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All Journal Jurnal Informatika dan Teknik Elektro Terapan e-Jurnal Arus Elektro Indonesia RELE: Rekayasa Elektrikal dan Energi Jurnal Teknik Elektro Elsains : Jurnal Elektro Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknik Elektro dan Komputasi (ELKOM) Best : Journal of Applied Electrical, Science and Technology BEES: Bulletin of Electrical and Electronics Engineering Journal Geuthee of Engineering and Energy Jurnal Penelitian Multidisiplin Bangsa Emitor: Jurnal Teknik Elektro
Prenata, Giovanni Dimas
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mechanical Engineering Physics Public Health Social Sciences

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Evaluation of ANN Training Methods: A Comparative Study of Back Propagation, Genetic Algorithm, and Particle Swarm Optimization for Predicting Electrical Energy Consumption Prenata, Giovanni Dimas
Emitor: Jurnal Teknik Elektro Vol 25, No 3: November 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/emitor.v25i3.12719

Abstract

This study compares the performance of ANN with three training methods: Backpropagation (BP), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) in a simple classification case. The results show that ANN GA has the smallest average error (0.0308), followed by ANN BP (0.0569), while ANN PSO is much larger (0.7559). Thus, ANN GA proved to be the most stable and accurate, ANN BP still performed quite well, while ANN PSO had the weakest performance.
Sistem Monitoring Alat Perkakas Pada Kendaraan Truk Towing Menggunakan ESP32 Ridho’i, Ahmad; Prenata, Giovanni Dimas; Prasetyo, Arif Bayu
Jurnal Penelitian Multidisiplin Bangsa Vol. 2 No. 10 (2026): Maret
Publisher : Amirul Bangun Bangsa Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpnmb.v2i10.793

Abstract

Kamera adalah perangkat umum yang telah menjadi bagian tak terpisahkan dalam pengambilan gambar atau perekaman peristiwa yang nantinya dapat dilihat oleh individu lain. Selain itu, kamera juga memiliki kemampuan untuk menggantikan peran mata manusia dalam proses pengamatan visual. Mata manusia mampu mengidentifikasi dan memproses informasi visual, termasuk warna, bentuk, dan berbagai unsur lainnya dalam lingkungan sekitarnya. Penelitian ini bertujuan untuk merancang sistem deteksi objek dengan memanfaatkan kamera sebagai komponen utamanya. Mikrokontroler ESP32 yang terintegrasi dengan ESP32-CAM digunakan untuk pengawasan visual, dan sistem ini didukung oleh motor servo SG90 yang dapat dikendalikan oleh pengguna. Melalui integrasi Web Server, pengguna dapat dengan mudah memantau kamera secara real-time melalui perangkat seluler mereka. Keuntungan tambahan dari integrasi ini adalah meningkatkan keterlibatan dan kenyamanan pengguna, memungkinkan mereka untuk mengakses informasi visual dengan cepat dan efisien. Hasil pengujian menunjukkan bahwa sistem berhasil menampilkan streaming video secara real-time pada antarmuka Web Server dengan tingkat latensi rata-rata sebesar 5 milidetik. Motor servo SG90 terbukti mampu merespons perintah kontrol dari perangkat seluler dengan akurasi pergerakan sudut sebesar 90O, memperluas cakupan area pemantauan. Selain itu, fungsi deteksi objek pada sistem ini mampu beroperasi dengan tingkat akurasi mencapai 99,8 % pada kondisi pencahayaan terang. Penerapan sistem deteksi objek melalui kamera, ESP32, dan motor servo SG90 juga memiliki potensi dalam berbagai skenario, seperti pengawasan industri, pemantauan lalu lintas, atau keamanan rumah tangga. Dengan adopsi teknologi ini, pengembangan solusi inovatif di berbagai bidang dapat lebih dimungkinkan.
Comparative Evaluation of LSTM and Metaheuristic-Optimized Neural Networks for ECG Prediction under Limited Data Conditions Prenata, Giovanni Dimas; Ridho’i, Ahmad; Arshad, Mohd Rizal
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1524

Abstract

This study presents a comparative evaluation of Deep Feedforward Neural Network (DFFNN) models optimized using single-stage metaheuristic approaches, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), as well as a multi-stage hybrid optimization strategy (GA+GWO) for ECG-based emotion classification. The experimental dataset consists of ECG recordings collected from three elderly participants using a Sparkfun AD8232 sensor under controlled emotional stimuli, representing a limited-subject and small-data scenario. Feature extraction is conducted using Heart Rate Variability (HRV) parameters derived from both time domain (Mean RR, SDNN, RMSSD, Mean HR, and STD HR) and frequency domain (LF, HF, and LF/HF ratio). Experimental results from six repeated trials demonstrate that the multi-stage DFFNN+GA+GWO model achieves the best optimization performance, yielding the lowest Mean Squared Error (MSE) of 0.01599 and a consistent training accuracy of up to 85.71%. Compared with single-stage optimization methods, the hybrid approach exhibits improved convergence behavior and reduced performance variance, indicating enhanced optimization stability. However, test accuracy remains relatively limited (33.33%–50.00%), reflecting constrained generalization capability due to the small dataset and the absence of subject-wise or external validation. Further statistical analysis using confidence intervals and nonparametric testing confirms that the observed performance improvements are primarily associated with optimization stability rather than statistically significant gains in predictive generalization. Therefore, this study emphasizes the role of metaheuristic optimization in stabilizing neural network training under limited data conditions. The findings should be interpreted as a pilot feasibility study, and future work is required to validate the proposed approach using larger, more diverse datasets and more rigorous validation strategies.
Application of fourier transform for early detection of bearing failures in electric motors Prenata, Giovanni Dimas; Syariffudin, Muhammad Alif Sofi; Widagdo, Reza Sarwo
Journal Geuthee of Engineering and Energy Vol 5, No 1 (2026): Journal Geuthee of Engineering and Energy
Publisher : Geuthèë Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52626/joge.v5i1.69

Abstract

This study presents an analysis of bearing fault conditions in electric motors through stator current measurements and their transformation into the frequency domain. Measurements were conducted under two main bearing conditions: normal and damaged, each tested with three load variations (no load, generator load, and generator load with one lamp). The time-domain current waveforms showed minimal visual distinction between normal and damaged bearing conditions, making classification difficult. Therefore, the current data were transformed into the frequency domain using the Discrete Fourier Transform (DFT). The frequency domain analysis revealed that in normal bearing conditions, the frequency magnitude distribution was relatively stable and symmetrical, with low fluctuation in the frequency index range k = 0 to k = 10. In contrast, damaged bearing conditions exhibited larger and irregular fluctuations in frequency magnitude across different load levels, indicating a distinct signature of bearing failure. Consequently, frequency domain analysis proves to be an effective approach for detecting bearing faults based on the spectral characteristics of motor current signals.
Co-Authors Aris Heri Andriawan, Aris Heri Arshad, Mohd Rizal Baihaqi, Ahmad Basyarach, Niken Adriaty Fidhini, Fachrul Fauzan Noufanda, Yudo Farrel Prasetyo, Arif Bayu Prayoga, Ageng Puji Slamet, Puji Rafli, Syah Achmad Amar Reza Sarwo Widagdo ridhoi, achmad Ridhoi, Ahmad Ridho’i, Ahmad Setiawan, Dani Agus Setiyawan, Ryan Agus Subekti Yuliananda, Subekti Subyakto, Doris Swarga, Lutfi Agung Syariffudin, Muhammad Alif Sofi Wardah, Izzah Aula