Jurnal Infotel (Sekolah Tinggi Teknologi Telematika Telkom Purwokerto)
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392 research outputs found
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Decision tree method to classify the electroencephalography-based emotion data
Full text linkElectroencephalography (EEG) data contains recordings of brain signal activity divided into several channels with different impulse responses that can be used to detect human emotions. In classifying emotions, EEG data needs to be parsed or signal processed into values that can help recognize emotions. Research related to electroencephalography has been carried out previously and has experienced success using the Fuzzy C-Means, Multiple Discriminant Analysis, and Deep Neural Network methods. This study was conducted to classify human emotions from electroencephalography data on 10 participants. Each participant carried out 40 trials of testing using the Power Spectral Density (PSD) and Discrete Wavelet Transform (DWT) methods at the initial stage of classification and the Decision Tree method as the final method that can improve the accuracy of the two methods at the initial stage of classification. The results of this study were the finding of 2 participants (3 trials) who were unmatched from a total of 10 participants (400 trials), which were analyzed using the decision tree method. The decision tree method can correct this error and increase the classification result to 100%. The DWT method is used as a reference in the classification of emotions, considering that the DWT method has an output of arousal and valance values . In contrast, the PSD method only has a combined output.Electroencephalography (EEG) data contains recordings of brain signal activity divided into several channels with different impulse responses that can be used to detect human emotions. In classifying emotions, EEG data needs to be parsed or signal processed into values that can help recognize emotions. Research related to electroencephalography has been carried out previously and has experienced success using the Fuzzy C-Means, Multiple Discriminant Analysis, and Deep Neural Network methods. This study was conducted to classify human emotions from electroencephalography data on 10 participants. Each participant carried out 40 trials of testing using the Power Spectral Density (PSD) and Discrete Wavelet Transform (DWT) methods at the initial stage of classification and the Decision Tree method as the final method that can improve the accuracy of the two methods at the initial stage of classification. The results of this study were the finding of 2 participants (3 trials) who were unmatched from a total of 10 participants (400 trials), which were analyzed using the decision tree method. The decision tree method can correct this error and increase the classification result to 100%. The DWT method is used as a reference in the classification of emotions, considering that the DWT method has an output of arousal and valance values . In contrast, the PSD method only has a combined output
Receiver diversity with selection combining for drone communication around buildings at frequency 10 GHz
Full text linkThe communication network for cellular network keep development. This research analyzed about cellular network was used drone network. The mobile drone used frequency at 10 GHz for communication. The mobile drone moved around buildings. Buildings were used high variation. Base Station placed around building. This research was using macro diversity Base Station, variation building, variation modulation, and variation height of drone trajectory. Macro diversity mechanism used for that two Base Station. Selection Combining (SC) method was used for that macro diversity mechanism. The modulation communication based from Adaptive Modulation and Coding (AMC). Adaptive Modulation and Coding (AMC) was used Modulation and coding scheme (MCS). Modulation was used QPSK, 16 QAM, and 64 QAM. As the result described signal to noise ratio (SNR) at every node communication, probability MCS, and percentage coverage of drone trajectory. MCS probability for 64 QAM become increased with selection combining method. The percentages coverage of drone trajectory was obtained 77.2% of the first BS, 66.8% of the second BS, and 87.2% with SC method.The communication network for cellular network keep development. This research analyzed about cellular network was used drone network. The mobile drone used frequency at 10 GHz for communication. The mobile drone moved around buildings. Buildings were used high variation. Base Station placed around building. This research was using macro diversity Base Station, variation building, variation modulation, and variation height of drone trajectory. Macro diversity mechanism used for that two Base Station. Selection Combining (SC) method was used for that macro diversity mechanism. The modulation communication based from Adaptive Modulation and Coding (AMC). Adaptive Modulation and Coding (AMC) was used Modulation and coding scheme (MCS). Modulation was used QPSK, 16 QAM, and 64 QAM. As the result described signal to noise ratio (SNR) at every node communication, probability MCS, and percentage coverage of drone trajectory. MCS probability for 64 QAM become increased with selection combining method. The percentages coverage of drone trajectory was obtained 77.2% of the first BS, 66.8% of the second BS, and 87.2% with SC method
