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All Journal CIRCUIT: Jurnal Ilmiah Pendidikan Teknik Elektro Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Surya Teknika Jurnal Pengabdian Masyarakat (ABDIRA) Andalasian International Journal of Applied Science, Engineering, and Technology Innovative: Journal Of Social Science Research
Alfi Zuhriya Khoirunnisaa
Universitas Muhammadiyah Gresik, Indonesia
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Nursing Public Health Social Sciences Other

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Multi-scale Entropy and Multiclass Fisher’s Linear Discriminant for Emotion Recognition Based on Multimodal Signal Lutfi Hakim; Sepyan Purnama Kristanto; Alfi Zuhriya Khoirunnisaa; Adhi Dharma Wibawa
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 5, No. 1, February 2020
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (685.165 KB) | DOI: 10.22219/kinetik.v5i1.896

Abstract

Emotion recognition using physiological signals has been a special topic frequently discussed by researchers and practitioners in the past decade. However, the use of SpO2 and Pulse rate signals for emotion recognitionisvery limited and the results still showed low accuracy. It is due to the low complexity of SpO2 and Pulse rate signals characteristics. Therefore, this study proposes a Multiscale Entropy and Multiclass Fisher’s Linear Discriminant Analysis for feature extraction and dimensional reduction of these physiological signals for improving emotion recognition accuracy in elders.  In this study, the dimensional reduction process was grouped into three experimental schemes, namely a dimensional reduction using only SpO2 signals, pulse rate signals, and multimodal signals (a combination feature vectors of SpO2 and Pulse rate signals). The three schemes were then classified into three emotion classes (happy, sad, and angry emotions) using Support Vector Machine and Linear Discriminant Analysis Methods. The results showed that Support Vector Machine with the third scheme achieved optimal performance with an accuracy score of 95.24%. This result showed a significant increase of more than 22%from the previous works.
Klasifikasi Penggunaan Listrik Rumah Tangga Menggunakan Metode Algoritma C4.5, Random Forest, Dan SVM pada PT. PLN ULP Benjeng Saputra, Bagas Diki; Khoirunnisaa, Alfi Zuhriya
Innovative: Journal Of Social Science Research Vol. 5 No. 4 (2025): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v5i4.21025

Abstract

Penggunaan listrik rumah tangga terus meningkat seiring dengan bertambahnya kebutuhan energi akibat perkembangan teknologi. Untuk mendukung efisiensi energi dan pengelolaan. daya yang lebih baik, dibutuhkan sistem klasifikasi yang mampu memprediksi tingkat konsumsi. listrik. Penelitian ini bertujuan untuk mengklasifikasikan penggunaan listrik rumah tangga menggunakan metode algoritma C4.5, SVM, dan random forest berbasis decision tree yang diimplementasikan melalui perangkat lunak Python. Data yang digunakan diperoleh dari PT. PLN (Persero) ULP Benjeng dan mencakup atribut seperti daya, tarif, pemakaian per KWh, serta komponen biaya lainnya. Proses meliputi tahap prapemrosesan data, pembentukan model klasifikasi, dan evaluasi akurasi dengan confusion matrix. Hasil penelitian diharapkan dapat membantu masyarakat dan penyedia layanan listrik dalam mengelola konsumsi energi secara efisien dan tepat guna.
Pelatihan Pemrograman Arduino menggunakan Wokwi Simulator di SMK Mambaul Ulum Kebomas-Gresik Khoirunnisaa, Alfi Zuhriya; Hardiyanti, Mega Rahayu
Jurnal Pengabdian Masyarakat (ABDIRA) Vol 6, No 1 (2026): Abdira, Januari
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/abdira.v6i1.1262

Abstract

The Arduino UNO microcontroller is a microcontroller widely used by vocational high school students to design Internet of Things-based projects or intelligent systems in the field of industrial automation. However, some obstacles in making projects using Arduino hardware include unstable data communication, signal interference/noise, wiring errors and component compatibility. Therefore, it is necessary to introduce the Wokwi Simulator to minimize the occurrence of these obstacles. The Wokwi simulator is a simulation for creating prototypes using IoT-based Arduino virtually. The use of this simulator can help vocational high school students in designing industrial automation projects virtually, without purchasing electronic components first. The method used is a theoretical explanation of the Wokwi simulator and direct practice in making IoT-based prototypes using the Wokwi simulator in groups. The results of this community service program include increasing the knowledge and skills of vocational high school students in using the Wokwi simulator both in theory and practice. This was obtained from the post-test results of vocational high school students who got an average score of 94.
Design and Development of a Non-Invasive Blood Glucose Level Measurement Device Based on Arduino Uno and Near-Infrared (NIR) Sensor Baiturrohmah, Siti; Alfi Zuhriya Khoirunnisaa
Circuit: Jurnal Ilmiah Pendidikan Teknik Elektro Vol. 10 No. 1 (2026)
Publisher : PTE FTK UIN Ar-Raniry

