Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Novel Engineering Science and Technology
Syamlan, Muhammad Adib
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Environmental Science Mechanical Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Pengaruh Ciri Temporal, Spasial, dan Frekuensi pada Klasifikasi Motor Imagery Nurtsani, Afin Muhammad; Syamlan, Muhammad Adib; Setiawan, Agung Wahyu
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 3: Juni 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022935715

Abstract

Interaksi mesin-komputer merupakan suatu keniscayaan dan akan menjadi bagian yang tidak terpisahkan dari kehidupan dalam waktu dekat, terutama di bidang rekayasa rehabilitasi. Salah satu bidang yang berkembang adalah klasifikasi Motor Imagery (MI) berbasis sinyal EEG. Set data pada studi ini diambil dari BCI Competition IV - 2b. Prapemrosesan data dilakukan dengan menggunakan BPF Butterworth orde 5 dengan frekuensi cut-off sebesar 8 – 30 Hz.  Pada studi ini, dilakukan investigasi pengaruh ciri temporal; spasial; dan frekuensi serta kombinasi ciri temporal-spasial dan temporal-spasial-frekuensi. Ciri temporal diekstraksi dengan menggunakan ICA, ciri spasial dengan CSP, dan frekuensi dengan STFT. Terdapat empat pengklasifikasi yang digunakan, yaitu SVM; RF; k-NN; dan NB. Salah satu temuan pada studi ini adalah meskipun digunakan kombinasi ciri temporal-spasial maupun temporal-spasial-frekuensi, nilai akurasi yang diperoleh sama, yaitu sekitar 72%. Kinerja kedua kombinasi ciri ini masih kalah apabila dibandingkan dengan hanya menggunakan ciri independen temporal dengan nilai akurasi mencapai 73%. Selain itu, pengklasifikasi RF memberikan kinerja yang paling baik dibandingkan dengan SVM; k-NN; serta NB.  Abstract Human-computer interaction is a necessity and will be deployed in the near future, especially in rehabilitation engineering. One of the development is focused on the classification of Imagery Motor (MI) based on EEG signals. In this study, the dataset is taken from BCI Competition IV - 2b. The first step of the classification process is data preprocessing that is performed using BPF Butterworth 5th order with a cut-off frequency of 8 - 30 Hz. The aim of this study is to investigate the effect of independent feature such as temporal, spatial, frequency, and the combination of temporal-spatial and temporal-spatial-frequency features. Temporal feature is extracted using ICA, spatial feature using CSP, and frequency feature using STFT. In this study, four classifiers are used, i.e., SVM; RF; k-NN; and NB. One of the main findings in this study is that although the combination of temporal-spatial and temporal-spatial-frequency features is used, the accuracy value of 72% are obtained. The performance of these two combinations of features is still inferior when compared to independent temporal feature with an accuracy value of 73%. In addition, RF classifier provides the best performance compared to SVM; k-NN; and NB. Keywords: motor imagery, temporal, spatial, frequency, random forest
A Low-Cost Wearable System to Detect Fall and Non-Fall Activities for Elderly Individuals Syamlan, Muhammad Adib; Arifin, Ahmad; Pramudijanto , Josaphat; Arrofiqi, Fauzan; Syamlan, Muhammad Ariq; Noor, Raihan Aria Muhamad; Suhartono, Alif Syihabudin Fawwaz
Journal of Novel Engineering Science and Technology Vol. 5 No. 01 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i01.1534

Abstract

As the elderly population grows, the prevalence of age-related health conditions such as cardiovascular diseases, cognitive decline, and mobility impairment is also increased. Among these health conditions, falls are considered one of the greatest threats to elderly individuals. A low-cost wearable fall detection system is designed, with the purpose of monitoring and detecting their activities. Three master modules were constructed, with each consisting of an inertial sensor, a microcontroller, and a power supply circuit block. The data were collected using IMU MPU6050 and preprocessed using the MCU ESP32. Each master module is also supplied using a 3.7V 1S LiPo battery. 18 healthy subjects, consisting of 13 males and 5 females, agreed to volunteer for the experiments. They were instructed to do 8 different activities, including non-fall (stand still, sit-to-stand, walk, and sleep position) and fall events (forward fall, sideways fall, and backward fall). Overall, the system showed a good performance using the Multilayer Perceptron (MLP) algorithm with an accuracy of 95.3% across all activities. While misclassification happens between classes, our system is still able to distinguish between non-fall vs. fall events with 100% accuracy. Cost analysis was also conducted; the overall cost for the three master modules in our proposed system is $65.4. This is cheaper than commercial fall detection systems and other related research, and our proposed system can also be used continuously. The system will alert caregivers to the immediate attention of elderly individuals.
Co-Authors ahmad arifin Arrofiqi, Fauzan Noor, Raihan Aria Muhamad Nurtsani, Afin Muhammad Pramudijanto , Josaphat Setiawan, Agung Wahyu Suhartono, Alif Syihabudin Fawwaz Syamlan, Muhammad Ariq