Faiz, Aufa Nizar (2019) Sistem Pengenalan Pergerakkan Lengan Menggunakan Exponential Moving Average dengan Metode Decision Tree Berbasis EMG. Sarjana thesis, Universitas Brawijaya.
Abstract
Kesehatan adalah hal yang terpenting pada proses kehidupan manusia. Manusia sehat dapat melakukan perkerjaannya, tetapi beberapa manusia yang tidak beruntung tidak bisa melakukan pekerjaannya dikarenakan disabiltilas kesehatan. Biomedis pada saat ini berkembang secara pesat mendukung kesahatan dengan cepat dan mudah. Salah satu ilmu biomedis untuk deteksi sinyal fiologis yang terjadi ditubuh manusia yaitu sinyal electromyography (EMG), tanpa harus menggunakan metode inverse adalah memasukan alat deteksi kedalam tubuh manusia yang berisiko tinggi merusak kesehatan tubuh manusia. Dengan menggunakan sinyal electromyography (EMG) dari kontraksi otot manusia, kita dapat menggunakan sinyal tersebut untuk berbagai hal. Salah satu kegunaan adalah pendeteksian pergerakkan untuk perangkat elektronik. Pada penilitian ini akan menggunakan sinyal electromyography (EMG) sebagai input dan mengkategorikan sinyal tersebut dalam sebuah gerakan, yang diharapkan sistem dapat mengenali sinyal tersebut. Sebelum sinyal dikategorikan dilakukan memperhalus dengan metode exponetial moving average filter, tidak hanya memperhalus filter juga dapat meredam noise yang ada pada sinyal electromyography (EMG). Exponential Moving Average memiliki nilai bobot yaitu bernilai 0.1 dan 0.3 atau 10% dan 30% sebagai nilai rendah dan nilai tinggi seperti cutoff band-pass filter. Pengkategorian dilakukan dengan menggunakan klasifikasi decision tree, yang mengkategorikan pergerakkan lengan yaitu 0, 30, 60, 90,120, 150 dan 180 derajat. Hasil dari pengkategorian akan dikirimkan kepada output dari sistem penelitian. Output menggunakan liquid crystal display (LCD) dan light emitting diode (LED). LCD akan menampilkan hasil berupa tulisan derajat pergerakkan lengan yang dilakukan dan LED akan hidup sesuai derajat perhegerakan lengan.
English Abstract
Health is the most important thing in the process of human life. Healthy humans can do the work, but some unfortunate humans cannot do their jobs because of health disabilities. Biomedicine is currently developing rapidly to support health quickly and easily. One of the biomedical sciences for the detection of fiological signals that occur in the human body is electromyography (EMG) signals, without having to use the inverse method is to enter a detection device into the human body which has a high risk of damaging the health of the human body. By using electromyography (EMG) signals from contractions of human muscles, we can use these signals for various things. One of the uses is movement detection for electronic devices. In this research will use electromyography signal (EMG) as input and categorize the signal in a motion, which is expected by the system to recognize the signal. Before the signal is categorized as smoothing with the exponetial moving average filter method, not only smoothing the filter can also reduce noise in the electromyography (EMG) signal. Exponential Moving Averages have weight values that are worth 0.1 and 0.3 or 10% and 30% as low values and high values such as the band-pass filter cutoff. Categorization is done using the DECISION TREE classification, which categorizes arm movements, namely 0, 30, 60, 90,120, 150 and 180 degrees. The results of the categorization will be sent to the output of the research system. Output uses liquid crystal displays (LCD) and light emitting diodes (LEDs). The LCD will display the results in writing the degree of arm movement performed and the LED will live according to the degree of movement of the arm.
Other obstract
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| Item Type: | Thesis (Sarjana) |
|---|---|
| Identification Number: | SKR/FILKOM/2019/677/051907869 |
| Uncontrolled Keywords: | sinyal otot, gerakan lengan, electromyography, Exponential Moving Average, muscle signal, arm movement, electromyography, Exponential Moving Average |
| Subjects: | 000 Computer science, information and general works > 006 Special computer methods > 006.4 Computer pattern recognition > 006.45 Accoustical pattern recognition |
| Divisions: | Fakultas Ilmu Komputer > Teknik Informatika |
| Depositing User: | Budi Wahyono Wahyono |
| Date Deposited: | 24 Aug 2020 07:26 |
| Last Modified: | 09 Mar 2022 06:49 |
| URI: | http://repository.ub.ac.id/id/eprint/174135 |
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