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JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA)
Published by Universitas Muhammadiyah Sidoarjo
ISSN : 25408658     EISSN : 25408658     DOI : https://doi.org/10.21070/jeeeu
Core Subject : Engineering,
Electrical & Electronics Engineering
Aim: to facilitate scholar, researchers, and teachers for publishing the original articles of review articles. Scope: Electrical, Electronica, Telecomunication, Medical Electronica, Digital system, Control system.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 133 Documents
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Comparative Evaluation of Support Vector Machine and Random Forest Algorithms for Heart Abnormality Detection Based on ECG Signals: Evaluasi Komparatif Algoritma Support Vector Machines (SVM) Dan Random Forest Untuk Deteksi Kelainan Jantung Berdasarkan Sinyal EKG Rizky Putri Intan Hafsari Hafsari; Basitha Febrinda Hidayatulail; Rahman Arifuddin
JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Vol. 10 No. 2 (2026): Oktober
Publisher : Muhammadiyah University, Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jeeeu.v10i1.1749

Abstract

Sinyal Elektrokardiogram (EKG) memuat informasi penting untuk mengidentifikasikan kelainan irama jantung dan dapat digunakan untuk menganalisis aktivitas listrik sinyal jantung dan menemukan kelainan irama pada sinyal jantung. Penelitian ini bertujuan untuk membandingkan metode Support Vector Machines (SVM) dan Random Forest untuk mengklasifikasikan sinyal pada kondisi normal, apnea, dan arrhytmia. Penelitian ini terdiri dari 20 rekaman EKG apnea dan 20 rekaman EKG arrhytmia yang diproses melalui rekaman tahap preposesing sinyal, deteksi R-Peak, perhitungan interval RR, ekstrasi filter, pembagian data pelatihan model dan evaluasi performa. Data apnea diperoleh dari database Apnea-ECG Physionet dan data Arrhythmia diperoleh dari MIT-BIH Arrythmia database yang tersedia di Physionet. Dataset dibagi menjadi dua bagian 80% data latihan dan 20% data uji untuk evaluasi performa model. Accuracy, precision recall, F1-score dan confusion matrix digunakan untuk membagi kedua bagian ini untuk klasifikasi kondisi, pada kondisi ini apnea Random Forest menunjukkan keakuratan 97.24% dan keakuratan Support Vector Machine 91.41% sementara klasifikasi arrhytmia Random Forest menunjukkan accurasi 97,67% sedangkan Support Vector Machine memperoleh accurasi 95.96% dari hasil tersebut menunjukkan bahwa Random Forest lebih unggul dan menghasilkan performa klasifikasi yang lebih efektif dibandingkan Support Vector Machine pada kedua pengujian. Dengan demikian Random Forest dinilai lebih efektif untuk klasifikasi kondisi normal, apnea, bradycardia, tachycardia dan normal dalam mengklasifikasikan pola sinyal EKG.
Prototipe Automatic Filling Machine Using PLC CP2E Maulidia Ika Safitri; Rahman Arifuddin; Subairi Subairi
JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Vol. 10 No. 2 (2026): Oktober
Publisher : Muhammadiyah University, Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jeeeu.v10i2.1750

Abstract

The manual liquid filling process still has several disadvantages, such as inconsistencies in filling volumes, low work efficiency, and high likelihood of operator errors. This research aims to design and implement an Automatic Filling Machine based on PLC Omron CP2E by utilizing a flow sensor as a volume measurement feedback and a servo motor as an actuator to regulate the opening of the ball valve. The system works by calculating the amount of pulses produced by the flow sensor using the High Speed Counter feature on the PLC to determine the volume of liquid that has flowed. The Proportional control (P-Only) method is applied to adjust the position of the servo based on the error value between the target volume and the actual volume so that the valve closing process is carried out gradually. The test results showed that the system was able to achieve a target volume of 250 mL with an average actual volume of 250 mL, an average error of 0%, and an average charging time of 32.57 seconds. The optimal Kp value of 0.168 results in an actual volume that corresponds to the target so that it is chosen as the best control parameter. Based on these results, the system designed is able to automatically fill liquids with a good level of accuracy, stability, and consistency.
Model-Based Predictive Command of a 5-DoF Robotic Manipulator using Dynamics Mathematics: Pengendalian Prediktif Berbasis Model untuk Manipulator Robot 5-DoF Menggunakan Model Dinamika Matematis Nabeel Muhamed; Khaleel Ali khudhur; Raid W. Daoud
JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Vol. 10 No. 2 (2026): Oktober
Publisher : Muhammadiyah University, Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jeeeu.v10i1.1752

Abstract

This study presents the design and implementation of a predictive model control (MPC) framework tailored for articulated robot arms, enabling precise local motion control during complex manipulations and interdepartmental transport tasks. The research begins with a systematic formulation of the robot arm’s kinematics and dynamics, establishing mathematical models that serve as the foundation for advanced control strategies. A five-degree-of-freedom robotic arm, composed of motor and arm components, is employed as a case study to demonstrate the modeling process and validate the MPC design. Unlike conventional control approaches, the proposed framework emphasizes adaptability and robustness, offering improved trajectory tracking and disturbance rejection. Simulation results highlight the effectiveness of MPC in achieving smoother motion and reduced error margins, with quantitative indicators such as tracking accuracy and computational efficiency underscoring its performance advantages. While the outcomes confirm the feasibility of the approach, limitations related to hardware scalability and real-time implementation are acknowledged. Future work will focus on extending the framework to collaborative robotic systems, integrating sensor fusion for enhanced perception, and exploring deployment in industrial environments. Overall, this research contributes a structured methodology for predictive control in robotics, bridging theoretical modeling with practical application..

Page 14 of 14 | Total Record : 133

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