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/5yqqya14

Abstract

Diabetes Mellitus is one of the leading causes of death in the world. Therefore, regular monitoring of blood sugar levels is very important to prevent complications. However, monitoring blood sugar using conventional-invasive methods can cause discomfort and risk causing infection. This study aims to design a non-invasive blood sugar level measuring device based on Arduino Uno R3 using a BPW34 Near-Infrared (NIR) sensor reinforced with an LM358 operational amplifier. The measurement results are displayed on a 16x2 LCD and sent via Telegram Bot in real-time. This study focused on Type 2 Diabetes Mellitus and was tested on 10 participants aged 18-45 years with some patients in fasting and some not fasting conditions. The test results showed good accuracy with a Mean Absolute Error of 2.48%, minimal systematic bias (-0.08%), and a very strong correlation coefficient (r = 0.996436) against invasive methods. All measurement data meets the tolerance criteria of ISO 15197:2013 with an error range of -4.55% to +3.16%, indicating that this tool has the potential to be a more convenient and safer alternative for initial blood sugar level screening
EEG-Based Cybersickness Classification Using Hjorth Parameters and Random Forest During 3D Gaming Exposure Alfi Zuhriya Khoirunnisaa; Misbah Misbah
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 6 No. 2 (2026): July 2026
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v6i2.351

Abstract

This study presents an efficient EEG-based framework for cybersickness classification utilizing Hjorth parameter features under a 3D immersive video game stimulus (Mirror Edge). Multimodal data acquisition was performed using a 14-channel Emotiv EEG system for objective measurement and the Simulator Sickness Questionnaire (SSQ) for subjective validation. The EEG signals were subjected to comprehensive preprocessing procedures, including band pass filtering and Independent Component Analysis (ICA) to eliminate artifacts. Then, using Discrete Wavelet Transform (DWT) to isolate theta,alpha, and beta bands. Hjorth parameters: activity, mobility, and complexity were subsequently extracted to capture the temporal dynamics of neural activity with low computational overhead. To mitigate feature redundancy and dimensionality, Correlation Feature Selection reduced the feature space from 126 to 9 salient features. Classification performance was evaluated using Random Forest, Support Vector Machine, and K-Nearest Neighbor. Experimental results indicate a consistent increase in SSQ scores across participants, with disorientation emerging as the predominant symptom. Random Forest achieved superior performance with an accuracy of 82%, outperforming K-NN (72.72%) and SVM (59.09%). Notably, feature reduction preserved Random Forest performance while enhancing alternative classifiers. These findings highlight the robustness and computational efficiency of the proposed approach, demonstrating its potential for real-time EEG-based cybersickness detection.
Metode Charging Constant Current Constant Voltage Baterai Li-Po Dany Mufty, Wasith; Khoirunnisaa, Alfi Zuhriya
SURYA TEKNIKA Vol 13 No 1 (2026): JURNAL SURYA TEKNIKA
Publisher : Fakultas Teknik UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jst.v13i1.11544

Abstract

The use of renewable energy has experienced significant developments, especially solar panel technology. Due to the unstable nature of solar energy sources, an energy storage system is needed to maintain the continuity of power supply in the form of batteries. One type of battery is the Lithium Polymer (Li-Po) battery, which is often used because it has a high energy density and good work efficiency. However, over time the battery capacity will decrease so it needs to be charged. When charging, there is a risk of overcharging, so the Constant Current Constant Voltage (CC-CV) charging method is needed. This method is usually combined with a Proportional-Integral (PI) controller to regulate the Buck converter output through signal adjustments so that the current and voltage match the desired values. The use of the combination of the CC-CV method and PI control allows real-time regulation of charging parameters based on the battery's State of Charge (SoC) conditions, thereby extending battery life and improving the safety of solar panel-based energy storage systems.
Co-Authors Adhi Dharma Wibawa Baiturrohmah, Siti Dany Mufty, Wasith Hardiyanti, Mega Rahayu Lutfi Hakim Misbah Misbah Saputra, Bagas Diki Sepyan Purnama Kristanto