems and thereby raising the need for analyzing and testing the big data before applications can be put into use. Testing such huge data coming from the various number of sources like the internet, smartphones, audios, videos, media, etc. is a challenge itself. The most favourable solution to test big data follows the automated/programmed approach. This paper outlines the big data characteristics, and various challenges associated with it followed by the approach, strategy, and proposed framework for testing big data applications.
Prediction Of Students Academic Success Using Case Based Reasoning Rahman, Abdul; Mutiarawan, Rezza Anugrah; Darmawan, Agung; Rianto, Yan; Syafrullah, Mohammad
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1956

Abstract

Academic success for a student is influenced by many factors during their study period. Factors such as student gender, student absenteeism, parental satisfaction with schools, relations and parents who are responsible for students can influence student success in the academic field. Researchers try to find out what are the most dominant factors in determining academic success for a student at different levels of education such as elementary, middle and high school level. Previous research grouped the level of student academic success into three levels, namely low, medium, high and obtained 15 Association Rules Generated By Apriori Algorithm. This study tried to find out and predict the possible level of academic success of students by using 9 Association Rules Generated By Apriori Algorithm from previous research. The method used to predict the level of student academic success is case based reasoning with the nearest neighbor algorithm. By using the Association Rules Generated By Image Algorithm and with the data set from the xAPIEducational Mining Dataset the case similarity value was obtained with knowledge data that is 1 with a percentage of 81%, and data that had a similarity value of less than 1 was 19%. While in the previous study the best classification accuracy was 80.6% by the Voting classifier. And the grouping of success data is divided into two, namely low and high.
Fish Eggs Calculation Models Using Morphological Operation Ramdhan, Syaipul; Syafrullah, Muhammad
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1959

Abstract

Calculations on group objects are the concern of current researchers, to find optimal detection and calculation solutions. One of them is fish eggs in a group. Fish cultivators need precision in calculations, because currently conventional methods often make errors in calculations. If the calculation is wrong, it will have an impact on production and sales that are not balanced (loss). Small and easily broken fish eggs are grouped and it isdifficult to do manual calculations. The purpose of this study is to test which segmentation method is the most optimal in calculating these grouped fish egg objects and produce precise and fast calculations. The test model was developed from algorithm of morphological operations,watershed and statistical approaches with the same number of samples. The result shows morphological operation is better than the others with 96.67%, watershed 81.28% and the count statistic is 95.62% with an average calculation process speed of 54.5 seconds for morphological operations, watershed 1 minute 55 seconds and statistical approach 58.9 seconds. As a result. morphology gets the most optimal and fast calculation results.
A Third Order based Additional Regularization in Intrinsic Space of the Manifold Yadav, Rakesh Kumar; Singh, Abhishek; Verma, Shekhar; Venkatesan, S.; Syafrullah, M.; Adiyarta, Krisna
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1961

Abstract

Second order graph Laplacian regularization has the limitation that the solution remains biased towards a constant which restricts its extrapolationcapability. The lack of extrapolation results in poor generalization. An additional penalty factor is needed on the function to avoid its over-fitting on seen unlabeled training instances. The third order derivative based technique identifies the sharp variations in the function and accurately penalizes them to avoid overfitting. The resultant function leads to a more accurate and generic model that exploits the twist and curvature variations on the manifold. Extensive experiments on synthetic and real-world data set clearly shows thatthe additional regularization increases accuracy and generic nature of model.
Privacy Control In Social Networks By Trust Aware Link Prediction Dhannuri, Syam Prasad; Sonbhadra, Sanjay Kumar; Agarwal, Sonali; Nagabhushan, P.; Syafrullah, M.; Adiyarta, Krisna
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1972

Abstract

Social networks are exceedingly common in today’s society. A social network site is an online platform where people build social relations with others and share information. For the last two decades, rapid growth in the number of users and applications with these social networking sites, make the security as the most challenging issue. In this virtual environment, some greedy people intentionally perform illegal activities by accessing others’ private information. This paper proposes a novel approach to detect the illegal access of a particular’s information by using trustaware link prediction. The facebook dataset is used for experiments and the results justify the robustness andtrustworthiness of the proposed model.
Gesture recognition by learning local motion signatures using smartphones Agarwal, Prachi; Sonbhadra, Sanjay Kumar; Agarwal, Sonali; Nagabhushan, P.; Syafrullah, Muhammad; Adiyarta, Krisna
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1982

Abstract

In recent years, gesture or activity recognition is an important area of research for the modern health care system. An activity is recognized by learning from human body postures and signatures. Presently all smartphones are equipped with accelerometer and gyroscopes sensors, and the reading of these sensors can be utilized as an input to a classifier to predict the human activity. Although the human activity recognition gained a notable scientific interest in recent years, still accuracy, scalability and robustness need significant improvement to cater as a solution of most of the real world problems. This paper aims to fill the identified research gap and proposes Grid Search based Logistic Regression and Gradient Boosting Decision Tree multistage prediction model. UCI-HAR dataset has been used to perform Gesture recognition by learning local motion signatures. The proposed approach exhibits improved accuracy over preexisting techniques concerning to human activity recognition.
Client Side Channel State Information Estimation for MIMO Communication Tiwari, Sambhavi; Abhishek, Abhishek; Verma, Shkehar; Singh, K; Syafrullah, M; Adiyarta, Krisna
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1993