Performing the high bitrate visible light communications in the foggy weather to anticipate the interference on vehicle communications
Full text linkWe propose transmission media with visible light to communicate between vehicles. We evaluate the research under four scenarios using the modulation of On-Off Keying Non-Return to Zero (OOK-NRZ) and bitrate up to 1 Gbps. These scenarios are (i) ideal conditions, (ii) interference from other vehicle lights, (iii) foggy conditions, and (iv) interference from vehicles and fog conditions. Based on the extensive simulation, the results obtained are that interference and fog conditions can affect and reduce the value of the signal to interference and noise ratio (SNR) and increase the value of the error rate (BER). The results obtained are that interference and fog conditions can affect and reduce the value of the signal to interference and noise ratio (SNR) and increase the value of the error rate (BER). The SNR value in the first scenario is 23.6 dB and the second scenario is 11.1 dB, where this value is still sufficient. The SNR in scenario three is 16.1 dB, and the lowest in the fourth scenario is -7.78326 dB, indicating that the noise is extensive compared to signal power. In addition, we also obtain an optimal distance of communication between vehicles for each scenario sequentially 14.5 m, 13 m, 11.5 m, and 9 m
The foF2 depression over pameungpeuk during solar minimum and its application on HF radio communication
Full text linkThe foF2 depression of the ionosphere layer is a reference for determining the maximum usable frequency depression for an HF communication circuit. This paper discusses the foF2 depression observed at the Pameungpeuk observation station ((7.65°S, 107.96°E; inclination 32.38°S), in 2018 - 2021 when solar activity is minimum and the sun is at a minimum, but the foF2 depression continues to occur up to the severe level. Likewise, geomagnetic disturbances also occur to a moderate level, so that geomagnetic disturbances are a potential cause of foF2 depression. Another result is that the temporal variation pattern of the foF2 depression is less clear so that statistical models cannot be used. The correlation between the number of occurrences of foF2 depression in a month and the number of occurrences of geomagnetic disturbances is relatively low and found in months without the occurrence of geomagnetic disturbances but still foF2 depression occurs, so that geomagnetic disturbances are not the only cause of foF2 depression. Another possibility is the cause of foF2 depression is solar eclipse. In the application, information on the prediction of the foF2 depression that will occur can be used in frequency management, so that a frequency channel is obtained that matches the reflectivity of the ionospheric layer during operation. Anomalies of solar activity and geomagnetic disturbances can be used as inputs in predicting the foF2 depression
Classification of ECG signal-based cardiac abnormalities using fluctuation-based dispersion entropy and first-order statistics
Full text linkThe heart is one of the most important organs in the human body. The presence of abnormalities in the heart can be fatal for a person. One way to detect heart abnormalities is an Electrocardiogram (EKG) signal examination. To facilitate the detection of ECG signal abnormalities, an automatic classification method is needed. Therefore, in this study, a method for classifying ECG signals using FdispEn (Fluctuation-based dispersion Entropy) and first-order statistics is proposed. FdispEn measures the uncertainty in the signal and is expected to be able to distinguish the physiological state of the ECG signal time series. In this study FdispEn and statistical computing were used as feature extraction of the ECG signal and combined with the Support Vector Machine (SVM) for the classification process of Normal ECG, AFIB (Atrial Fibrilation), and CHF (Congestive Heart Failure). The method proposed in this study generates an accuracy of 91.5%. The system proposed in this study is expected to assist in the clinical diagnosis of abnormalities in the heart.
