Jurnal SINTA 2
Pengenalan Aktivitas Manusia Menggunakan Sensor Akselerometer dan Giroskop pada Smatphone dengan Metode K-Nearest Neighbor
Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 9 No. 1 (2022) · pp. 173–180
- ISSN
- 2528-6579, 2355-7699
- Published
- 2022-02-07
- Publisher
- Fakultas Ilmu Komputer, Universitas Brawijaya
- Language
- id
Abstract
Pengenalan aktivitas manusia atau Human Activity Recognition (HAR) merupakan salah satu topik yang populer karena besarnya peluang untuk diterapkan di kehidupan sehari-hari. Tujuan dari pengenalan ini adalah untuk mengenali, mendeteksi, dan mengklasifikasikan aktivitas yang dilakukan manusia. Pengenalan aktivitas manusia adalah salah satu teknologi penting untuk memantau dinamisme seseorang sehingga dapat bermanfaat di berbagai hal. Selain untuk menjaga kesehatan, pencegahan penyakit, dan membantu menentukan jenis olah raga, HAR dapat dimanfaatkan juga untuk diterapkan pada bidang keamanan dan pengembangan teknologi. Penelitian ini menggunakan smartphone sebagai teknologi utama dalam memperoleh data dengan memanfaatkan sensor akselerometer dan giroskop yang telah tertanam di dalamnya. Terdapat 8 macam aktivitas yang diteliti dengan kombinasi lama waktu eksperimen 5, 10, dan 15 detik serta posisi smartphone dipegang bebas maupun di dalam saku celana kanan. Data yang diperoleh terdiri dari 3 sumbu (x, y, dan z) pada setiap sensor yang digunakan. Data tersebut kemudian melalui proses pengolahan dan klasifikasi menggunakan algoritme k-Nearest Neighbor (k-NN). Hasil akurasi yang didapat dalam penelitian ini mencapai 79,56%. Hasil yang diperoleh melalui penelitian ini menunjukkan bahwa perbedaan peletakan smartphone mempengaruhi hasil pengenalan aktivitas manusia secara stabil. Selain itu, perbedaan jumlah data akibat perbedaan lamanya waktu eksperimen dapat mengakibatkan perbedaan lamanya waktu komputasi. Penelitian ini menjadi penting karena hasilnya dapat menjadi batu loncatan bagi penelitian selanjutnya. Beberapa peluang pengembangan juga dilampirkan pada bagian akhir. AbstractHuman activity recognition (HAR) is one of the most popular topics because of the large opportunities for its application in life. The purpose of HAR is to recognize, detect and classify human activities. Human activity recognition is one of the important technologies for monitoring a person's dynamism so that it can be utilized in various ways. Apart from maintaining health, preventing disease, and helping determine the type of exercise, HAR can also be used to be applied in the field of security and technological developments. This study uses smartphones as the main technology in obtaining data by utilizing the built-in accelerometer and gyroscope sensors. There are 8 types of activities studied with a combination of 5, 10, and 15 seconds of experimental time and the position of the smartphone is carried freely or in the right trouser pocket. The data obtained consists of 3 axes (x, y, and z) on each sensor used. The data then processed and classified using the k-Nearest Neighbor (k-NN) algorithm. The accuracy results obtained in this study reaches 79.56%. The results obtained through this study indicate that differences in smartphone placement affect the results of human activity recognition stably. In addition, differences in the amount of data due to differences in the length of the experiment period can result in differences in the length of computation time. This research is important because the results can be used as material for further research assistance. Some development opportunities are also attached at the end.
Collaboration network of the authors
40 authors · 110 co-authorship links · showing the 40 most connected
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
- University of Brawijaya
- Affiliation unknown
- Different organisations
- Same organisation
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- Thicker line = more joint articles · bigger circle = more articles
Show as table
| Co-author | Organisation | Joint articles |
|---|---|---|
| Fitra Abdurrachman Bachtiar | University of Brawijaya | 2 |
| Achmad Ridok | University of Brawijaya | 2 |
| Yuita Arum Sari | University of Brawijaya | 1 |
| Fadhil Yusuf Rahadika | University of Brawijaya | 1 |
| Agus Wahyu Widodo | University of Brawijaya | 1 |
| Bayu Rahayudi | University of Brawijaya | 1 |
| M. Sofyan Irwanto | University of Brawijaya | 1 |
| Candra Dewi | University of Brawijaya | 1 |
| Nurfatima Selle | University of Brawijaya | 1 |
| Alvin Tarisa Akbar | University of Brawijaya | 1 |
| Michael Stephen Lui | University of Brawijaya | 1 |
| Daniel Geoffrey Manurung | University of Brawijaya | 1 |
| Mohammad Ryan Pinasthika | University of Brawijaya | 1 |
| Muhammad Azka Obila Vasya | University of Brawijaya | 1 |
| Rania Aprilia Dwi Setya Putri | University of Brawijaya | 1 |
| Agustinus Parasian Tampubolon | University of Brawijaya | 1 |
| Rakan Fadhil Prayata | University of Brawijaya | 1 |
| Septia Khoirin Nisa | University of Brawijaya | 1 |
| Assyfa Rasida Hanum | University of Brawijaya | 1 |
| Ivykaeyla Adriana Zetha | University of Brawijaya | 1 |
| Salwa Cahyani Putri | University of Brawijaya | 1 |
| Rafifah Ayud Wulandari | University of Brawijaya | 1 |
| Sherla Puspa Andina | University of Brawijaya | 1 |
| Julia Nur Fajrina | University of Brawijaya | 1 |
| Mohammad Alfiano Rizky Mahardika | University of Brawijaya | 1 |
| Yehezkiel Stephanus Austin | University of Brawijaya | 1 |
| Haikal Irfano | University of Brawijaya | 1 |
| Juan Young Christopher | University of Brawijaya | 1 |
| Lintang Cahyaning Sukma | University of Brawijaya | 1 |
| Octo Perdana Putra | University of Brawijaya | 1 |
| Riyadh Ilham Ardhanto | University of Brawijaya | 1 |
| Sulthan Abiyyu Hakim | University of Brawijaya | 1 |
| Muhammad Ubaidillah | University of Brawijaya | 1 |
| Aditya Ramadhan | University of Brawijaya | 1 |
| Rahmada Zulvia Azzahra Hawari | University of Brawijaya | 1 |
| Audhinata Bebytama Rizky | University of Brawijaya | 1 |
| Lailil Muflikhah | University of Brawijaya | 1 |
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.