Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Techno.Com: Jurnal Teknologi Informasi JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Journal of Electrical and Intelligent Systems (JEIS)
Rika Rokhana
Politeknik Elektronika Negeri Surabaya
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Fuzzy-PID controller for an energy efficient personal vehicle: Two-wheel electric skateboard Bambang Sumantri; Eko Henfri Binugroho; Ilham Mandala Putra; Rika Rokhana
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 6: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (318.79 KB) | DOI: 10.11591/ijece.v9i6.pp5312-5320

Abstract

The two-wheeled electric skateboard (TWS) is designed for a personal vehicle. A Fuzzy-PID control strategy is designed and implemented for controlling its motion. Basically, motions control of the TWS is performed by balancing the pitch position of the TWS. Performance of the designed controller is demonstrated experimentally. The Fuzzy algorithm updates the PID gains and therefore it can handle the changing of the TWS load. Contribution of Fuzzy-PID in reducing the electric energy consumption, which is an important issue in electrical system, is also evaluated. The Fuzzy-PID successes to reduce the electric energy consumption of the TWS compared to the conventional PID.
Fusi Algoritma K-Means dan CNN untuk Klasifikasi Emosi pada Anak Fildzah Aure Gehara Zhafirah; Rika Rokhana; Riyanto Sigit; Bima Sena Bayu Dewantara
Techno.Com Vol 22, No 3 (2023): Agustus 2023
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v22i3.8667

Abstract

Emosi adalah perasaan yang diarahkan pada seseorang ataupun sesuatu yang bisa menyebabkan sesorang bertindak atau mengekspresikan diri dan dapat dipicu secara internal ataupun eksternal. Ekspresi wajah merupakan salah satu cara termudah untuk mengetahui emosi seseorang, namun terkadang seseorang dapat mengontrol dan memanipulasi ekspresi wajah mereka sehingga tidak sesuai dengan apa yang dialami. Oleh karena itu, penelitian ini mengembangkan sistem yang dapat mengidentifikasi emosi anak tidak hanya berdasarkan wajah tetapi juga berdasarkan perubahan kondisi tubuhnya. Penelitian ini menggunakan metode klasifikasi Convolutional Neural Network (CNN) dan juga metode clusterisasi K-Means. Penggunaan 2 metode pada penelitian ini berfungsi untuk memperkuat akurasi sistem. Metode K-Means digunakan untuk mengidentifikasi emosi berdasarkan detak jantung dan konduktivitas kulit sedangkan Metode CNN digunakan untuk mengidentifikasi emosi berdasarkan ekspresi wajah. Hasil yang diperoleh dari kedua metode tersebut akan diproses menggunakan metode fusi yang aturannya disesuaikan berdasarkan hasil pengamatan dan pengukuran, sehingga dapat diprediksi emosi pada anak berdasarkan parameter detak jantung, ekspresi wajah, dan konduktivitas kulit. Anak dengan umur 6 hingga 12 tahun digunakan sebagai subjek pada penelitian ini. Dari penelitian ini berhasil didapatkan hasil prediksi emosi anak dengan akurasi keberhasilan sebesar 80%.
Feature Extraction of Multichannel EMG Signals for Shoulder Joint Movement Patterns Paulus Susetyo Wardana; Lince Markis; Rika Rokhana
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 7 No 2 (2026)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.7.2.614

Abstract

Electromyography (EMG) signals provide information about muscle activity and can support rehabilitation and prosthetic control systems. This study aims to extract and analyze features of multichannel EMG signals recorded from seven shoulder joint movement patterns. EMG data were acquired using surface electrodes placed on eight dominant muscles associated with shoulder joint motion, namely Deltoid1, Deltoid2, Infraspinatus, Supraspinatus, Teres Major, Latissimus Dorsi, Pectoralis1, and Pectoralis2. The recorded movements included resting, shoulder flexion, shoulder extension, shoulder abduction, shoulder adduction, external rotation, and internal rotation. The proposed processing procedure consisted of signal acquisition, rectification, transformation into the frequency domain using Discrete Fourier Transform, and feature extraction using Linear Envelope, Modified Mean Frequency (MMNF), and Modified Median Frequency (MMDF). The results show that Linear Envelope can describe temporal energy changes in each movement pattern, while MMNF and MMDF can identify groups of similar signal patterns and distinguish several movements through specific muscle channels. Resting movement had very small amplitude changes, while active shoulder movements produced different dominant energy patterns across subjects. MMNF and MMDF produced two main similarity groups, although the distinguishing muscles differed among subjects. These findings indicate that multichannel EMG feature extraction is useful as an initial basis for shoulder movement pattern analysis; however, further development is required to improve online acquisition, automatic gain adjustment, and classification robustness.
Automatic Control of Oxygen Flow for Hypoxemia Therapy Based on Fuzzy Method Rika Rokhana; Santi Anggraini; Retno Sukmaningrum; Hary Oktavianto; Paulus Susetyo Wardana; Agrippina Waya Rahmaning; Moch. Rochmad; Kemalasari; Hendhi Hermawan Efendi; Zainal Arief
Journal of Electrical and Intelligent Systems Vol. 1 No. 1 (2026): April
Publisher : Politeknik Elektronika Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/jeis.v1i1.2

Abstract

Hypoxemia is a serious condition that requires oxygen transfusion. Indiscriminate oxygen administration is a poor strategy that can increase organ damage and even death. This paper describes a system for automatically controlling airflow of an oxygen tubes to a patient based on blood oxygen saturation and respiratory rate measurements. The MAX30102 sensor is used to measure oxygen saturation levels, and the MAX9814 module is used to determine respiratory rate. Both sensor outputs are processed by an STM32F411 microcontroller, and then sent wirelessly to an Arduino Uno microcontroller, which implements the fuzzy logic controller to control oxygen flow. The fuzzy output is used to activate a motor servo that controls the oxygen tube valve opening. The valve opening width (in degrees) is divided into 5 categories. Communication between the microcontroller and the valve actuator uses a 433MHz wireless RF module. The device test results revealed an MAE of 0.40% for oxygen saturation measurements compared to standard hospital measuring instruments and an MAE of 0.47% for respiratory rate measurements compared to manual measurements. Overall system testing produced a valve opening with an MAE of 0.56% compared to simulation results using MATLAB.
Co-Authors Agrippina Waya Rahmaning Bambang Sumantri Bima Sena Bayu Dewantara Eko Henfri Binugroho Fildzah Aure Gehara Zhafirah Hary Oktavianto Hendhi Hermawan Efendi Ilham Mandala Putra Kemalasari Lince Markis Moch. Rochmad PAULUS SUSETYO WARDANA Retno Sukmaningrum Riyanto Sigit Santi Anggraini Zainal Arief