 
Implementation of line detection self-driving car using HSV method based on raspberry pi 4
Full text linkWith the development of technology, especially in the field of robotics, daily human activities can be carried out with artificial intelligence. One of the artificial intelligence technologies that help ease the burden on humans, especially in terms of driving, is self-driving cars. In this case, self-driving cars have several methods with GPS systems, radar, lidar, or cameras. In this study, a self-driving car system was designed on a navigation path model using a street mark detector with an intermediary sensor, namely a camera as a vision sensor. This self-driving car system uses a prototype called an autonomous car to walk on a path which is a self-driving car navigation direction based on the detected line to be able to detect camera sensors that process line images from the camera using HSV. method. In this study, a self-driving car system has been successfully designed using a microcontroller, namely Raspberry Pi 4 as a programmer and L298n motor driver, BTS7960 as a driver for a self-driving car. The Raspberry Pi 4 sends real-time images through the camera as a vision sensor which then detects a line to navigate the movement of this self-driving car. By using image processing, the resulting level of precision can reach the average value according to the direction of the self-driving car
Opinion mining indonesian presidential election on twitter data based on decision tree method
Full text linkIndonesia is a country led by a president. The term of the leadership of a president will be democratically elected every five years. The current president will end his term of office in 2024. So that in that year, the people will hold a direct general election to determine the president between 2024 and 2029. Before the general election was held in Indonesia itself, it was thick related to the campaign for each presidential candidate carried out by his supporters. The campaign is carried out directly to village locations and on social media Twitter/Facebook/YouTube. His campaign writing on Twitter is exciting to analyze. Even now, many tweets related to the 2024 presidential election contain various opinions from the public. This study will examine the sentiment of someone's tweet to see the public's statement regarding the 2024 presidential election. The resulting sentiment categories are positive, negative, and neutral, and the word tweet related to the sentiment category will be visualized. The results of the sentiment category will then be classified using a tree-based method, namely a decision tree. The accuracy generated by applying the decision tree method is 99.3%. The decision tree method is also superior to the regression-based way by 2.5%.Indonesia is a country led by a president. The term of the leadership of a president will be democratically elected every five years. The current president will end his term of office in 2024. So that in that year, the people will hold a direct general election to determine the president between 2024 and 2029. Before the general election was held in Indonesia itself, it was thick related to the campaign for each presidential candidate carried out by his supporters. The campaign is carried out directly to village locations and on social media Twitter/Facebook/YouTube. His campaign writing on Twitter is exciting to analyze. Even now, many tweets related to the 2024 presidential election contain various opinions from the public. This study will examine the sentiment of someone's tweet to see the public's statement regarding the 2024 presidential election. The resulting sentiment categories are positive, negative, and neutral, and the word tweet related to the sentiment category will be visualized. The results of the sentiment category will then be classified using a tree-based method, namely a decision tree. The accuracy generated by applying the decision tree method is 99.3%. The decision tree method is also superior to the regression-based way by 2.5%
Utilization of the COBIT 2019 framework to identify the level of governance in internet services
Full text linkInformation and communication technology services at the University of Muhammadiyah Bengkulu are IT services that support IT needs in all sectors. Of all the IT services that have been implemented at this institution, there is one very crucial service, namely the internet connection service, where this internet connection service is needed by all existing information technology access. In managing this internet connection, a standardized feasibility calculation has not been carried out which results in it not being in accordance with the institutional business needs. Information technology governance is a process that is able to manage investment decisions related to Information Technology within the company in order to achieve the company's current and future needs. To achieve standardized governance, this research uses the COBIT 2019 framework which is the latest version of the development results from COBIT 5. The purpose of this study is to identify the extent to which the value of existing processes for internet connection services is currently and the value of the process achievement that refers to the standard. COBIT 2019 by calculating the maturity level value which represents the level of performance on internet connection services. From the results of the 2019 COBIT Design, LTIK Muhammadiyah Bengkulu University, it is known that those who score above 80 or must reach Capability Level 4 are APO13, BAI10, DSS02, DSS03 and DSS04, for a value of 100 there is APO12
Analysis of greedy perimeter stateless routing protocol network simulation using bird flocking algorithm
Full text linkThe purpose of this study is to simulate the GPSR protocol network on NS3 using the bird flocking algorithm and to analyze the comparison of performance measurements obtained from the simulation results. The Greedy Perimeter Stateless Routing network was simulated using NS-3 in this simulation. The simulation area is created in length, width. The distance between nodes is 50 meters and is simulated in an area of 1000m x 300m for 30 seconds and 802.11 MAC protocol is used. This simulation was successfully implemented in finding the location of the nearest node using the GPSR protocol with the PSO / BFA algorithm. The number of nodes used in the simulation is 150 nodes and 2 nodes, so it can be concluded that the performance of Quality of Service (QoS) is greatly affected by the number of nodes and the algorithm used in the simulation