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All Journal JOIV : International Journal on Informatics Visualization
Seniman, Seniman
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Computer Science & IT

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Classification of Human Concentration Levels Based on Electroencephalography Signals Siregar, Baihaqi; Florence, Grace; Seniman, Seniman; Fahmi, Fahmi; Mubarakah, Naemah
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2045

Abstract

Concentration denotes the capability to direct one's attention to a specific subject matter. Presently, within the era characterized by an overwhelming abundance of information inundating human existence, distractions frequently impede human concentration, thereby influencing the depth of knowledge acquisition. Various elements contribute to the decline in human concentration, including diminished metabolic states, inadequate sleep, and engaging in multiple tasks simultaneously. The cognitive state of an individual during the process of thinking can be assessed through the analysis of electroencephalography signals. The primary objective of this investigation is to facilitate experts' interpretation of electroencephalography signal outcomes for categorizing concentration levels. The dataset utilized in this examination comprises unprocessed EEG data obtained from observing individuals in both relaxation and concentration states. After data preprocessing, feature extraction is executed, and classification is performed using the Support Vector Machine technique. The outcome of this study reveals an accuracy rate of 84%. These developments allow for continual monitoring of brain function, an enhanced comprehension of cerebral activities, and increased operational efficacy of end-effectors. The implications of these advancements on prospective research opportunities are evident in the potential for more accurate diagnosis of neurological disorders and the progression of sophisticated BCI applications designed to support healthcare and monitor cognitive states. The evolution of EEG technology is paving the way for novel research pathways in neuroscience and human-computer interaction.
Bike Fitting System Based on Digital Image Processing on Road Bike Nasution, Tigor Hamonangan; Sitohang, Andreas; Seniman, Seniman; Soeharwinto, Soeharwinto
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2796

Abstract

This research aims to develop a bike fitting system based on digital image processing for road bikes. The method used in this study involves using the OpenCV and MediaPipe libraries in the Python programming language to detect the rider's body pose from a video stream captured using a webcam. The body pose data is then used to calculate important angles such as elbow, hip, knee, and ankle range related to the correct riding position for road bikes. In this research, a comparison is made between the body angles obtained and the angle range considered ideal for bike fitting on road bikes. If the body angles fall within the desired range, the system will label it as "Fit”; if the body angles are outside the selected range, the system will label it as "Not Fit." The results of this study indicate that the bike fitting system based on digital image processing using a webcam can provide helpful visual feedback in improving the rider's body position for road bikes. By observing the body angles produced and seeing the "Fit" or "Not Fit" label, cyclists can adjust their position to match the ideal position in bike fitting. The system test results show a low error rate, with elbow angle having an average error of 0.81%, hip angle of 1.37%, knee angle of 0.83%, and ankle range of 1.76%. Thus, this research contributes significantly to supporting cyclists in achieving a position appropriate to their inseam height.
Co-Authors Fahmi Fahmi Florence, Grace Naemah Mubarakah Nasution, Tigor Hamonangan Siregar, Baihaqi Sitohang, Andreas Soeharwinto Soeharwinto, Soeharwinto