Abstract

Multiple-input multiple-output (MIMO) system relies on a feedback signal which holds channel state information (CSI) from receiver to the transmitter to do pre-coding for achieving better performance. However, sending CSI feedback at each time stamp for long duration is an overhead in the communication system. We introduce a deep reinforcement learning based channel estimation at receiver end for single user MIMO communication without CSI feedback. In this paper we propose to train the receiver with known pilot signals to analyse the stochastic behaviour of the wireless channel. The simulation on MIMO channel with additive white Gaussian noise (AWGN) shows that our proposed method can learn the different characteristics affecting the channel with limited number of pilot signals. Extensive experiments show that the proposed method was able to outperform the existing state-of-the-art end to end reinforcement learning method. The results demonstrate that the proposed method learns and predicts the stochastic time varying channel characteristic accurately at receiver’s end.
PID Controller Design for Mobile Robot Using Bat Algorithm with Mutation (BAM) Pebranti, Dwi; Bayuaji, Luhur; Arumgam, Yogesvaran; Riyanto, Indra; Syafrullah, Muhammad; Qasrina Ann Ayop, Nurnajmin
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1996

Abstract

By definition, a mobile robot is a type of robotthat has capability to move in a certain kind of environmentand generally used to accomplish certain tasks with somedegrees of freedom (DoF). Applications of mobile robots coverboth industrial and domestic area. It may help to reduce risk tohuman being and to the environment. Mobile robot is expectedto operate safely where it must stay away from hazards such asobstacles. Therefore, a controller needs to be designed to makethe system robust and adaptive. In this study, PID controller ischosen to control a mobile robot. PID is considered as simpleyet powerful controller for many kind of applications. Indesigning PID, user needs to set appropriate controller gain toachieve a desired performance of the control system, in termsof time response and its steady state error. Here, anoptimization algorithm called Bat Algorithm with Mutation(BAM) is proposed to optimize the value of PID controller gainfor mobile robot. This algorithm is compared with a wellknownoptimization algorithm, Particle Swarm Optimization(PSO). The result shows that BAM has better performancecompared to PSO in term of overshoot percentage and steadystate error. BAM gives 2.29% of overshoot and 2.94% ofsteady state error. Meanwhile, PSO gives 3.07% of overshootand 3.72% of steady state error.
Co-Authors Abdul Rahman Abdul Rahman Wahid Abhishek Abhishek Abhishek Singh Abhishek, Abhishek Achmad Maulana Achmad Solichin Adiyarta, Krisna Agarwal, Prachi Agarwal, Sonali Agarwal, Sonali Agarwal, Sonali Agung Darmawan Agus Riyanto Andrico Andrico Aria Mustofa Hidayat Armando Ondihon Kristoper Purba Arumgam, Yogesvaran Bayuaji, Luhur Darmawan, Agung Devit Setiono Dewi, Ernawati Dhannuri, Syam Prasad Dwi Pebranti Dwi Pebrianti Emil Salim Ernawati Dewi Esti Setiasih Hadi Syahrial Indra Nugraha Abdullah Indra Riyanto Irawan Irawan Jamhari Jamhari Jan Everhard Riwurohi Juan Kalyzta K Singh Kassim, Siti Rafidah Binti Krisna Adiyarta Luhur Bayuaji M. Ivan Putra Eriansya Makhdum Rosadi Meilieta Anggriani Porrie Mohammad Fadhil Abas Muhammad Azhar Mujahid Muhammad Hasanul Huda Mutiarawan, Rezza Anugrah Nagabhushan, P. Narinder Punn Nurnajmin Qasrina Ann Nurnajmin Qasrina Ann Ayop P. Nagabhushan Pandu Pradinata Pebranti, Dwi Porrie, Meilieta Anggriani Prachi Agarwal Punn, Narinder Purba, Armando Ondihon Kristoper Purwanto Purwanto Qasrina Ann Ayop, Nurnajmin Rakesh Kumar Yadav Ramdhan, Syaipul Ratna Kusumawardani Ratna Kusumawardani Rezza Anugrah Mutiarawan Rianto, Yan Ridho Saputra Riyanto, Indra Rizki Aji Wibowo Rusdah Rusdah S. Venkatesan Sadhana Tiwari Sambhavi Tiwari Samidi Samidi Sanjay Kumar Sonbhadra Sanjay Kumar Sonbhadra Sari, Widya Kumala Setyawan Widyartoh Shekhar Verma Shkehar Verma Singh, Abhishek Singh, K Siti Rafidah Binti Kassim Sonali Agarwal Sonali Agarwal Sonali Agarwal Sonbhadra, Sanjay Kumar Sonbhadra, Sanjay Kumar Supardi Supardi Supardi, Supardi Syaiful Anwar Syaipul Ramdhan Syam Prasad Dhannuri Thisa Tri Utami Tiwari, Sadhana Tiwari, Sambhavi Tutik Sri Susilowati Venkatesan, S. Verma, Shekhar Verma, Shkehar Victor Ilyas Sugara Widya Kumala Sari Widyartoh, Setyawan Windarto Windarto Yadav, Rakesh Kumar Yan Rianto Yodi Susanto Yogesvaran Arumgam Yulianawati Zulkarnaen Noor Syarif